Developed Model Research Articles

Pengembangan Media Pembelajaran Berbasis Whatsapp dengan Fitur Chatbot untuk Meningkatkan Pemahaman Siswa pada Materi Kebijakan Moneter dan Fiskal Kelas XI SMAS Islam Nurul Amanah

The purpose of this development research is to produce a whatsapp-based learning media product with a chatbot feature to improve students' understanding of monetary and fiscal policy materials. SMAS Islam Nurul Amanah Class XI is the subject of this study. In this study, a type of development research (Development Research) is used. The development model in this study uses the ADDIE development model. This model consists of five main phases or stages, namely Analysis, Design, Development, Implementation, and Evaluation. In this study, quantitative and qualitative methods are used. The results of the study showed that the whatsapp-based learning media with the chatbot feature was considered quite valid based on the results of a questionnaire filled out by material validators with a score of 70%, media validators with a score of 78%, and obtained a very valid assessment from economic learning practitioners with a score of 87%. Based on the results of validation and field trials, whatsapp-based learning media with a chatbot feature that has been developed by researchers is feasible to be used in the learning process in economics lessons, especially in monetary and fiscal policy material for grade XI of SMAS Islam Nurul Amanah.

IDENTIFICATION AND LOCALIZATION OF VULNERABILITIES IN SMART CONTRACTS USING ATTENTION VECTORS ANALYSIS IN A BERT-BASED MODEL

Context. With the development of blockchain technology and the increasing use of smart contracts, which are automatically executed in blockchain networks, the significance of securing these contracts has become extremely relevant. Traditional code auditing methods often prove ineffective in identifying complex vulnerabilities, which can lead to significant financial losses. For example, the reentrancy vulnerability that led to the DAO attack in 2016 resulted in the loss of 3.6 million ethers and the split of the Ethereum blockchain network. This underscores the necessity for early detection of vulnerabilities. Objective. The objective of this work is to develop and test an innovative approach for identifying and localizing vulnerabilities in smart contracts based on the analysis of attention vectors in a model using BERT architecture. Method. The methodology described includes data preparation and training a transformer-based model for analyzing smart contract code. The proposed attention vector analysis method allows for the precise identification of vulnerable code segments. The use of the CodeBERT model significantly improves the accuracy of vulnerability identification compared to traditional methods. Specifically, three types of vulnerabilities are considered: reentrancy, timestamp dependence, and tx.origin vulnerability. The data is preprocessed, which includes the standardization of variables and the simplification of functions. Results. The developed model demonstrated a high F-score of 95.51%, which significantly exceeds the results of contemporary approaches, such as the BGRU-ATT model with an F-score of 91.41%. The accuracy of the method in the task of localizing reentrancy vulnerabilities was 82%. Conclusions. The experiments conducted confirmed the effectiveness of the proposed solution. Prospects for further research include the integration of more advanced deep learning models, such as GPT-4 or T5, to improve the accuracy and reliability of vulnerability detection, as well as expanding the dataset to cover other smart contract languages, such as Vyper or LLL, to enhance the applicability and efficiency of the model across various blockchain platforms. Thus, the developed CodeBERT-based model demonstrates high results in detecting and localizing vulnerabilities in smart contracts, which opens new opportunities for research in the field of blockchain platform security.

Pengembangan Media Ajar Interaktif Berbantuan Microsoft Powerpoint Menggunakan Add-In Classpoint Pada Mata Pelajaran Komputer Akuntansi Kelas XI AKL

