Project Model Research Articles

Designing the routing level in microservice architectures on the Spring platform

The subject matter of research is the routing of requests in the microservice architecture. The goal of the article is to develop a target design concept for the level of request routing in the microservice architecture using the Spring technology stack as an example. Tasks: to analyse modern approaches to the structure of microservice architecture; programming of the routing entity and establishment of processes that ensure the routing of requests; programming stacks of Spring technologies that implement routing; design the routing layer of the application on the Spring platform. The following methods are used: analysis and synthesis to study technologies of interaction between services; abstraction and generalization to determine the structure of the microservice architecture, routing level, generalization of technologies that ensure interaction between services; modelling for the purpose of building a model of microservice architecture, highlighting the level of routing and connections with other structural models. The following results were obtained: the structure of the microservice architecture was investigated, in particular the level of routing; the role of design patterns that provide routing is defined: Service discovery, API Gateway, Load Balancer, etc.; the types of interprocess interaction (synchronous, asynchronous, hybrid) were analysed and the advantages and expediency of the application were determined; models of system fault tolerance are presented; a stack of technologies on the Spring platform is defined for the implementation of the routing layer; a model of a multi-level microservice architecture project was developed using the Spring technology stack, which implements the most effective solutions in the context of request routing. Conclusions: it is advisable to consider microservice architecture as a multi-level structure built on functional levels and connections between them; the level of microservices routing should include all processes related to the establishment of interprocess interaction, service detection, load balancing and fault tolerance, and the creation of a single entry point; Spring is a popular microservice architecture development tool platform that provides requirements for implementing request routing; the developed project model is an example of effective solutions for designing a multi-level architecture using the Spring technology stack in the context of request routing.

The “Turnkey School” project model for teacher team training

This study aims to present a new model of professional training for teachers in educational institutions within the framework of the "Turnkey School" project within the Master of Education degree program at the Humanitarian and Pedagogical Institute of Sevastopol State University (SevSU). This study used a controlled research design method to validate the team concept through a proposed model based on the socio-constructivist and contextual paradigms in education. The model involves training in a master's program as part of team building for a new educational institution in which cohort groups are formed based on the specific needs of assigned schools, which are then evaluated by an independent community of experts, as well as with the writing of a final thesis as a result of cohort team collaboration. This study confirms that the cohort group formed through this model shows a better level of maturity and functioning than randomly assigned teachers. These findings support the effectiveness of a Master of Education degree in improving the training and performance of future teachers. This model emphasizes not only the creation of updated educational products but also the self-implementation of those products, which contributes to the overall improvement of educational institutions.

A Mathematical Method for Estimating the Critical Slope Angle of Sheet Erosion

Estimating the critical slope angle (CSA) for sheet erosion is important for the precision estimation of sheet erosion and the development of erosion control practices. This study developed mathematical equations considering rainfall intensity and soil infiltration to efficiently estimate both instantaneous (at a given instant during rainfall) and cumulative CSAs, while also providing a valuable explanation for the change in CSA. The mathematical equations were consistent with observations from runoff plots (NSE = −1.01) of loess soils from Zhangjiakou (China) and simulation results (NSE = 0.96) from the Water Erosion Prediction Project model for a loam soil in Montana (USA). Estimated instantaneous CSA determined by the mathematical equations increased as the ratio of rainfall intensity to soil infiltration (I/f) increased, resulting in higher observed cumulative CSA after heavy versus normal rainfall events. Heavy rainfall, compacted soil, and varying rainfall duration affected the CSA by changing the I/f ratio. Maximum instantaneous CSA provided a better prediction of changes in soil erosion dynamics than that obtained from CSAs determined by field observations or experimental simulations. The mathematical equations illustrate the underlying physical mechanisms by which rainfall intensity and soil infiltration affect the CSA through changing the shear stress of overland flow. The results of this study provide critical information for guiding the development of effective soil erosion control strategies.

Teaching differential equations through modelling: hot water heater

We present an example of one of the modelling projects we assign to students in our differential equations classes. Students are asked to determine how to run a cost-efficient hot water heating system. We consider a cylindrical tank filled with water and heated by a heating element immersed in it. Together with students we discuss physical laws governing the process and make assumptions necessary to build a model. One of the goals of the modelling activity is to compute the time needed for water to reach the desired temperature given the power of the heating element and the size of the water tank. Several different scenarios can be discussed. Assuming the cost of heating the water is directly related to the time for which the power is ON, students can choose the most cost-efficient scenario. We provide the description of several modelling activities and obtained results.

