Project Model Research Articles

Model sensitivity of Tibetan Plateau surface potential vorticity and the Asian summer monsoon system to Asian orographic perturbation in FGOALS-f2

The thermodynamic forcing of the Tibetan Plateau (TP) is important for the regulation of the Asian summer monsoon (ASM). However, the monsoon responses to orographic perturbation simulations show controversial results in previously published literature. One reason for this is that the monsoon responses to orographic forcing differ among climate models. Another reason is that the quantitative changes in the TP thermodynamic forcing remain unknown when the orography is modified. Therefore, the relationship between the TP forcing and the ASM may be different among climate models. In this paper, the surface potential vorticity (SPV) is used to quantify the surface thermodynamic forcing on the TP in the FGOALS-f2 climate model for both the standard Atmospheric Model Intercomparison Project (AMIP) and Coupled Model Intercomparison Project (CMIP) experiments with a modified orography, and various aspects of the ASM responses are also examined. Finally, their relationships with TP-SPV changes are quantitively estimated. The results indicate that the intensity of TP-SPV is reduced by nearly 100% in the AMIP runs, while it is reduced by 68% in the CMIP runs when the Asian mountains are removed. Overall, the responses of the monsoon system are more sensitive when air-sea interactions are considered. When the mountains are removed, the precipitation over the southern slope of the TP decreases by 73% and increases by nearly 30% over the tropical Indian Ocean in the CMIP runs. Moreover, the precipitation responses exhibit the largest difference between the AMIP and CMIP runs over the East Asian and Western Pacific regions. The precipitation over the Western Pacific remains almost unchanged when the orography is removed in the AMIP runs but is reduced by 20% in the CMIP runs. We propose that the calculation of SPV over the TP is helpful for understanding the TP orographic forcing and monsoon dynamics in the future.

DEVELOPMENT OF LEARNING TOOLS USING THE MISSOURI MATHEMATICS PROJECT MODEL ASSISTED BY GEOGEBRA TO IMPROVE THE MATHEMATICAL REPRESENTATION ABILITIES OF STUDENTS

The aims of this study were: 1) to determine the validity, effectiveness, and practicality of the Missouri Mathematics Project model assisted by Geogebra; and 2) to know students' mathematical representation abilities after using the Missouri Mathematics Project model assisted by Geogebra. The research model used is the Thiagarajan 3D Development model. The sample of this research was 32 students of class X TITL 2 of SMK Negeri 2 Medan. This research produced mathematics learning tools in the form of lesson plans, modules, media (Geogebra), initial ability tests (pretest), final ability tests (posttest), student needs questionnaires, and student response questionnaires. Based on the assessment of material experts (validator I), media experts (validator II), and student experts, a validity level of 94%. Based on the results of the N-Gain test on the Pretest and Posttest, the learning tools developed are categorized as effective and have an effectiveness level of 82,25%. Based on the results of student response questionnaire calculations, the practicality of the learning tools developed reached 85.34%. In addition, this study proves that there is an increase in the learning model of the Missouri Mathematics Project assisted by Geogebra on students' mathematical representation abilities carried out at SMK Negeri 2 Medan.

Economic consequences of climate change impacts on South Asian agriculture: A computable general equilibrium analysis

AbstractSouth Asia is one of the most vulnerable regions of the globe in terms of climate change, with agriculture the most affected economic sector of the region. This study employs an environmental Global Trade Analysis Project model to simulate the impact of an average global 2°C temperature increase by 2050, with a focus on South Asian countries. The economic costs of climate change in relation to crop productivity losses due to increasing temperature, land productivity losses caused by rising sea levels and heat stress‐induced agricultural labour productivity losses are assessed based on model simulations. The results show that the unfavourable climate change impacts on agricultural productivity (crop, land and labour) will reduce food production and create upward pressure on food prices. This will lead to a reduction in food consumption at the household level, threatening future food security in the region. The results further predict a contraction in all South Asian economies by 2050, due to adverse climate change impacts on the agricultural sector. In addition, out of the three climate change damage factors considered, labour productivity causes the greatest economic losses, while land productivity losses caused by rising sea levels impact the least. The study also found that low‐income countries would suffer most severely due to the impacts of climate change on the agricultural sector, while high‐income countries would be impacted the least.