This development research aims to produce an interactive teaching media product assisted by Microsoft Powerpoint using the Classpoint add-in in the Accounting Computer subject class XI AKL. Another goal is to find out the process of developing teaching media, the feasibility of teaching media, and the response of students to the developed teaching media. This study uses a type of R&D (Research and Development) research with the ADDIE development model which consists of 5 stages, namely Analyse, Design, Develop, Implement, and Evaluate. The test subjects of this study are grade XI AKL students from SMK YPM 11 Wonoayu. The results of this study show the results of the material expert test by the Lecturer of the Faculty of Economics and Business and the Teacher of the Accounting Computer subject of SMK YPM 11 Wonoayu which is said to be very feasible with an average percentage of 88.13%, while the results of the media expert test by the Lecturer of the Faculty of Economics and Business are said to be very feasible with an average presentation of 88.89%. The response from students of class XI AKL SMK YPM 11 Wonoayu to the interactive teaching media developed received an average of 97.99% which was included in the category of very understanding. From the average results of the above percentage, it can be said that interactive teaching media assisted by Microsoft Powerpoint using the Classpoint add-in in the Accounting Computer subject class XI AKL is very feasible and can be applied to learning in the classroom

Modelling project activities for future teachers within the innovative infrastructure of pedagogical universities: integrative solutions

Introduction. In the quest to enhance the training of future teachers, particular emphasis is placed on identifying the educational potential of new learning environments within pedagogical universities under the Ministry of Education of Russia. These institutions have undergone infrastructure updates as part of federal programs designed to support education. Aim. The aim of this study is to develop an original model for organising the project activities of student teachers, taking into account the contemporary infrastructural capabilities of pedagogical universities under the Ministry of Education of Russia. Methodology, methods and techniques. The methodological foundation of this research is grounded in the principles of community development pedagogy and the design of evolving educational systems. The integration of integrative, synergetic, environmental, and system-activity approaches facilitated the identification and development of components within the author’s model for organising project activities for future teachers in the context of pedagogical technoparks, such as “Quantorium”, and technoparks focused on universal pedagogical competencies in higher education institutions under the Ministry of Education of the Russian Federation. The validation of the developed model was conducted through expert assessments. Results. The authors developed the model for organising project activities for future teachers within the innovative infrastructure of pedagogical universities. The main components of the model are outlined, including integrative objectives and anticipated outcomes of students’ project activities, interdisciplinary project content, and a technological component that facilitates the transformation of interdisciplinary project content into personally meaningful experiences through the technology of collaborative distributed project activities. The study identifies and justifies the conditions necessary for the effective implementation of the developed model, focusing on the management of processes, resources, and participants involved in interdisciplinary project activities. Scientific novelty. Integrative processes, within the context of this study, are regarded as a pedagogical phenomenon that reflects the interconnections among the objects, processes, and subjects involved in project activities. Practical significance. The research materials can be utilised in the development of programmes aimed at enhancing the infrastructure of pedagogical universities. They can also assist in the refinement of educational curricula for training teaching staff, particularly in disciplines that prepare future educators to effectively organise project-based activities within contemporary educational environments.

ЛЮДСЬКИЙ КАПІТАЛ В КОНЦЕПЦІЇ ВІДКРИТОЇ НАУКИ

Introduction. Any economic system's development is only possible with capital participation. Over several centuries, a stable concept has emerged that considers human capital as an essential source of investment for innovation, scientific and technological progress, and the stable development of the state. At the same time, the practical realization of this concept is associated with such categories as education, science, healthcare, competencies, skills, professional abilities of employees, intellectual output, etc. The purpose of the article: The aim is to comprehensively investigate the innovative potential of human capital from the perspective of a driving force for progress in the knowledge age. Methods: Modern science is based on openness and accessibility as tools of scientific search and research. Human capital is an essential element of this search, expressed in intellectual abilities, self-development, and theoretical and practical generalizations. The basis for more profound research and generalizations is the study of internal and external influences on the formation of human capital and the definition of its role in the scientific sense. Results: Analyzing human capital through the prism of its structural formation, it is worth noting that its meaning has changed in different periods. In the twentieth century, when the economy was formed in the context of industrial development, human capital was focused on technological excellence and ingenuity. In the 21st century, economic development is dominated by the intellectual and scientific component, which is the basis for the functioning of the national system. Therefore, human capital in this period is gaining new meaning as a knowledge economy. In forming the country's national economy, the intangible form of capital is becoming increasingly important, where human resource is the basis of socio-economic development. Conclusions. As a result of the theoretical analysis of existing approaches and new proposals, the innovative economic development model is developing dynamically. Human (intellectual) capital is prioritized in this model, stimulating informatization and introducing modern scientific and technological innovations. Human capital is a complex phenomenon that adapts to modern innovative and technological changes and acts as an organizational motivator for achieving a high level of industrialization and a civilized environment.

