Developed Model Research Articles

Intermediary organizations, international cooperation and the changing social contract: Morocco’s new development model

ABSTRACT In 2021, King Mohammed VI’s appointed commission released a new vision for Moroccan state-society relations: the New Development Model (Nouveau Modèle de Développement, NMD). Presented as a ‘national collective and federative project’, it entailed a social protection system called a ‘social pact’ or ‘social contract’. The King’s vision articulated in the NMD promises changes that many Moroccans have long been calling for. The government has created several intermediary organizations mandated to work on reforms, which reflect citizens’ expectations but keep the pace of change under control. From an international cooperation perspective, these bodies are potential partners for reforms and achieving common development objectives. This article addresses two related questions: what scope of action do Moroccan intermediary organizations have to press for changes to the social contract under the NMD? Do they require international support in fulfilling their roles as change makers? Our analytical framework is based on scholarship exploring political settlements and institutional mandates in neo-patrimonial political systems. We focus on three intermediary organizations relevant to the deliverables of the social contract and discuss the extent to which they push the limits of their mandates to facilitate change. We then turn to the support that international development cooperation could provide.

Estimation of foam (surfactant) consumption in earth pressure balance tunnel boring machine using statistical and soft-computing methods

The use of foam, as the most economical soil conditioning technique, in earth pressure balance tunnel boring machine (EPB-TBM) tunneling projects has significant effects on operation efficiency, excavation cost, and operation time. This study mainly focuses on developing models to predict the foam (surfactant) consumption. For this purpose, five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran. This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption. Multiple linear regression analysis, multiple non-linear regression analysis, M5Prime decision tree, artificial neural network, and least squares support vector machine techniques are used to construct the models. To evaluate the performance of developed models, three performance evaluation criteria (including normalized root mean square error, variance account for, and coefficient of determination) are used based on the training and testing datasets. The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset. Furthermore, the M5Prime model, which demonstrates the best performance compared to other models, is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro, Iran. After conducting an excavation operation in this station and comparing the results, it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%. Therefore, the developed models, particularly M5Prime model, can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.

A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials

This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimize the mixture composition of soilcrete mixes according to different site requirements.

Long-Short Term Memory Network Based Model for Reverse Brute Force Attack Detection

Reverse brute force attacks pose a significant threat to the security of online systems, where adversaries attempt to gain unauthorized access by systematically testing a multitude of username and password combinations against a single account. To address this challenge, the research presents an innovative Long-Short Term Memory Network based model designed to detect such attacks. The model utilizes LSTM algorithms to analyze login attempt patterns, identifying anomalies that may indicate reverse brute force attacks. By examining various factors like user login behavior, IP address, and time-based patterns, the model distinguishes legitimate access attempts from potential attacks with high accuracy. It incorporates real-time threat intelligence feeds and historical data analysis to continuously adapt and improve its detection capabilities. The model dynamically adjusts security parameters, enforces account lockouts, and communicates with firewall systems to block suspicious IP addresses, thus providing a proactive response to thwart attacks. The research evaluates the effectiveness of the AI model through simulated and real-world testing scenarios, demonstrating a significant reduction in false positives and successful prevention of reverse brute force attacks. Overall, the developed AI model offers a sophisticated and proactive solution to the evolving threat of reverse brute force attacks, contributing to the advancement of cybersecurity measures.

XCP-D: A Robust Pipeline for the Post-Processing of fMRI data

Abstract Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they may not support output from different pre-processing pipelines, may have limited documentation, and may not follow generally accepted data organization standards (e.g. BIDS). In response, we present XCP-D: a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University of Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Apptainer image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NIfTI or CIFTI files following pre-processing with fMRIPrep, HCP, or ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >5,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

The perception of affordances; towards an ecological-enactive model of teacher noticing, expertise and development

ABSTRACT This article builds on nascent ecological approaches to teacher noticing by drawing on the ecological psychology and enactivist concept of ‘affordance’ – meaning opportunity for action – and offers a new definition of teacher noticing; the perception of affordances. This perspective provides a deeper understanding of what noticing is, particularly why it is so fundamentally situated, and moreover provides a mechanism by which expert noticing is acquired and thereby points to concrete ways in which noticing can be developed. I argue that an ecological-enactive approach offers a productive, and more comprehensive model of teacher noticing.

