Optimization Design of Large-Aperture Primary Mirror for a Space Remote Camera.

Lightweight, high stability, and high-temperature adaptability are the primary considerations when designing the primary mirror of a micro/nano satellite remote sensing camera. In this paper, the optimized design and experimental verification of the large-aperture primary mirror of the space camera with a diameter of Φ610 mm is carried out. First, the design performance index of the primary mirror was determined according to the coaxial tri-reflective optical imaging system. Then, SiC, with excellent comprehensive performance, was selected as the primary mirror material. The initial structural parameters of the primary mirror were obtained using the traditional empirical design method. Due to the improvement of SiC material casting complex structure reflector technology level, the initial structure of the primary mirror was improved by integrating the flange with the primary mirror body design. The support force acts directly on the flange, changing the transmission path of the traditional back plate support force, and has the advantage that the primary mirror surface shape accuracy can be maintained for a long time when subjected to shock, vibration, and temperature changes. Then, a parametric optimization algorithm based on the mathematical method of compromise programming was used to optimize the design of the initial structural parameters of the improved primary mirror and the flexible hinge, and finite element simulation was conducted on the optimally designed primary mirror assembly. Simulation results show that the root mean square (RMS) surface error is less than λ/50 (λ = 632.8 nm) under gravity, 4 °C temperature rise, and 0.01 mm assembly error. The mass of the primary mirror is 8.66 kg. The maximum displacement of the primary mirror assembly is less than 10 µm, and the maximum inclination angle is less than 5″. The fundamental frequency is 203.74 Hz. Finally, after the primary mirror assembly was precision manufactured and assembled, the surface shape accuracy of the primary mirror was tested by ZYGO interferometer, and the test value was 0.02 λ. The vibration test of the primary mirror assembly was conducted at a fundamental frequency of 208.25 Hz. This simulation and experimental results show that the optimized design of the primary mirror assembly meets the design requirements of the space camera.

Performance evaluation of CMIP6 climate models for selecting a suitable GCM for future precipitation at different places of Tamil Nadu.

Climate change refers to long-term variations in climate parameters. Future climate information can be projected using a GCM (General Circulation Model). Identifying a particular GCM is crucial for climate impact studies. Researchers are perplexed about selecting a suitable GCM for downscaling to predict future climate parameters. Recent updates to CMIP6 global climate models have included shared socioeconomic pathways based on the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report (AR6). The performance of 24 CMIP6 GCMs in precipitation with a multi-model ensemble filter was compared to IMD (India Meteorological Department) 0.25 × 0.25 degrees rainfall data in Tamil Nadu. The performance was evaluated with the help of Compromise Programming (CP), which involves metrics such as R2 (Pearson correlation co-efficient), PBIAS (Percentage Bias), NRMSE (Normalized Root Mean Square Error), and NSE (Nash-Sutcliffe Efficiency). The GCM ranking was performed through Compromise programming by comparing the IMD data and GCM data. The results of the CP analyses of the statistical metrics suggest that the suitable GCMs for the North-East monsoon are CESM2 for Chennai, CAN-ESM5 for Vellore, MIROC6 for Salem, BCC-CSM2-MR for Thiruvannamalai, MPI-ESM-1-2-HAM for Erode, MPI-ESM1-2-LR for Tiruppur, MPI-ESM1-2-LR for Trichy, MPI-ESM1-2-LR for Pondicherry, MPI-ESM1-2-LR for Dindigul, CNRM-CM6-HR for Thanjavur, MPI-ESM1-2-LR for Thirunelveli and UKESM1-0-LL for Thoothukudi. The appropriate suitable GCMs for South-West monsoon as CESM2 is appropriate for Chennai, IPSL-CM6A-LR for Vellore, CESM2-WACCM-FV2 for Salem, CAMS-CSM1-0 for Thiruvannamalai, MPI-ESM-1-2-HR for Erode, MPI-ESM-1-2-HR for Tiruppur, EC- EARTH3 for Trichy, EC- EARTH3 for Pondicherry, MPI-ESM-1-2-HR for Dindigul, CESM2-FV2 for Thanjavur, ACCESS-CM2 for Thirunelveli and ACCESS-CM2 for Thoothukudi respectively. This study emphasizes the importance of selecting an appropriate GCM. Selecting a suitable GCM will be useful in climate change impact studies and there by suggesting necessary adaptation and mitigation strategies.