SURVEILLANCE AND SECURITY MODELS BASED ON THE ARDUINO UNO INTERFACE

The issue demonstrated in the article covers construction of surveillance and security systems on Arduino UNO platform Technological and Software aspect which are based on Quality Semiconductor Sensor-MQ135; PIR Motion Sensor and use of blocking peculiarities. 
 The project model integration process in both a virtual and physical context is described in detail and analyzed in the paper. The following are specifically used: An Arduino UNO microcontroller board, the Arduino Software IDE (Integrated Development Environment), a local code editor on the PC, and the virtual online platform tinkercad.com. The work is supplemented by pertinent electronic diagrams, schemes, software code, descriptions of fundamental functionalities, links to virtual project simulation, real-world application, and links to video recordings of the experiment in order to demonstrate the project model's smooth operation.

Data Management for PalMod-II – A FAIR-Based Strategy for Data Handling in Large Climate Modeling Projects

Data Management for PalMod-II – A FAIR-Based Strategy for Data Handling in Large Climate Modeling Projects

Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents

Using machine learning and earth observation data to capture real-world variability in spatial predictive mapping depends on sample size, design, and spatial extent. Nonetheless, there is still ambiguity in answering some basic questions: a) How many samples are necessary for fitting the model? b) Which sampling techniques are suitable for modeling? c) Do results vary with changes in spatial extents? These questions are crucial for spatial modeling projects and require proper investigation. In the present study, we evaluated two sampling designs with different sample sizes, considering three nested spatial extents. Specifically, we adopted the conditioned Latin Hypercube Sampling and Simple Random Sampling designs. Based on this, a Random Forest model was used to predict Above-Ground forest Biomass at local, regional, and national spatial extents, comparing different sample sizes (n = 25, 50, 100, 200, 300, and 500). We defined one national extent, five regional extents within the national extent, and a local extent inside each regional extent. Each sampling design and size combination was tested 100 iterations. The results showed that there was no significant difference between the different sampling designs. The accuracy metrics showed marginal differences for 25 and 50 sample sizes, which were then reduced to minimal and provided similar results. However, a deeper analysis of all 100 repetitions exposed a noteworthy pattern: cLHS outperformed the SRS in terms of RMSE and variability. Regarding the sampling size, the R2 values increased with increasing sample size. Nevertheless, beyond a minimum of 300 to 500 samples, the improvement in accuracy became insignificant, emphasizing the diminishing returns with excessively large sample sizes. Moreover, increasing the size of the spatial extent reduced the accuracy of the model, possibly due to the effect of environmental factors or landscape nature. Therefore, this study demonstrates the potential impact of sample size, sampling design, and spatial extents on model accuracy and emphasizes the importance of reducing the sample size to reduce the model's complexity.

Predator exclosures increase nest success but reduce adult survival and increase dispersal distance of Piping Plovers, indicating exclosures should be used with caution

Abstract Diagnosing unsuccessful population outcomes for endangered species requires understanding relationships among vital rates, ecological conditions, and management variables, including unintended consequences of management actions. The federally threatened Piping Plover (Charadrius melodus) has remained below recovery goals, despite intensive management, including the use of nest exclosures to protect eggs from predation. We studied ecological factors and management actions affecting nest success, survival, site fidelity, and dispersal of the New Jersey, USA population of Piping Plovers over a 7-yr period, focused on evaluating the impact of exclosures on demography. While exclosures increased nest success by 62% over a 34-day period, exclosed nests were 4.7 times more likely to be abandoned, which was likely a consequence of adult mortality. Abandoned nests were associated with lower adult survival, particularly for males, but there was evidence that site fidelity was greater for birds whose last nest was exclosed vs. unexclosed. Regardless of exclosure status, females who abandoned their first nesting attempt dispersed 10 times farther between attempts than those whose first nest attempts were lost to other causes. Moreover, females that abandoned their last nesting attempts dispersed farther than females that lost their last nest to predation or flooding. This difference was more substantial for males. Our results corroborate studies documenting adverse impacts of exclosures on survival, and, for the first time, demonstrate that surviving mates (particularly females) emigrate from the breeding site, resulting in a realized loss of a local breeding pair. Further, we used an online population project model (i.e., PiperEx) to demonstrate that exclosures are not expected to improve growth rates in New Jersey. We conclude that there is a trade-off among increased nest survival, reduced adult survival and increased emigration rates, and we encourage managers to consider whether exclosures are worth the protection of eggs from predators using online decision support tools.