Conducted Emission signal-based identification and real-time hardware security with deep learning

In advancing electronic systems, the replicable nature of devices used in critical environments raises concerns regarding system security. It is of great importance to establish security by identifying electronic devices and computers based on their unique electrical signals, rather than assigning an artificial identity number for security purposes. This paper presents a methodology for identifying electronic devices by capturing their Conducted Emission signal over cables using a Line Impedance Stabilization Network (LISN), followed by applying deep learning techniques. After the extraction of the identities specific to each device based on their electronic characteristics, the establishment of security has been accomplished through these characteristics. The identification process was analyzed through theoretical approaches, and signals were examined by acquiring simulation-based data. After performing the analyses, the data was converted into spectrograms for training the deep learning model using the Conducted Emission signals, and a Convolutional Neural Network (CNN) was used due to its recognized efficacy in handling two-dimensional data formats like spectrogram for the deep learning algorithm. It was observed that with an accuracy of 99.9%, identification was achieved in deep learning results utilizing the data obtained through simulations. Real-time data from 15 devices was collected after training the system with simulation-based data, demonstrating a 99.8% accuracy in accurately identifying the devices. Furthermore, the developed deep learning model was deployed on an Embedded Linux edge device, tested within the system for one day, and achieved an average accuracy of 99.54% in accurately identifying registered devices.

Harnessing Artificial Neural Networks for Financial Analysis of Investments in a Shower Heat Exchanger

This study focused on assessing the financial efficiency of investing in a horizontal shower heat exchanger. The analysis was based on net present value (NPV). The research also examined the possibility of using artificial neural networks and SHapley Additive exPlanation (SHAP) analysis to assess the profitability of the investment and the significance of individual parameters affecting the NPV of the project related to installing the heat exchanger in buildings. Comprehensive research was conducted, considering a wide range of input parameters. As a result, 1,215,000 NPV values were obtained, ranging from EUR −1996.40 to EUR 36,933.83. Based on these values, artificial neural network models were generated, and the one exhibiting the highest accuracy in prediction was selected (R2 ≈ 0.999, RMSE ≈ 57). SHAP analysis identified total daily shower length and initial energy price as key factors influencing the profitability of the shower heat exchanger. The least influential parameter was found to be the efficiency of the hot water heater. The research results can contribute to improving systems for assessing the profitability of investments in shower heat exchangers. The application of the developed model can also help in selecting appropriate technical parameters of the system to achieve maximum financial benefits.

Unified analytical and design-oriented models for compressive and tensile membrane actions in RC beam-slab structures under internal column loss

Resisting mechanisms including compressive membrane action (CMA) and tensile membrane action (TMA) are critical for RC beam-slab structures against progressive collapse. However, due to the difficulties in considering the nonlinear behaviors and beam-slab interactions, limited analytical studies are currently available to accurately determine the whole resistance evolution path, let alone a simplified design model. This study presents a unified analytical model and a design-oriented simplified model to predict the whole resistance displacement of RC beam-slab substructures under internal column loss. Both equivalent uniformly distributed load (EUDL) and concentrate load (CL) conditions have been considered. In the analytical model, the beam-slab system is discretized into interior beams and multiple slab strips with consideration of their interaction. The compatibility and equilibrium conditions, curvature equations, and constitutive laws of concrete and reinforcement are incorporated to analyze each beam and slab strip. In the design model, a new strategy is proposed to decompose the structural resistance of membrane effect into summation of two components, CMA and TMA resistances, with explicit expressions. Both developed models are validated through experimental and numerical results and good agreements are achieved. Results of the analytical model show that ignoring the beam-slab interactions can lead to underestimation of structural resistance, especially during the transition from the CMA to TMA. The locations of loading points should be carefully selected to make sure 12-point loading system effective. A rule is also suggested to determine proper locations. The simplified design method gives slightly conservative predictions of structural resistance, and its calculation is easy for engineers to use.