Enhancing Strategic Planning of Projects: Selecting the Right Product Development Methodology

The selection of an appropriate development methodology is a critical strategic decision when managing a New Product Development (NPD) project. However, accurately estimating project duration based on the chosen methodology remains a challenge. This paper addresses the limitations of existing models and proposes a novel NPD project model that allows for testing and evaluation of different product development strategies. The model considers Waterfall, Spiral, Agile, and Hybrid methodologies and provides system engineers and project managers with decision-making tools to determine the optimal strategy and understand associated tradeoffs. The model is validated using real projects from various organizations and methodologies. It incorporates stochastic variables, risk management, and dynamic resource allocation, while addressing both Waterfall and Agile methodologies. The study contributes to the body of knowledge by offering practical tools for system engineers and project managers for choosing development methodology, improving project duration estimation, and identifying critical processes and risks in NPD projects. The research results also provide a basis for further studies and can benefit researchers interested in systems engineering methodologies. The proposed model fills a gap in the literature by providing a validated NPD model to evaluate the impact of different product development methodologies on project duration.

Friction or cooperation? Boosting the global economy and fighting climate change in the post-pandemic era

In the post-COVID-19 pandemic era, the world faces a choice between trade protection and cooperation. However, current literature provides very little information on the benefit or loss of trade friction or cooperation on global economy and climate mitigation. This study applied the Global Trade Analysis Project model to assess the impacts of trade friction on global economic recovery and climate change. The results indicated that international trade friction can both delay global economic recovery and affect CO2 emission reduction. The shocks of consumption reduction and production suspension have a higher marginal effect in developing and emerging economies, whereas trade friction has a higher effect on developed countries. Trade friction has more negative economic effects for developed countries, but developing countries cannot reduce CO2 emissions proportionally with the decrease in trade and related production. In the post-pandemic era, if the global trade barrier increases, the world may face a co-occurring economic decline and an increase in or low abatement of carbon emissions.

A Vertically Integrated Project Approach to Ethnographic Methods Training

The Vertically Integrated Project (VIP) model, developed in engineering, is a methodological approach that integrates undergraduate students, graduate students and faculty in projects for both research and student training. Recent applications of this model report that it could foster student-faculty collaboration, empower senior students to mentor juniors, transcend disciplinary silos that often hinder research, and promote diversity and inclusivity in higher education. Our cross-disciplinary experimentation with this model in ethnographic methods training demonstrates its value to facilitate or even institutionalize faculty-student collaborative research and mutual learning in social sciences. We also suggest transforming this model to be less hierarchical in collaboration, more mutually beneficial for all involved, and more sustainable for long-term projects.

Analytic hierarchy process (AHP) assessment of Kuwait mega construction projects' complexity

PurposeMegaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.Design/methodology/approachThe study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.FindingsIt was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.Originality/valueThe contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

Tomato Plant Leaf Disease Detection Using Convolutional Neural Network

Our country is growing country, India is mostly depended on the agriculture field, and Agriculture is pillar of our country’s economy. Because of this India has to improve in agriculture filed, in many of situations’ farmer face difficulties to detect crop disease in a case large area farm, or late in detection of crop disease in such cases farmer lose their all crop and face the huge loss, to avoid this, in agricultural field is detection of plant diseases is very important. And it is very difficult tasks. In regular normal procedure it requires a more time period and trained person to detect accurate plant disease. In this paper, we proposed effective way for plant disease detection using computer science and machine learning model. Disease transmission from unhealthy plant to all other healthy plants in farm is one of the major damages to crop farm. And these diseases spread like forest fire and have the possible to impact the whole operation if not identified in early on. Now Plant disease detection methods helps to identify infected plants in a very early stage and also help us to identify plant disease in a wide range of area of crops in a cost-effective manner. The aim of this project model is to implement machine learning models, in our proposed system we take a plant leaf image on that leaf images we predict the plant disease using Convolution Neural Networks (CNN) model, in that we build a such a model to predict the plant disease with maximum accuracy and it is for plant disease detection for tomato plant. This machine learning model is analyzing different image metrics pixels data to determine the best performance of network. For that dataset were used around 7around 8016 images we going to use to train the model. We going to use 14 layers CNN models to get better accuracy results. Model consist various layers like convolution, pooling, flatten and dense. Early two layers that is preprocessing and augmentation of images Finally, we get the result of which disease that plant have.

