Modelling

Healthcare systems in low-resource settings need effective methods for managing their scant resources, especially people and equipment. Digital technologies may provide means for circumventing the constraints hindering low-income economies from improving their healthcare services. Although analytical and simulation techniques, such as queuing theory and discrete event simulation, have already been successfully applied in addressing various optimisation problems across different operational contexts, the literature reveals that their application in optimisation of healthcare maintenance systems remains relatively unexplored. This study considers the problem of maintenance workflow optimisation with respect to labour, equipment availability and cost. The study aims to provide objective means for forecasting resource demand, given a set of task requests with varying priorities and queue characteristics that flow from multiple queues, and in parallel, into the same maintenance process for resolution. The paper presents how discrete event simulation is adopted in combination with simulated annealing to develop a decision-support tool that helps healthcare asset managers leverage operational performance data to project future asset-performance trends objectively, and thereby determine appropriate interventions for optimal performance. The study demonstrates that healthcare facilities can achieve efficiency in a cost-effective manner through tool-generated maintenance strategies, and that any future changes can be expeditiously re-evaluated and addressed. Full article

The functionalization of nanoporous ceramics for applications in healthcare and defence necessitates the study of the effects of geometric structures on their fundamental mechanical properties. However, there is a lack of research on their stiffness and fracture strength along diverse directions under multi-axial loading conditions, particularly with the existence of typical voids in the models. In this study, accurate atomic models and corresponding properties were meticulously selected and validated for further investigation. Comparisons were made between typical material geometric and elastic properties with measured results to ensure the reliability of the selected models. The mechanical behavior of nanoporous alumina under multiaxial stretching was explored through molecular dynamics simulations. The results indicated that the stiffness of nanoporous alumina ceramics under uniaxial tension was greater, while the fracture strength was lower compared to that under multiaxial loading. The fracture of nanoporous ceramics under multi-axial stretching, was mainly dominated by void and crack extension, atomic bond fracture, and cracking with different orientations. Furthermore, the effects of increasing strain rates on the void volume fraction were found to be similar across different initial radii. It was also found that the increasing tension loading rates had greater effects on decreasing the fracture strain. These findings provide additional insight into the fracture mechanisms of nanoporous ceramics under complex loading states, which can also contribute to the development of higher-scale models in the future. Full article

The structural health monitoring (SHM) technique is a highly competent operative process dedicated to improving the resilience of an infrastructure by evaluating its system state. SHM is performed to identify any modification in the dynamic properties of an infrastructure by evaluating the acceleration, natural frequencies, and damping ratios. Apart from the vibrational measurements, SHM is employed to assess the displacement. Consequently, sensors are mounted on the investigated framework aiming to collect frequent readings at regularly spaced time intervals during and after being induced. In this study, a LabVIEW program was developed for vibrational monitoring and system evaluation. In a case study reported herein, it calculates the natural frequencies as well as the damping and displacement parameters of a cantilever steel beam after being subjected to excitation at its free end. For that purpose, a Bridge Diagnostic Inc. (BDI) accelerometer and a displacement transducer were parallelly mounted on the free end of the beam. The developed program was capable of detecting the eigenfrequencies, the damping properties, and the displacements from the acceleration data. The evaluated parameters were estimated with the ARTeMIS modal analysis software for comparison purposes. The reported response confirmed that the proposed system strongly conducted the desired performance as it successfully identified the system state and modal parameters. Full article

The automatic character recognition of historic documents gained more attention from scholars recently, due to the big improvements in computer vision, image processing, and digitization. While Neural Networks, the current state-of-the-art models used for image recognition, are very performant, they typically suffer from using large amounts of training data. In our study we manually built our own relatively small dataset of 404 characters by cropping letter images from a popular historic manuscript, the Electronic Beowulf. To compensate for the small dataset we use ImageDataGenerator, a Python library was used to augment our Beowulf manuscript’s dataset. The training dataset was augmented once, twice, and thrice, which we call resampling 1, resampling 2, and resampling 3, respectively. To classify the manuscript’s character images efficiently, we developed a customized Convolutional Neural Network (CNN) model. We conducted a comparative analysis of the results achieved by our proposed model with other machine learning (ML) models such as support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and XGBoost. We used pretrained models such as VGG16, MobileNet, and ResNet50 to extract features from character images. We then trained and tested the above ML models and recorded the results. Moreover, we validated our proposed CNN model against the well-established MNIST dataset. Our proposed CNN model achieves very good recognition accuracies of 88.67%, 90.91%, and 98.86% in the cases of resampling 1, resampling 2, and resampling 3, respectively, for the Beowulf manuscript’s data. Additionally, our CNN model achieves the benchmark recognition accuracy of 99.03% for the MNIST dataset. Full article