The Marginal Effect and LSTM Prediction Model under the Chinese Solar Greenhouse Film

The solar greenhouse is a significant agricultural facility in China. It enables the cultivation of crops during periods that do not coincide with the natural growing season, thus alleviating the pressure on the supply of fruits and vegetables during the winter months. The primary rationale behind the necessity of greenhouse cultivation lies in the fact that the temperature conditions conducive to optimal crop growth can be precisely replicated within this controlled environment. However, it is important to acknowledge that a distinct low-temperature area persists under the film during the overwintering period, with the precise delineation of its boundaries and distribution patterns remaining uncertain. In order to investigate the characteristics of the temperature distribution within the marginal region under the solar greenhouse film, experimental studies, CFD simulations, and LSTM prediction models were employed. The results of these studies indicate that, during the overwintering period, a low-temperature region was observed with approximately equal temperatures near the film membrane. The maximum horizontal distance from the south-side bottom corner was 6130 mm, while the minimum height from the ground was 600 mm. The lowest temperature in the low-temperature region was 4 °C, and the maximum observed temperature difference within the same period in different months was 1 °C. Additionally, a region of elevated temperatures was observed under the film. The lowest temperature in this region was 36.7 °C, and the highest temperature point was within the optimal range for crop growth. The CFD numerical simulation results were consistent with the actual observations, and the LSTM prediction model demonstrated high reliability. The findings of this study offer a theoretical foundation for the distribution of high and low temperatures in solar greenhouses. Furthermore, the developed prediction model provides the necessary buffer time for control, thus enhancing the efficiency of greenhouse cultivation.

Performance Evaluation and Comparison of Deep Neural Network Models for African Soil Properties Prediction

ABSTRACT The significance of soil in agriculture and human survival cannot be overstated. Given its limited supply, improving soil properties is imperative. This study addresses this challenge by predicting five major soil properties—organic carbon, pH values, Mehlich-3 extractable calcium, Mehlich-3 extractable phosphorus, and sand content—utilizing mid-infrared absorbance measurements from the African Soil Information Service (AfSIS) dataset, covering non-desert regions of Africa. With a pressing need for a reliable and efficient model to predict African soil properties using spectral measurements, this study fills a crucial gap, addressing the scarcity of functional soil property databases in Africa. The developed model eliminates costly soil sample preparation and lengthy chemical analysis, applicable in both onsite and laboratory settings for determining soil functional properties. By employing stacked autoencoders for feature dimensionality reduction and combining discrete wavelet transform with two feature selection methods (stepwise regression and random forest) to build robust multi-layer perceptron (MLP) models, the study offers a comprehensive approach. Evaluation metrics including root mean square error (RMSE), scatter index (SI), Variance Accounted For (VAF), Nash-Sutcliffe Model Efficiency (NSE), and Correlation Coefficient (R) demonstrate the superior performance of the MLP model informed by stacked autoencoder-selected features, outperforming models informed by wavelet-transformed features and partial least square regression. This best-performing autoencoder-based model presents a valuable tool for soil scientists tasked with modeling African soil properties.

MAF process for aluminum 6060: An analytical temperature modelling and chemo-mechanical analysis

The magnetic abrasive finishing process is a sophisticated micro-finishing process that enhances the surface quality of superalloys, ceramics, and composites. Surface temperature plays a pivotal role in facilitating chemo-mechanical reactions between the processed surface and abrasive particles. In triggering the chemo-mechanical response of the micro-finished surface of Aluminum 6060 (Al-6060), an analytical thermal model derived using Jaeger moving heat formulation to find the flash temperature (FT) and flash time duration (FTD) at the micro-finishing interface of the processed workpiece and the abrasive particles of the magnetic abrasive brush. Furthermore, the present work performed an RSM-based Box Behnken design experiment to analyse and explain the crucial role of machining gap, abrasive weight, Voltage, and rotational speed on surface temperature, machining surface, and hardness. ANOVA highlighted key factors in machining responses. Surface roughness and temperature were notably influenced by voltage (58.42 % and 57.62 %, respectively), while surface hardness primarily depended on abrasive weight (60.65 %). Using the developed model, extreme FT of 880 °C and 810 °C were calculated under specific conditions, along with corresponding FTD required for quasi-steady-state conditions. FT and FTD were identified as sufficient for initiating chemo-mechanical action in the MAF micro-finishing process, depending on contact pressure, the number of abrasive particles, and sliding velocity, which in turn are influenced by magnetic intensity, abrasive ratio, and rotational speed. Furthermore, a detailed examination of the finished surface was carried out using the SEM, EDS and XRD of Al-6060 which shows that the SiC does participate on the finishing surface. Chemical substrates such as Kaolinite (Al2O9Si2), Iron silicide (FeSi), and Cristobalite (O2Si) had formed on the finished surface of Al-6060 due to the chemo-mechanical action between the SiC and workpiece material.