Changes in the Global Water Exchange by the Results of Historical Experiments on Climate Models under CMIP-6 Project

Long-term (up to 165 years) series of globally averaged values of the main water exchange components, obtained as the result of historic experiments on several dozen (from 34 to 41) climate models of CMIP-6 project, are analyzed. The examined characteristics include variations of evaporation from ocean surface, precipitation over the ocean, effective evaporation from the ocean (total horizontal moisture transfer in the atmosphere from the ocean to land), and total model river runoff from the continents. It is shown that the model precipitation over the ocean effectively filters out the monotonic positive trend in evaporation from the ocean and, therefore, increases the stationarity of the total chain of global water exchange, including long-term changes in the global river runoff.

Comparative analysis of the anti-radicalism value project model and the conventional model in improving citizen commitment

This research aims to compare the results of two learning approaches, namely the anti-radicalism value project model and the conventional model, in enhancing the commitment of young citizens. Through data analysis from two groups of classes, the experimental and control groups, we evaluated the outcomes of both learning models on the improvement of young citizens' commitment, including their knowledge, attitudes, and behaviors towards the values and principles of Indonesia's constitutional democracy. By using a combination of qualitative and quantitative research methods, it is hoped that research findings can be reliable. The study shows that the anti-radicalism value project model has significant advantages in increasing the commitment of young citizens compared to the conventional model. Students participating in the project model demonstrated active engagement in the learning process, meaningful learning experiences, and better development of critical thinking skills. On the other hand, the conventional model tends to be limited in providing opportunities for interaction and the active participation of students in the learning process. Students involved in the conventional model are more inclined to be passive recipients of information and less engaged in the learning process. Furthermore, the conventional model also falls short in encouraging students to think critically and relate the subject matter to a real-life context.

Radiatively Active Hydrometeor Frequencies From CloudSat–CALIPSO Data for Evaluating Cloud Fraction in Global Climate Models

AbstractThis study derives radiatively active hydrometeor frequencies (HFs) from CloudSat–CALIPSO satellite data to evaluate modeled cloud fraction in recent historical period simulations by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models. Most CMIP5 models do not consider precipitating and convective core hydrometeors in radiative transfer calculation except for CESM1‐CAM5 with precipitating ice (snow). They do not provide radiatively active cloud fractions for all precipitating hydrometeor types such as snow fraction while the satellite‐retrieved HFs include mixed profiles of stratiform clouds and precipitation as well as convective cores. A filtering method is applied to produce estimates of nonprecipitating and nonconvective core HF (NPCHF) from the total HF (THF) excluding rain, which is defined here as the sum of NPCHF, precipitating ice (snow, graupel, and hail), and convective core HF. We determine the optimal reference HF data for model comparison that include estimates of liquid‐phase NPCHF from CloudSat radar‐only data (2B‐CWC) and ice‐phase THF from CloudSat–CALIPSO 2C‐ICE combined radar/lidar data. The zonal‐mean at selected heights and regionally averaged profiles of CMIP5 multi‐model‐mean cloud fraction show good agreements in comparison with NPCHF estimates with biases within 5%, whereas cloud fraction is significantly underestimated (up to 30%) against the THF estimates. The biases appear below the midtroposphere over the extratropics, in the upper troposphere over the midlatitude lands and a few tropical convective regions, where precipitating ice and convective core hydrometeors contribute greatly. This comparison reveals some underestimates of thick cloud HFs resulting from measurement limitations due probably to attenuation of radar/lidar signals, which cannot be qualified in this study.