The 2022-like compound dry and hot extreme in the Northern Hemisphere: Extremeness, attribution, and projection

Droughts, hot extremes, and their combinations (i.e., compound dry and hot extremes, CDHEs) may pose threats to ecosystems and human society under global warming. The year 2022 has undoubtedly been a challenging year fueled by severe droughts, scorching hot days, and their concurrences in the Northern Hemisphere (NH), promoting the escalation of natural disasters. Though the analysis of individual droughts and hot extremes has been witnessed, the systematic assessment of CDHEs during summer 2022 remains largely underexplored. In this study, we used monthly precipitation and temperature data from the ERA5 covering the period of 1950–2022 to explore the extremeness of summer CDHEs across the NH. In addition, we explored the anthropogenic influences on the 2022-like CDHEs area and projected the likelihoods based on simulations from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) models. Results show that the spatial extent of CDHEs during summer 2022 across the NH reached the second highest in historical periods. Regional assessments show that the spatial extent of CDHEs in Europe was the largest since records began in 1950. Event attribution analysis demonstrates that the 2022-like CDHEs area during summer in the NH would have been virtually impossible without anthropogenic climate change. Future projection under different warming levels (1.5 °C, 2 °C, and 4 °C, respectively) indicates an increased probability of the 2022-like CDHEs area under SSP585. This study provides useful insights for understanding the extremeness, anthropogenic influence, and future likelihoods of CDHEs during summer 2022 in the NH.

ANALISIS MODEL PEMBELAJARAN PROJECT BASED LEARNING DALAM MENINGKATKAN KETERAMPILAN KOLABORASI PESERTA DIDIK MATA PELAJARAN MATEMATIKA KELAS 2 SD MUHMMADIYAH 03 ASSALAAM

In 21st century learning there are 4 skills that need to be developed, namely critical thinking, creativity, collaboration, and communication skills which are called 4C. Collaboration skills are one of the 21st century skills that are important to be instilled in students since they were still in low grade elementary school. This study aims to determine the effect of applying the project based learning model on the collaboration skills of students in mathematics class 2 SD Muhammadiyah 3 Assalaam. This research is a descriptive study with a qualitative approach using semi-structured interviews, observation sheets and documentation. The research subjects were students of class 2 SD Muhammadiyah 3 Assalaam. The results showed that most of the 2nd grade students had a high interest in participating in mathematics learning activities using the project based learning model. The majority of students agreed or strongly agreed that they enjoyed learning mathematics by using a project-based learning model and understood more about abstract mathematical material because one of the factors was being able to discuss with friends in a group so that it could be concluded that the application of the project model based learning improves the collaboration skills of students.

A study of Inter-Technology Information Management (ITIM) system for industry-education integration

The integration of big data technology in the manufacturing process has become a norm, and as society's dependence on the digital economy increases, colleges and universities must adjust their teaching methods to cater to their students' needs. In evaluating the success of business-school partnerships, there is a need for common criteria and visualising data analysis results. However, the current educational approach presents some challenges, including a lack of practical experience with software, overemphasis on theoretical concepts, and inadequate training in problem-oriented statistical modeling and big data statistics projects. Industry-education cooperation should be leveraged to enhance the implementation of big data technology and promote its overall development. This paper analyses the shortcomings of traditional talent training models in higher education and proposes incorporating industrial education to address the gaps. The paper aims to bridge the industry-education gap by developing and implementing an Inter-Technology Information Management (ITIM) system for quality education. The ITIM system uses a fuzzy algorithm to evaluate the quality of education and provides various intelligent functional modules, such as group management, financial management, and process-to-process communication. Compared to other integration models, the proposed management system offers superior performance with an industrial education performance accuracy of 98%, an average analysis, and calculation time of 20 ms and a maximum performance efficiency of 98%.By incorporating dynamic analysis of industry education, the experimental results of the talent training model have led to improvements in teaching effectiveness, student learning, and theoretical-applied teaching quality.