Predicting the removal efficiency of titanium dioxide nanoparticles in an activated sludge system using fate modeling depending on the operating conditions

Wastewater treatment plants (WWTPs) are a major discharge pathway for engineered nanoparticles (ENPs) that can potentially impact aquatic systems, making it crucial to understand ENP behavior in these facilities. In this study, we developed a fate model for titanium dioxide (TiO2) nanoparticles (NPs) to predict their removal efficiency (RE) in activated sludge (AS) systems. The model, based on material balance equations for free and sludge-attached TiO2 NPs, incorporated a linear-adsorption partition coefficient (Kd) and pseudo-second-order kinetics for TiO2 NP attachment to the activated sludge. Validated with data from three WWTPs, the model investigated RE correlations with inflow TiO2 NP concentration, sludge retention time (SRT), hydraulic retention time (HRT), mixed liquor suspended solid (MLSS) concentration, and water temperature. The model reasonably predicted the comparative performance of the WWTPs but underestimated the RE by up to ∼29 %, probably due to system simplification, limited Kd data, the presence of other Ti-containing NPs besides TiO2 NPs, and unincorporated removal mechanisms such as sweeping in the secondary clarifier. The model revealed favorable design/operating conditions for increasing the TiO2 NP RE of the AS system: shortening the SRT, increasing the MLSS concentration, and increasing the water temperature. The developed model could be useful for estimating and comparing the expected TiO2 NP REs across different WWTPs under varying operating conditions or for tracing the material flow and fate of TiO2 NPs in environmental compartments involving WWTPs, both regionally and country-wide.

Development and validation of prediction models for prehospital triage of military trauma patients.

The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes. A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration. Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort. The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.

Development of Particulate Matter Concentration Estimation Models for Road Sections Based on Micro-Data

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of Blockchain Technology in various domains

Blockchain technology (BT) is a digitally decentralized, distributed and public ledger, which considers a secure and viable solution for storing and accessing the record transactions in a public or private peer-to-peer network and assures a smart world of automation of complex services. This paper aims to evaluate the factors persuading the BT adoption and also to assess the possible application areas for the adoption of BT. Implementation of the BT process considers multiple criteria and alternatives with uncertain information; therefore, it can be considered as an uncertain multi-criteria decision-making problem. In this context, a hybrid approach is proposed based on the criteria interaction through inter-criteria correlation (CRITIC) and the complex proportional assessment (COPRAS) methods with interval-valued q-rung orthopair fuzzy set (IVq-ROFS) named as the “IVq-ROF-CRITIC-COPRAS” framework. In this approach, the significance values of the factors are computed through novel score function-based CRITIC model, whereas the CRITIC-based COPRAS model is employed to rank different domains/application areas for the adoption of BT under IVq-ROFSs environment. In this regard, a novel score function is introduced with its desirable characteristics. Further, the developed model is executed on a case study of BT adoption considering 14 factors, which confirms the efficiency and applicability of introduced approach. Based on the results, the healthcare BT alternative is most appropriate among the others. To evaluate its permanence, sensitivity analysis with regard to various ratings of decision strategic coefficient is presented under IVq-ROFS context. The strength of the developed approach is emphasized by comparing it with some of the former models under the context of IVq-ROFSs. The proposed extension of the COPRAS approach can be utilized to solve other complex group decision-making problems with uncertainties.