Socio-cyber-physical systems (SCPSs) have emerged as networked heterogeneous systems that incorporate social components (e.g., business processes and social networks) along with physical (e.g., Internet-of-Things devices) and software components. Model-driven techniques for building SCPSs need actor and goal models to capture social concerns, whereas system issues are often addressed with the Systems Modeling Language (SysML). Comprehensive traceability between these types of models is essential to support consistency and completeness checks, change management, and impact analysis. However, traceability management between these complementary views is not well supported across SysML tools, particularly when models evolve because SysML does not provide sophisticated out-of-the-box goal modeling capabilities. In our previous work, we proposed a model-based framework, called CGS4Adaptation, that supports basic traceability by importing goal and SysML models into a leading third-party requirement-management system, namely IBM Rational DOORS. In this paper, we present the framework’s traceability management method and its use for automated consistency and completeness checks. Traceability management also includes implicit link detection, thereby, improving the quality of traceability links while better aligning designs with requirements. The method is evaluated using an adaptive SCPS case study involving an IoT-based smart home. The results suggest that the tool-supported method is effective and useful in supporting the traceability management process involving complex goal and SysML models in one environment while saving development time and effort. Full article

Copulas are well-known tools for describing the relationship between two or more quantitative variables. They have recently received a lot of attention, owing to the variable dependence complexity that appears in heterogeneous modern problems. In this paper, we offer five new copulas based on a common original ratio form. All of them are defined with a single tuning parameter, and all reduce to the independence copula when this parameter is equal to zero. Wide admissible domains for this parameter are established, and the mathematical developments primarily rely on non-trivial limits, two-dimensional differentiations, suitable factorizations, and mathematical inequalities. The corresponding functions and characteristics of the proposed copulas are looked at in some important details. In particular, as common features, it is shown that they are diagonally symmetric, but not Archimedean, not radially symmetric, and without tail dependence. The theory is illustrated with numerical tables and graphics. A final part discusses the multi-dimensional variation of our original ratio form. The contributions are primarily theoretical, but they provide the framework for cutting-edge dependence models that have potential applications across a wide range of fields. Some established two-dimensional inequalities may be of interest beyond the purposes of this paper. Full article

We have developed a three-dimensional hybrid finite-discrete element model to investigate the mode I tensile opening failure of alumina ceramic. This model implicitly considers the flaw system in the material and explicitly shows the macroscopic failure patterns. A single main crack perpendicular to the loading direction is observed during the tensile loading simulation. Some fragments appear near the crack surfaces due to crack branching. The tensile strength obtained by our model is consistent with the experimental results from the literature. Once validated with the literature, the influences of the distribution of the flaw system on the tensile strength and elastic modulus are explored. The simulation results show that the material with more uniform flaw sizes and fewer big flaws has stronger tensile strength and higher elastic modulus. Full article

This paper expands upon the state of the art in nonlinear modeling of automotive air conditioning systems. Prior models considered only the effects of the refrigerant compressor and the condenser fan. There are two new aspects included here. First, we create a mathematical model for front-end underhood airflow, considering vehicle speed, condenser fan rotational speed, and active grille shutter position. In addition, we present a new model for the power consumption of the vehicle associated with aerodynamic drag caused by underhood flow, as well as a fan power model which accounts not only for changes in rotational speed but also changes in flow rate. The models developed in this paper are coded in MATLAB/Simulink and assessed for various vehicle driving conditions against a higher-fidelity vehicle energy management model, showing good agreement. By including the active grille shutters as a controllable actuator and the impact of underhood flow on vehicle drag and fan power consumption, control schemes can be developed to holistically target reduced energy consumption for the air conditioning system and, thus, improve the overall vehicle energy efficiency. Full article