Hopf Bifurcation Analysis of a Continuous Investment Update Project Model

Some investment projects aim not only to produce goods but more importantly, to update and efficiently supply products in accordance with market demand. A double time delay differential dynamics model is formulated for continuous renewal investment projects based on the flowchart of the capital appreciation process and the assumed transfer functions. By analyzing the mathematical model, it can be determined that a unique local asymptotically stable positive equilibrium point exists for the continuous investment project. In accordance with the Hopf branching theorem, the model displays periodic behavior in proximity to its positive equilibrium point under certain conditions. The simulation results are compared under various conditions, and the validity of the relevant conclusions is confirmed.

통일과 안보 전략상 비무장지대(DMZ) 제도화에 관한 입법적 검토

The current Yoon Suk Yeol administration has clearly defined North Korea and strengthened cooperation between South Korea, the U.S., and Japan, but there is still room for inter-Korean summits due to peaceful unification and national security strategies. Therefore, in the long run, various strategies are needed to improve inter-Korean relations, and one of the issues that can be suggested is the institutionalization of the Demilitarized Zone (DMZ).
 The Demilitarized Zone should be converted from a military zone to a peace zone, developed into a clean ecological zone, and transformed into an inter-Korean joint cooperation project site that creates high added value. In addition, it is important to consistently convey and gain sympathy with North Korea by excluding any political considerations and using them together only from the perspective of economic and social benefits and environmental protection.
 In order to promote inter-Korean cooperation projects in the Demilitarized Zone, new normative systems such as new agreements and treaties that supplement and update the armistice agreement must be derived. South Korea may also consider forming a multilateral consultative body led by the United Nations, the United Nations Command, North Korea, and interested countries.
 Therefore, there is a fundamental limit to planning and implementing a master plan for the Demilitarized Zone and the border area unless compatible measures between legislation are taken suddenly under the current individual law system. Therefore, it is necessary to prepare an “institutional platform” so that all policies and discussions related to the Demilitarized Zone can be implemented consistently within the legal framework.
 Accordingly, an institutional platform for establishing cooperative projects is established through the enactment of the Special Act on Conservation and Sustainable Utilization of Environmental Resources in Demilitarized Zone. A practical project model is needed.

Gifted youths’ mathematical modelling-based project production: Structure, triggers and contributing factors

This study aims to determine gifted youths’ mathematical modelling project production competencies and structure, to examine the impact of a modelling-based project production and management program on these competencies and project production, and to identify the triggers and contributing factors of project production. Mixed method study was employed. The first study group consisted of 300 randomly selected gifted youths who were registered in the Project Production and Management Program (PPMP) in Science and Art Centers (SACs) throughout Türkiye. The second study group comprised 60 participants who were purposively selected from amongst the 300 participants that took part in the first study group. The data sources used in the study include scale, interview, log, modelling form, peer suggestion and expert suggestion forms. Structural equation modelling, ANCOVA, content analysis was conducted. It has been concluded that the mathematical modelling-based project production has a five-factor structure. It has been obtained that the modelling-based PPMP positively affected the mathematical modelling competencies and project productions and that this improvement was maintained. It has been found that identification of global, real-life and popular science problems in the field gifted youths are interested in is most important trigger. It was indicated there are 15 factors contributing to projects production.

Global Change in Agricultural Flash Drought over the 21st Century

Agricultural flash droughts are high-impact phenomena, characterized by rapid soil moisture dry down. The ensuing dry conditions can persist for weeks to months, with detrimental effects on natural ecosystems and crop cultivation. Increases in the frequency of these rare events in a future warmer climate would have significant societal impact. This study uses an ensemble of 10 Coupled Model Intercomparison Project (CMIP) models to investigate the projected change in agricultural flash drought during the 21st century. Comparison across geographical regions and climatic zones indicates that individual events are preceded by anomalously low relative humidity and precipitation, with long-term trends governed by changes in temperature, relative humidity, and soil moisture. As a result of these processes, the frequency of both upper-level and root-zone flash drought is projected to more than double in the mid- and high latitudes over the 21st century, with hot spots developing in the temperate regions of Europe, and humid regions of South America, Europe, and southern Africa.