Analysis of Changes in the Stress–Strain State and Permeability of a Terrigenous Reservoir Based on a Numerical Model of the Near-Well Zone with Casing and Perforation Channels

A finite element model, which includes reservoir rock, cement stone, casing, and perforation channels, was developed. The purpose of the study is to create a geomechanical model of the zone around the well, which includes support elements and perforation channels. This model will help predict changes in the productivity coefficient of a terrigenous reservoir and determine the most efficient mode of operation of a producing well. In order to exclude the stress concentration within the casing–cement stone and cement stone–rock, the numerical model applies contact elements. As a result, structural elements slip, while the stresses are redistributed accurately. The numerical simulation of a stress state in the near-well zone was carried out by using the developed model with differential pressure drawdown on the terrigenous reservoir, one of the oil fields in the Perm region. It is shown that the safety factor of the casing reaches roughly 3–4 units. The only exceptions are the upper and lower parts of the perforations, where this parameter is close to one unit. The safety factor of cement stone accounts for 2–3 units. However, parts with its lowest value (1.35) are also concentrated near the perforation channels. In order to analyze the change in permeability, the dependence of the safety factor on effective stresses was taken into account. Therefore, it was found that, in the upper and lower parts of perforations, the stresses decreased, while permeability rose by up to 20% of the initial value. An increase in differential pressure drawdown, on the contrary, can lead to a permeability reduction of 25%, especially in the lateral parts of the perforations. Areas of rock destruction under tensile and compressive forces were identified by using the Mohr–Coulomb criterion. It is estimated that with an increase in pressure drawdown, the areas of rock destruction under tensile force disappear, while the areas of rock destruction under compression increase. After further analysis, it was found that, with the maximum pressure drawdown of 12 MPa, the well productivity index can decrease by 15% due to the reservoir rock compaction.

Simulation of thermal-mechanical coupling in Al alloy/steel inertia friction welding

Inertia friction welding (IFW) is a complicated thermal-mechanical coupling process. In-depth understanding of the thermal-mechanical process can improve the joint quality. In this study, a three-dimensional (3D) thermal-mechanical coupling model was successfully established to simulate the evolution of temperature, energy density, stress, strain, and flow behavior of the IFW process between 2219-O Al alloy and 304 stainless steel. The developed model was validated by comparing it with the measured temperature and deformation. Results show a transforming distribution of interface temperature from a M-shape to a squished V-shape during the IFW process. The characterization is attributed to the transformation of the accumulated friction energy density at the interface from a M-shape to an approximate V-shape due to the combined effect of varying sliding velocity and contact pressure. The Mises stress at the region near the interface is high and low at the steel and Al alloy sides, respectively. A significant portion of the kinetic energy stored in the flywheel is dissipated in the form of plastic deformation energy and cannot be neglected. The tracer particles show that most Al alloy at the initial interface is extruded to form flash, while a small amount of Al alloy is sticky and remains at the interface center. Increasing friction pressure and initial rotation speed result in higher interface temperature, axial shortening distance, and proportion of plastic work in total energy input.

Performance analysis of turning operation parameters empirically on Delrin

Delrin is the best additional material for metals because of its inherent qualities, such as great wear resistance and tensile strength. The main aim of this work is to investigate how independent variables like feed rate, spindle speed, and depth of cut are significant for dependent variables such as surface roughness, temperature, stresses, and material removal rate on novel material Delrin. The independent variable ranges are selected based on tool and workpiece material combinations and machine tool specification as spindle speed 230 – 844 rpm, feed rate 0.5 – 1.5 mm/rev, and depth of cut 1 – 3 mm. Based on the L27 orthogonal array experimental plan 27 numbers of experiments are conducted by the design of experiment concepts. The theoretical investigation through response surface methodology is also conducted to establish how independent variables affected dependent variables when Delrin was being machined. The independent variable's significance is determined by the ANOVA table for all the considered responses. In addition to the considered flow of work, the experiential model is developed by the utilization of regression analysis. The developed models are confirmed by experimental data and the models have the best validation results with the experimental results.