Fouling, caused by the adhesion of fine materials to the blades of the compressor’s last stages, changes the airfoil’s shape and function and the inlet flow angle on the blades. As the fouling increases, the range of influence increases, and the mass flow rate and overall engine efficiency reduce. Therefore, the compressor is choked at lower speeds. This study aims to simulate compressor performance during off-design conditions due to fouling and to present an approach for modeling faults in diagnostic and health monitoring systems. A computational fluid dynamics analysis is carried out to evaluate the proposed method on General Electric’s T700-GE turboshaft engine, and the performance is evaluated at different flight conditions. The results show promising outcomes with an average accuracy of 88% that would help future turboshaft health monitoring systems. Full article

Machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, such as diabetes mellitus, which is classified as an epi-demic due to its high rates of global prevalence. Machine learning techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This study investigates the ability of different classification methods to classify diabetes prevalence rates and the predicted trends in the disease according to associated behavioural risk factors (smoking, obesity, and inactivity) in Saudi Arabia. Classification models for diabetes prevalence were developed using different machine learning algorithms, including linear discriminant (LD), support vector machine (SVM), K -nearest neighbour (KNN), and neural network pattern recognition (NPR). Four kernel functions of SVM and two types of KNN algorithms were used, namely linear SVM, Gaussian SVM, quadratic SVM, cubic SVM, fine KNN, and weighted KNN. The performance evaluation in terms of the accuracy of each developed model was determined, and the developed classifiers were compared using the Classification Learner App in MATLAB, according to prediction speed and training time. The experimental results on the predictive performance analysis of the classification models showed that weighted KNN performed well in the prediction of diabetes prevalence rate, with the highest average accuracy of 94.5% and less training time than the other classification methods, for both men and women datasets. Full article

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modeling capability as well as a high-level of customization and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as being suitable within a number of applications contexts—i.e., as a research/educational tool, communication framework, gamification and participatory modeling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features. Full article

Melby presents a formula to predict damage evolution in rubble-mound breakwaters whose armour layer is made of rock, based on the erosion measured in scale-model tests and the characteristics of the incident sea waves in such tests. However, this formula is only valid for armour layers made of rock and for the range of tested sea states. The present work aims to show how the Melby methodology can be used to establish a similar formula for the armour layer damage evolution in a rubble-mound breakwater where tetrapods are employed. For that, a long-duration test series is conducted with a 1:50 scale model of the quay section of the Ericeira Harbour breakwater. The eroded volume of the armour layer was measured using a Kinect position sensor. The damage parameter values measured in the experiments are lower than those predicted by the formulation for rock armour layers. New $a_p$ and $b$ coefficients for the Melby formula for the tested armour layer were established based on the minimum root mean square error between the measured and the predicted damage. This work shows also that it is possible to assess the damage evolution in scale-model tests with rubble-mound breakwaters by computing the eroded volume and subsequently, the dimensionless damage parameter based on the equivalent removed armour units. Full article

While the effects of axial compression on beams have long been known, the effect of tensile axial loads on one-sided transversely cracked beams is less known. The crack namely shifts the position of the resultant of the axial normal stresses deeper into the uncracked part of the cross-section, and the crack tends to open, causing a transverse displacement. Therefore, this paper focuses on empirical modeling of the considered phenomenon for slender prismatic beams in order to establish a suitable 1D computational model based on detailed 3D FE mesh results. This goal can be achieved through the already established simplified model, where the crack is represented by an internal hinge endowed with a rotational spring. Several analyses of various beams differing in geometry, crack locations, and boundary conditions were executed by implementing 3D FE meshes to establish the appropriate model’s bending governing differential equation. After that, the corresponding parameter definitions were calibrated from the database of 3D FE models. By redefining the model’s input parameters, a suitable solution is achieved, offering a good balance between the results’ accuracy and the required computational effort. The functionality of the newly obtained solutions was verified through some comparative case studies that supplement the derivations. Full article