Use of computer programming for the development of a CAD-CAE system on the Revit platform applied in urban drainage projects

Urban storm drainage emerged with the evolution of society in search of improvements in health and comfort going through different phases and today it connects with a sustainable and technological bias. Through Python programming, it is possible to integrate a Computer Aided Design/Computer Aided Engineering (CAD/CAE) system into the Revit platform using the Dynamo tool as an intermediary. This work presents the development and validation of a computational tool for dimensioning urban drainage coupled to the graphical project modeling platform Revit. The results obtained with the numerical code point to a good agreement when compared with those generated by conventional tools, using a calculator and an Excel spreadsheet. It shows that, due to the direct and automatic communication between drawing and dimensioning is possible to reduce the time spent filling out spreadsheets and facilitate analyses such as minimum inclinations of the pipe through warnings and factors such as the ratio of the water sheet and diameter through color scales.

Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum.

Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.

Catalyst: Fast and flexible modeling of reaction networks.

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst's broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

Modelling labs for a course on differential equations

Differential equations are widely used tools for modelling the world around us, making a course in differential equations a natural place for students to connect concrete mathematical applications to abstract concepts. Since students grasp the concepts better by applying them, introducing differential equations through modelling becomes essential. However, students often struggle to create and understand these models in the course. The purpose of this article is to share and examine a scaffolded sequence of labs implemented at regular intervals throughout a semester intended to train students in construction and analysis of mathematical models using differential equations. In these labs students learn basic principles of modelling that better prepare them for many of the high-quality modelling projects already available in differential equations. The course in which these activities were developed is taught using the SIMIODE text, Differential Equations: A Toolbox for Modelling the World, by Kurt Bryan. The labs are split into two components, with the first completed during class time and the second as out of class assignments. Furthermore, the course was re-organised to allow for the time needed to introduce the labs in class. We include classroom materials for labs, analysis, results on student perceptions and pedagogical recommendations.

Observed changes in wet days and dry spells over the IGAD region of eastern Africa

Changes in wet and dry patterns have an impact on rain-fed agriculture, crop productivity, and food security in Eastern Africa. The purpose of this research is to look into the changes in wet days and dry periods within the Intergovernmental Authority on Development (IGAD) region. Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) and Multi Models Ensembles (MME) of 10 historical simulations and projections from Coupled Model Intercomparison Project (CMIP6) models were employed as the data source. Several statistical approaches, as well as wet and dry spell thresholds, were used to calculate patterns of change in wet and dry spells on a decadal (10-year), 20, 30, and 41-year time scale. The results show the region exhibits decrease a decrease in the number of wet days and protracted dry spells in the 1980s, followed by an extraordinary (exceptional) increase in wet days in the subsequent decades (2011–2020) during March–May (MAM), June–September (JJAS), and October-December (OND). In Kenya, Somalia, southeastern Ethiopia, Eritrea, and Djibouti, the probability of surpassing 7, 14, 21, 28 days (1, 2, 3, 4 spells) was less than 5%. Furthermore, floods in 1997, 2018, 2019, and 2020, as well as droughts in 1983, 1984, 1985, and 2021, were triggered by an increase or decrease in the number of wet days and dry spells over most of the region. The number of wet days is projected to decrease by 10–20% during the MAM season across Sudan, South Sudan, and central and northern Ethiopia, JJAS is projected to increase by 30–50% across central and northern Sudan. However, during the OND season, increases are projected over Uganda, Ethiopia, and Kenya under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios. These findings contributed to the advancement of scientific knowledge in the IGAD region, as well as decision-making, food security, and the development of adaptation and mitigation strategies. We encourage rain-fed agriculture, crop variety planning, and irrigation supplement.