A rate-dependent crack bridging model for dynamic fracture of CNT-reinforced polymers

Carbon nanotubes (CNTs) improve the fracture toughness of polymer-based matrix composites by bridging the crack growth path. This research presents a finite element (FE) rate-dependent crack bridging model of Mode-I dynamic crack growth in CNT-reinforced polymers accounting for rate of loading and rapid crack growth. Two distinct CNT bridging and matrix cracking damage mechanisms are taken into account in the fracture process zone (FPZ) accounting for the dissipation of fracture energy. A viscoelastic-viscoplastic material model is adapted for the matrix phase to capture the strain rate effects. The crack in the matrix phase is modeled using cohesive zone elements characterized by experimental data. A rate-dependent traction-separation law obtained from CNT pull-out simulation at relevant crack opening speeds is used to simulate the CNT bridging in the FPZ. Considering the given traction-separation law as a constitutive equation, the CNTs are replaced with non-linear spring elements to facilitate the FE simulation of crack bridging with numerous CNTs in the FPZ. The proposed rate-dependent FE model for crack bridging enables the study of the effect of key CNT factors such as length, orientation, waviness, volume fraction, and agglomeration on fracture energy dissipation at various crack speeds. The developed model can simultaneously consider the interactive effects of all CNT parameters which is useful for considering all processing-induced uncertainties in analyzing the effects of CNTs on the dynamic fracture toughness of nanocomposites.

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering–attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64–0.71 and root mean square error in the range of 2.06–2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

Machine learning-based prediction of seismic response of elevated steel tanks

Seismic response analysis assesses structural safety and integrity during earthquakes and guides design for optimal performance, compliance with regulations, and risk mitigation. This study proposes a novel approach utilizing machine learning (ML) techniques to predict the seismic response of elevated steel tanks accurately. ML is a powerful tool that can handle complexity, improve accuracy, provide data-driven insights, and optimize designs for predicting seismic responses of structures. Moreover, this study evaluates various ML models containing Random Forest, Support Vector Regression, Adaptive Boosting, LightGBM, Histogram-based Gradient Boosting, XGBoost, CatBoost, Bagging Regression, and Decision Trees to identify the most effective predictive model. In general, the developed ML-based predictive models provide a promising tool for engineers involved in the seismic design and retrofitting of elevated steel tanks, aiding in optimizing designs and enhancing structural safety and resilience against seismic hazards. As a result, the HGB model obtained the most suitable performance compared to other developed models with higher R2 and lower RMSE. In addition, a sensitivity analysis was performed based on SHAP values, and it was found that the height of the liquid and the intensity measure were the most influential variables in the seismic response of the tank.

Application of ANN Concepts for Prediction of Crack Growth and Remaining Life of Circumferentially Cracked Piping Components under Different Loading Scenarios

This paper presents the details of Artificial Neural Network (ANN) based models to predict crack depth and remaining life of circumferentially part-through cracked piping components used in the nuclear industry. Crack growth data from experimental studies on (i) straight pipes made up of SA 312 Type 304LN stainless steel having part-through circumferential notch under combined bending and torsion and (ii) dissimilar metal pipe weld joints having circumferential through-wall crack in the weld were used. The dissimilar metal pipe weld joint is made up of SA312 Type 304LN austenitic stainless steel and SA508 Gr. 3 Cl. 1 low alloy carbon steel and joined by Nickel-rich Inconel alloy weld. About 75% of the mixed experimental data has been used for development of model and the remaining data for validation. For the development of ANN model, (i) back propagation technique (ii) sigmoidal transfer functions and (iii) Levenberg-Marquardt algorithm are used. The maximum percentage difference observed between the predicted and the experimental results for crack depth and remaining life was 19.59% and 15.78% respectively. The efficiency of the developed model was verified using several statistical parameters. The developed models are useful for the structural integrity assessment of piping components under different loading scenarios.