Increasing Student Success in Mathematics and Statistics through Sustainable Faculty Professional Development

Inclusive and student-centered approaches to teaching are crucial for student success in mathematics and statistics courses. However, faculty need ongoing support and education to effectively implement active learning strategies that address issues such as equitable participation, to ensure all students meet their learning objectives. At California State University Monterey Bay, we have implemented a professional development model focusing on regular professional development activities that meet the ongoing needs of our faculty that will help their students succeed. This model supports both new and experienced faculty, drawing on existing campus expertise while building faculty teaching skills and a stronger teaching and learning community over time. In this paper, we discuss the lessons learned from the first year of implementing our professional development model, as well as considerations for other departments that are interested in creating a similar model for professional development aimed at increasing student success, building community in the department, and improving teaching and learning for students and faculty.

Baseline Econometric Model and Solar Energy Requirement for Potential Hydrogen Demand in Tarlac, Philippines

This study aims to build a baseline econometric model for the possible development of green hydrogen production in Tarlac. Scenario analysis will consider hydrogen produced from renewable solar energy as an alternative energy carrier in the fuel mix for electricity generation in Tarlac to achieve sustainable economic growth and improve energy security while minimizing carbon emissions and improving resource efficiency. Tarlac is the chosen area to be considered in this study because this is where New Clark City is being established. New Clark City is a 9450-hectare disaster-resilient, environment-friendly, integrated urban development metropolis. It is important to build the baseline econometric model before proceeding with scenario generation. The parameters used are determined from available historical data through statistical procedures. The basic assumption is that the relationships between the independent and dependent variables that existed in the past will continue to be true in the future. The energy demand data and the continuous predictors were used to build a baseline econometric model for potential hydrogen demand in Tarlac, Philippines. Model equations were obtained using stepwise regression analysis. These model equations were used to forecast the energy demand up to 2032. These forecasted energy demands will be used in scenario generation to integrate green hydrogen production. Preliminary assessments of green hydrogen scenarios that are based on the assumption of renewable energy surplus generated from solar power plants in Tarlac are discussed. The forecasted demand will be used to assess the scenario generation in integrating green hydrogen in New Clark City. The proposed energy management strategy of storing surplus renewable energy, such as green hydrogen, for the power sector has the potential to impact various stakeholders, including industries and the government, significantly. This strategy aligns with transitioning to cleaner and more sustainable energy sources, contributing to national and international environmental targets. Adopting green hydrogen storage can contribute to energy security by diversifying the sources of power generation.

Urban Development Scenario Simulation and Model Research Based on System Dynamics from the Perspective of Effect and Efficiency

Urban Development Scenario Simulation and Model Research Based on System Dynamics from the Perspective of Effect and Efficiency

Analysis of some newly constructed compact models in f(R, T) theory

In this paper, black hole solutions are developed within the framework of f(R, T) gravity through the minimal gravitational decoupling approach. By introducing a new source in the original isotropic matter distribution, the corresponding field equations acquire additional degrees of freedom. Applying the transformation on the radial metric function leads these equations to two distinct sets, each representing the influence of either the seed or additional source exclusively. In order to formulate the black hole solutions, the seed source is considered to be a vacuum, characterized by the Schwarzschild metric. To derive a viable solution for the second system, constraints are imposed on the metric potentials and energy-momentum tensor of the additional source. Three distinct solutions are graphically analyzed based on varying values of the decoupling parameter. The energy conditions are also plotted to determine the nature of the extra source. Finally, it is concluded that two of our three developed models agree with the asymptotic flatness criterion and energy bounds in this modified theory.

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2.

To evaluate the application of an intelligent diagnostic model for pterygium. For intelligent diagnosis of pterygium, the attention mechanisms-SENet, ECANet, CBAM, and Self-Attention-were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models-VGG16, ResNet50, MobileNetV2, and EfficientNetB7-were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used. The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively. The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.