The Control of Civil Engineering Projects Based on Deep Learning and Building Information Modeling

The aim of this study is to enhance the quality of civil engineering project management and optimize project control in order to ensure adequate construction resources and facilitate seamless project progression. By integrating building information modeling (BIM) technology with deep learning techniques, optimal control was examined at various stages of civil engineering project management. A simulation test was performed on a selected gymnasium engineering project, focusing on cost and resource control aspects. The findings revealed that, as the project advanced, the planned cost exceeded the actual cost by nearly 100,000 yuan in the final stage. The combination of BIM technology and deep learning model prediction substantially reduced the cost and material budgets of the engineering project. Data analysis showed that the average positioning error of the convolutional neural network algorithm for the project model was below 2%.

Systematic Validation of Ensemble Cloud‐Process Simulations Using Polarimetric Radar Observations and Simulator Over the NASA Wallops Flight Facility

AbstractThe BiLateral Operational Storm‐Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long‐term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the Wallops Flight Facility site via a bilateral approach of storm‐scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud‐process simulations through mixed‐phase, light‐rain, and deep‐convective precipitation cases. The framework consists of creating a domain‐shifted ensemble of large‐scale forcing data sets, and configuring and performing cloud‐process simulations with three different bulk microphysics schemes. Validation uses NASA S‐band dual‐POLarimetric radar observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble‐based validation framework demonstrates that (a) no particular forcing or microphysics scheme outperforms the rest and (b) the skill scores of coarse‐ and fine‐resolution ensemble simulations with different domain‐shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse‐resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations in support of aircraft and satellite instrument development as well as cloud‐precipitation‐convection parameterizations.

Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models

The warm sea surface temperature (SST) bias in the Southern Ocean (SO) has persisted in several generations of Coupled Model Inter-comparison Project (CMIP) models, yet the origins of such a bias remain controversial. Using the latest CMIP6 models, here we find that the warm SST bias in the SO features a zonally oriented non-uniform pattern mainly located between the northern and southern fronts of the Antarctic Circumpolar Current. This common bias is not likely to be caused by the biases in the surface heat flux or the strength of the Atlantic meridional overturning circulation (AMOC) — the two previously suggested sources of the SO bias based on CMIP5 models. Instead, it is linked to the robust common warm bias in the Northern Atlantic deep ocean through the AMOC transport as an adiabatic process. Our findings indicate that remote oceanic biases that are dynamically connected to the SO should be taken into account to reduce the SO SST bias in climate models.

Strategic digital city: Concept, model, and research cases

Cities, in addition to being physical, are also digital, requiring the use of information technology resources and strategies, information, and services to make appropriate decisions. The objective is to present the strategic digital city (SDC) concept and project model and its research considering the subprojects: city strategies; city information; public services to citizens; and information technology resources applied in cities. The research methodology comprises approaches mixing techniques in respective phases, including bibliometrics assignment, and model theory applied in SDC cases with more than 11 doctoral, 100 master’s, undergraduate, scientific initiation, and postdoctoral program orientations obtained from more than 300 cities since 2003. The results showed that the SDC can be understood as a social project of public policy and is on the city managers’ agenda, as demonstrated in the 8 cases researched and in the more than 86 publications in different scientific international journals in the recent decade. The conclusion reiterates the importance of the participatory and democratic SDC original project accepted to contribute to the proper city management and the citizens’ quality of life expansion, including infrastructure, policy, and city development. Then, the SDC contemporary concept and model have been maintained regularly over the 10 years.

Calibration and validation of hillslope runoff and soil loss outputs from the Water Erosion Prediction Project model in Minnesota agricultural watersheds

AbstractThere is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity (Kbe), interrill and rill erodibility (EIR and ER), and monthly precipitation standard deviations (Pstdev) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff (Ravg‐T), runoff ratios (RRT), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly Ravg‐T (0.64–0.98), RRT (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for Ravg‐T and RRT (±25%) and soil loss (±55%), except for RRT at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RRT at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season.