Urban water drainage systems represent complex networks with nonlinear dynamics and different types of interactions. This yields an involved modeling problem for which different off-line simulation approaches are available. Nevertheless, these approaches cannot be used for real-time simulations, i.e., running in parallel to weather now- and forecasts and enabling the monitoring and automatic control of urban water drainage systems. Alternative approaches, used commonly for automation purposes, involve parameterized linear delay systems, which can be used in real-time but lack the necessary level of detail, which, in particular, is required for adequate flood risk prognostics. Given this setup, in the present paper, an approach for the effective modeling of detailed water drainage systems for real-time applications implemented with the open-source Storm Water Management Model (SWMM) software is addressed and exemplified for a part of the water drainage system of the city of Flensburg in northern Germany. Additionally, a freely available early-warning system prototype is introduced and used to combine weather forcast information on a 2-h prediction horizon with the developed model and available measurements. This prototype is subsequently used for data assimilation using the ensemble Kalman filter (EnKF) for the considered area in Flensburg. Full article

This paper presents a modeling practical works project of electrical engineering, proposed to the first-year students of the University Institute of Technology in France, during the COVID-19 pandemic. The objective of this paper is twofold. The first objective is to present to the students the opportunities of modeling and calculation development of a spreadsheet software in their professional lives. The second objective is to create a file that automatically calculates all the current and voltage values at each point of any alternative electrical circuit. The aim of this paper, geared toward students, is to bring them to build their own numerical remote lab, autonomously. Therefore, pedagogical keys are given along the reading of this document to help them to progress, both on electrical circuits conceptual understanding with series and parallel RLC circuits and on their computation in a spreadsheet software. As a conclusion, this paper can be used as a base to develop remote modeling practical works of many and different devices, as well as a database starting point of such analytical models. Full article

The centrifugal screw-type pump is a type of pump which, due to its hydraulic and mechanical properties, is used in several areas of the industry (e.g., for sludge and rainwater disposal). To avoid impeller passage clogging, the 3D impeller geometry is designed as a helically curved blade added to a conical hub. The passability through the fluid canal of the modelled impeller is 100 mm. In this paper, the magnitude of the radial force on an impeller blade is investigated as a function of the flow rate. The digital model was designed in Catia V5 and calculated using the commercial Ansys CFX software. A numerical computational fluid dynamics (CFD) method was used to investigate the performance characteristics of the pump, specifically discussing internal flow conditions such as velocity, pressure and the radial force mentioned above. Full article

We review and define the current state of the art as relating to discrete event simulation in healthcare-related systems. A review of published literature over the past five years (2017–2021) was conducted, building upon previously published work. PubMed and EBSCOhost were searched for journal articles on discrete event simulation in healthcare resulting in identification of 933 unique articles. Of these about half were excluded at the title/abstract level and 154 at the full text level, leaving 311 papers to analyze. These were categorized, then analyzed by category and collectively to identify publication volume over time, disease focus, activity levels by country, software systems used, and sizes of healthcare unit under study. A total of 1196 articles were initially identified. This list was narrowed down to 311 for systematic review. Following the schema from prior systematic reviews, the articles fell into four broad categories: health care systems operations (HCSO), disease progression modeling (DPM), screening modeling (SM), and health behavior modeling (HBM). We found that discrete event simulation in healthcare has continued to increase year-over-year, as well as expand into diverse areas of the healthcare system. In addition, this study adds extra bibliometric dimensions to gain more insight into the details and nuances of how and where simulation is being used in healthcare. Full article

Cyclic degradation in fully saturated sands is a liquefaction phenomenon characterized by the progressive variation of the soil strength and stiffness that occurs when the soil is subjected to cyclic loading in undrained conditions. An evaluation of the relationships between the degradation of the soil properties and the number of loading cycles is essential for deriving advanced cyclic constitutive soil models. Generally, the calibration of cyclic damage models can be performed through controlled laboratory tests, such as cyclic triaxial testing. However, the undrained response of soils is dependent on several factors, such as the fabric, sample preparation, initial density, initial stress state, and stress path during loading; hence, a large number of tests would be required. On the other hand, the Discrete Element Method offers an interesting approach to simulating the complex behavior of an assembly of particles, which can be used to perform simulations of geotechnical laboratory testing. In this paper, numerical triaxial analyses of sands with different consistencies, loose and medium-dense states, were performed. First, static triaxial testing was performed to characterize the sand properties and validate the results with the literature data. Then, cyclic undrained triaxial testing was performed to investigate the impact of the number of cycles on the cyclic degradation of the soil stiffness and strength. Laws that can be used in damage soil models were derived. Full article