An internal state variables constitutive model for Ni-based superalloy considering the influence of second phase and its application in flexible skew rolling

Microstructure prediction and control of Ni-based alloy during plastic forming is crucial for obtaining high-performance products. Taking GH4169 alloy as an example for study, firstly, hot compression experiments were conducted using both solution treated (ST) and aging treated (AT) alloys at different true strains (0.223–0.916), temperatures (900℃-1100℃), and strain rates (0.1 s−1-10 s−1). Then, an internal state variable constitutive model was established based on experimental data. In the developed model, the evolution of dislocation density, dynamic recovery, and dynamic recrystallization (DRX) behavior are reasonably described. Specifically, by introducing the volume fraction and size of the initial δ-phase, the interaction between δ-phase and dislocations, the influence of δ-phase on DRX behavior and critical strain, and its pinning effect on grain boundaries are considered. The calculation results indicate that accurate predictions can be achieved within a large parameter range. Subsequently, the model was applied to flexible skew rolling (FSR) process, a novel plastic forming method for forming shaft parts and bars. The finite element (FE) simulations and rolling experiments were carried out. The results indicate that the FE model embedded with the constitutive model can effectively predict the forming dimensions and microstructure distribution of GH4169 alloy. The established constitutive model can provide reference for the high-temperature plastic forming process of alloys containing the second phase.

A wideband polarization diversified MIMO antenna with improved isolation using machine learning optimized metasurface absorber

This communication presents a model and optimization of a dual-sense circularly polarized (RHCP/LHCP) 2-port MIMO antenna with increased isolation. The antenna operates in a broad frequency range of 1.76–4.0 GHz and is loaded with a metasurface. It has dimensions of 98.5 × 39 mm2 and an edge-to-edge distance of 55.5 mm. The MIMO antenna is created by replicating a single unit and placing it in a mirrored configuration around the y-axis. The dimensions of the unit antenna are 40 × 39 × 0.8 mm3 and it is printed on a substrate made of FR4 with a thickness of 0.8 mm. The substrate has a relative permittivity (ε r) of 4.4 and a loss tangent (tanδ) of 0.02. The isolation between the ports may be enhanced by including a metasurface absorber, increasing from 12 dB to 23 dB. To verify the wideband and CP properties, we examine the MIMO antenna unit element for its reflection coefficient, peak gain (4.2 dB), radiation efficiency (about 90%), axial ratio, and surface current distribution. Machine learning is used to improve the design of metasurface unit cells to achieve optimum absorption. The Envelope correlation coefficient (ECC), Diversity gain (DG), and Channel capacity loss (CCL) of the proposed metasurface absorber-enabled antenna are simulated and calculated to prove its MIMO identity. The HFSS simulations are consistent with the experimental results obtained from the developed model.

FOREIGN ECONOMIC ACTIVITY IN THE SYSTEM OF ACCOUNTING AND ANALYTICAL MANAGEMENT SUPPORT

The article decomposes the foreign economic activity of enterprise through the prism of accounting and analytical support of management. It has been determined that current trends in the development of the domestic and world economy, specificity and peculiarities of activities of individual business entities in the field of foreign economic relations, the need for further development of methodology and methods of accounting and analysis of foreign economic activity, taking into account the objectively arising needs for the development of information support for management, determine the relevance of decomposition of foreign economic activity in the system of accounting and analytical support for management. It is substantiated that the basis for the decomposition of foreign economic activity in the system of accounting and analytical support of management is the concept of the type of activity. The organisation of accounting of foreign economic activity at a particular enterprise is largely determined by the type of foreign economic activity inherent in this enterprise. In the process of production and economic activity, enterprises, as a rule, face several types of foreign economic operations simultaneously. The accounting and analytical aspects of management of foreign economic activity of enterprise are systematised. A model of decomposition of the foreign economic activity of enterprise has been developed. It is proposed that the model of decomposition of foreign economic activity of enterprise should include types of foreign economic activity and accounting and analytical aspects, grouped into blocks: key aspects; characteristics of types of foreign economic activity, organisational and legal conditions, purpose, directions and results of functioning, accounting approaches and basic analytical criteria, performance indicators, structural construction and functioning, etc. The developed model provides for the reflection and determination of performance results by types, subtypes, segments and other classification features of foreign economic activity in order to obtain analytical, detailed, transparent and relevant information based on financial, management and strategic accounting data. Formation of an integrated system of accounting and analysis of foreign economic activity is based on consideration of regulatory, contractual, structural, accounting, analytical, control and software and hardware support of foreign economic activity.