Investigation of the steady state subcooled boiling regime in the hot subchannel of a VVER-1200 reactor core: a CFD analysis

A Computational Fluid Dynamics (CFD) analysis is carried out on a three-dimensional subchannel representing the most intensive fuel assembly to simulate the thermal-hydraulic performance of the VVER-1200 hot channel under steady-state operation. It is found that, subcooled boiling is predicted for both the rated and the conservative operational parameters with the intensity of bubble generation found to be higher for the conservative scenario compared with the rated one. The predicted boiling phenomenon at the cladding surface may result in the formation of deposits, and pits, and may impair the strength of the fuel elements, and increase the inhomogeneity in fuel burnup. Therefore; a thorough investigation of the predicted boiling regime is required to shed light on the development of this phenomenon in a typical reactor subchannel using CFD tools. The CFD work conducted in this work is based on the Eulerian multiphase model in ANSYS in conjunction with the RPI wall boiling model. The developed model has been validated against other one-dimensional models found in the literature. The variation of mass-averaged quantities (across the channel) along the subchannel generally agrees with the 1D models, which builds confidence in the modeling approach. Contours showing the spatial variations of temperatures, vapor void ratio, as well as heat fluxes are presented. Profiles of the different components of heat flux during boiling are presented. It has been found that the heat flux variations during boiling pass through three distinct regimes. In the first, a steady increase in quenching and evaporation heat flux is observed, while convective heat flux declines. In the second regime, the quenching and evaporation heat fluxes plateau with a slight decrease, while the convective heat flux becomes zero. This signifies that the outer clad surface is largely covered with vapor bubbles and the only heat transfer mechanisms are due to quenching and evaporation. During the last regime, quenching and evaporation heat fluxes decline due to the decline of the imposed heat flux at the clad surface, and hence convective heat transfer starts to increase. In addition, profiles of the mass-averaged coolant temperature, void fraction, and outer clad surface temperatures along the hot element underrated and conservative operating conditions are also generated.

Performance of Artificial Neural Network Compared to Multi-Linear Regression in Prediction of Countermovement Jump Height

Previous research has used primarily linear regression models to predict jump height and establish contributors of performance. The purpose of this study was to compare the performance of artificial neural network (ANN) and multi-linear regression (MLR) in prediction of countermovement jump (CMJ) height and investigating the contribution of kinematic variables to CMJ performance. Thirty-four healthy young male athletes performed a total of 204 CMJ while eight kinematic variables (the hip, knee, and ankle angles at the begging of the concentric phase of CMJ, the hip and knee take-off angles, and the shoulder, hip, and knee maximum angular velocities) were used as inputs to ANN and MLR to predict CMJ height. The correlation coefficients between the jump height and the predicted value by the developed models indicated that ANN predict CMJ height better than MLR (R2 = 0.68 compared to R2 = 0.44). Moreover, the root mean squared error of prediction showed better performance of the ANN rather than the MLR (4.8 cm compared to 5.3 cm). The shoulder and hip maximum angular velocities were the most important contributors, and then the hip and knee take-off angles contributed to CMJ height. In conclusion, implementing ANN to identify key variables of performance may also be relevant for other sport skills.

A model for sustainable development: Advancing digital food retailing

The technological revolution has driven food retail towards self-service technologies (SSTs). The supermarket concept thrived due to consumer convenience and direct food access. This qualitative study explored the modernization of food retail through new technologies, focusing on Canadian stakeholders’ perceptions. Using a grounded theory approach, it aimed to create a sustainable digital food system model addressing social and moral food access aspects. Findings revealed that digitalization in food retail is driven by economic motives and stakeholders’ desire for collaboration and partnership to support food access.

Evaluation of Various Experimental Models in Antihypertensive Drug Development: Recent Update

Evaluation of Various Experimental Models in Antihypertensive Drug Development: Recent Update