The Southern Ocean carbon and climate observations and modeling (SOCCOM) project: A review

The Southern Ocean serves as the primary gateway through which the intermediate, deep, and bottom waters of the ocean interact with the surface ocean (and thus the atmosphere), and it has a profound influence on the oceanic uptake of anthropogenic carbon and heat as well as nutrient resupply from the abyss to the surface. Yet it has been the least observed and understood region of the world ocean. The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project was implemented in 2014 with a goal to help remedy this deficit in observations and understanding. The SOCCOM project is based on two major advances that have the potential to transform understanding of the Southern Ocean. The first is the development of new biogeochemical sensors mounted on autonomous profiling floats that allow sampling of ocean biogeochemistry in 3-dimensional space. Floats may detect processes with a temporal resolution that ranges from hours to years. The second is that the climate modeling community finally has the computational resources and physical understanding to develop fully coupled climate models that can represent crucial, mesoscale processes in the Southern Ocean, as well as corresponding models that assimilate observations to produce a state estimate. The observational component, based on deployment of profiling floats with oxygen, nitrate, pH and bio-optical sensors, is generating vast amounts of new biogeochemical data that provide a year-round view of the Southern Ocean from the surface to 2000 m. The modeling effort is applying these observations and enhancing our understanding of the current ocean, and reducing uncertainty in projections of future carbon and nutrient cycles and climate. After nine years of operation, including a project renewal in the sixth year, the SOCCOM project has deployed more than 260 profiling floats. These floats have collected over 27,000 vertical profiles throughout the Southern Ocean. A data-assimilating biogeochemical state estimate model has been implemented. Here, the design of the SOCCOM project is reviewed and the scientific results that have been obtained are described. The project’s capability to help meet the observing system priorities outlined for a notional UN Decade for Ocean Sciences Southern Ocean observing system is assessed.

The Influence of the Connected Mathematics Project Model on Mathematics Learning on Students’ Mathematics Learning on Students’ Mathematical Reasoning Abilities at SMAN 2 Rumbio Jaya

This research was motivated by the problem of low mathematical reasoning ability of students in mathematics learning in grade XI SMAN 2 Rumbio Jaya. One solution to overcome this problem is to use the connected mathematics project (CMP) learning model. The purpose of this study was to improve the mathematical reasoning ability of high school students. This type of research is quasi-experimental research or pseudo-experiments involving two classes that are given different treatments, namely the experimental class and the control class. The research subjects consisted of class XI where class XI IPS1 was an experimental class with a total of 19 people and class XI IPS2 in the form of a control class with a total of 21 people. The sample technique uses simple random sampling. Before being given experimental class treatment and control class is first given a test, namely pretest, and after being given experimental class treatment and control class given posttest test. The results of this study, students' mathematical reasoning ability in the control class was found to be on average in the control class of 76.19 while in the experimental class it was found to be an average of 83.00. So that the Connected Mathematics Project model has a significant effect on the mathematical reasoning ability of grade XI students of SMA Negeri 2 Rumbio Jaya. This is evidenced by an independent t-test, where the sig value is 0.000 < 0.05.

Global Temperature Projections from a Statistical Energy Balance Model Using Multiple Sources of Historical Data

Abstract This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement We develop a statistical model to understand Earth’s energy balance and how it impacts temperature across the planet’s surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model’s parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data.

ANALISIS KEVALIDAN PERANGKAT PEMBELAJARAN BERBASIS MODEL MISSOURI MATHEMATICS PROJECT UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH DAN MOTIVASI SISWA SD

This study aims to analyze the validity and find learning tools based on the Missouri Mathematics Project (MMP) model that are practical and effective in improving elementary school students' problem-solving skills and motivation. The research was carried out at SD Negeri 014647 Mekar Tanjung, Asahan Regency. The research population was all fifth grade students and the sample was determined as many as 60 students. The research instrument consisted of expert assessment sheets, motivational questionnaires and tests of students' mathematical problem solving abilities. The analysis technique uses expert validation and calculates Gain and N-gain with student test results. The results of the study stated that the mathematics learning tool based on the MMP model developed was valid for use in the implementation of learning in class V SD Negeri 014647 Mekar Tanjung, Asahan Regency. The MMP model-based mathematics learning tool is practically used in the implementation of learning in class V SD Negeri 014647 Mekar Tanjung, Asahan Regency. Suggestions for this research include that the learning tools developed need to be tested on other schools so that the scope and quality of this model can be met and to find out the advantages and disadvantages of the product because this research only reached stage 3, namely development has not yet reached the deployment stage (decimate).