A composite indicator to measure sustainable water use in Portugal: A compromise programming approach.

Clean water is an important resource for maintaining human life, economic activities, and ecosystems' survival. Nevertheless, its irregular distribution and occasional scarcity lead to the need to promote its sustainable use. To assess the current situation and the dynamics of sustainable water use, it is crucial to identify the main factors affecting it and to propose monitoring indicators. This paper develops an approach based on compromise programming to analyse water use sustainability at the municipal level, with a methodology that comprise a framework designed in five steps: 1 - indicators' choice; 2 - indicators's weights; 3 - definition of sustainability rankings with the application of a compromise programming approach; 4- application of a GIS analysis; 5 - identification of the main factors affecting sustainable water use. As a first result, the consensus weights of the chosen indicators were defined, indicating that the most important internal factors affecting sustainable water use are safe water, the percentage of housing served by water supply and water distributed by inhabitant. Then sustainability rankings at the municipality level were defined considering these factors. Finally, it was possible to conclude that tourism activity, income level, and young age population have a significant negative effect on sustainable water use, and municipal revenue has a positive effect. Irrigated farming shows a non-significant negative effect on sustainable water use. Population density, elderly population and education level did not show the expected effects on sustainable water use.

Suitability of different precipitation data sources for hydrological analysis: a study from Western Ghats, India.

For hydrological analysis, it is essential to have continuous and long-term precipitation data. However, the precipitation data from rain gauge stations are often insufficient and not continuous. At present, ground-based gridded data and satellite-based gridded data are often used as an alternative. However, these data sets have to be evaluated for their suitability in hydrological studies. The current study compared three different rainfall data sources with the observed station data for the Kallada River basin of Kerala, India. The ground-based gridded rainfall data from the India Meteorological Department (IMD), the high-resolution satellite product Tropical Rainfall Measuring Mission (TRMM 3B43, version 7), and the reanalysis data Modern-Era Retrospective Analysis for Research and Applications (MERRA) are used in the analysis. The correlation coefficient, normalized root mean square error, Nash-Sutcliffe efficiency, modified index of agreement, and volumetric efficiency are used as performance indicators. The performance indicator's weights are based on the entropy method. The multi-criteria decision-making techniques like compromise programming and Preference Ranking Organization Method (PROMETHEE II) are used for ranking the precipitation data sources. It is found that IMD ground-based gridded data is ranked 1 among the three data sets. The IMD ground-based gridded data are not homogeneous based on the absolute homogeneity test, even though they had the highest rank. The IMD gridded data are further corrected based on double mass curve analysis. The corrected data were analyzed using the precipitation concentration index (PCI) to assess the temporal variation in precipitation, and it was found that the location falls under a uniform distribution zone.

Statistical downscaling of sea levels: application of multi-criteria analysis for selection of global climate models.

Sea level rise is one of the serious aftermaths of global warming on the hydrosphere. The scientific community often depends on global climate models (GCMs) for projection of future sea levels. Numerous GCMs are available; thus, selecting the most appropriate GCM/GCMs is a critical task to be performed prior to downscaling. In this study, multi-criteria decision-making (MCDM) techniques, namely, Preference Ranking Organisation Method of Enrichment Evaluation (PROMETHEE-II), Elimination Et Choice Translating Reality (ELECTRE-II), and compromise programming, were used to identify appropriate GCMs whose projections can be used to downscale sea level projections at Ernakulam, Kerala, India. Support vector machine was employed to statistically downscale the sea level projections from the projections of GCMs. Five statistical metrics, namely, correlation coefficient ([Formula: see text]), normalized root mean square error, absolute normalized average bias, mean absolute relative error, and skill score, were adopted in this study as the performance criteria. The weightage of each criterion was computed using the entropy method. Six GCMs (GISS-E2-H, CanESM2, ACCESS1-0, CNRM-CM5, GFDL-CM3, and CMCC-CM) were considered for the analysis based on the availability of predictors. GISS-E2-H, CanESM2, and ACCESS1-0 occupied the first three positions respectively in all three MCDM techniques.

Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem.

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations and shipper considerations as goals, and a common goal such as transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. We evaluated the effectiveness of the proposed algorithm with the Tabu Search algorithm and the original genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

Insight into the precipitation behavior of gridded precipitation data in the Sina basin.

This paper examines the performance of three gridded precipitation data sets, namely, Global Precipitation Climatology Centre (GPCC), Tropical Precipitation Measuring Mission (TRMM), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), for the duration of 25 years using 9 rain gauge data sets of the Sina basin, India. Statistical measures were employed to measure the performance in reproducing the rainfall and to assess its ability to detect the rainfall/no rainfall events, its structure, pattern, and spatio-temporal variations in the monthly and annual time scales. Compromise programming (CP) is used to rank the statistical performances of selected gridded precipitation data sets and found that TRMM attained first rank for the 8 stations followed by MERRA. The precipitation concentration index (PCI) checks the pattern and distribution of rainfall and found that observed data shows a uniform distribution in the basin; however, all the three gridded data sets failed to demonstrate uniform distribution. Categorical metrics like Probability of Detection (POD) and False Alarm Ratio (FAR) revealed that TRMM followed by MERRA and GPCC have good capabilities to detect rainfall/no rainfall events at different thresholds. All the trends drawn between observed data set and gridded precipitation data sets revealed that the MERRA data tend to underestimate and the TRMM and GPCC data tend to overestimate the values and intensities of rainfall data sets at most of the stations for both monthly and annual time scales. The data analysis of extreme rainfall points at monthly and annual time scales exhibits better performance of TRMM data sets. Overall, the TRMM data set is capable in replicating different characteristics of the observed data in the study area and could be used for hydro-meteorological and climatic studies when continuous observed data set is not available.

Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms.

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.

Some metaheuristic algorithms for solving multiple cross-functional team selection problems.

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Carbon emission reduction effect of transportation structure adjustment in China: an approach on multi-objective optimization model.

This study explores the carbon emission reduction effect of the transportation structure adjustment from the perspectives of transportation structure optimization and transportation mode shift. A multi-objective optimization model of China's transportation structure is constructed, and the transportation structure is then adjusted by combing the multi-objective particle swarm optimization (MOPSO) algorithm and compromise programming technique. Under the premise of ensuring the economic and social effects, the largest carbon emission reduction effect of the transportation structure is acquired. The results indicate that the transportation structure optimization reduces carbon emissions by 12.70%. Optimization in the western region has the largest carbon emission reduction effect, followed by that of the central and eastern regions. Furthermore, the carbon emission reduction effect of the shift mode "road to rail" increases uninhibitedly with the increase in the shift range. However, the same effect of "road to water" has an upper limit. "Road to rail" is found to be more effective in the eastern region, whereas "road to water" is more effective in the central and western regions.

Evaluation of Land Cover Change and Agricultural Protection Sites: A GIS and Remote Sensing Approach for Ho Chi Minh City, Vietnam.

Ho Chi Minh City (HCMC), economic center and most populous city of Vietnam faces a strong structural change since its market liberalization in the late 1980s. Big challenges occur in the form of uncontrolled urban sprawl due to rapid population growth with encroachment of agricultural land, which leads to environmental and climatic issues like urban heat island effects, air pollution and flooding events. Remote Sensing and Geographic Information Systems (GIS) provide new computer-based technologies for urban planners which can greatly ease the monitoring of agricultural loss as well as improve decision making for future land management. In the first part of this study, land cover change dynamics are thoroughly assessed using moderate and high spatial resolution satellite imagery (Landsat and SPOT) over the period 2010-2017 in 22 districts of HCMC. In the second part, the land cover classification results of 2017 provide the initial map for a GIS-based Multi-Criteria-Decision-Analysis (MCDA) of potential agricultural protection sites. Therefore, criteria of climate adaptation and ecological service are established and embedded in the GIS-compatible Compromise Programming Model (CP). With the use of Analytic Hierarchy Process (AHP) by Thomas L. Saaty and additional expert knowledge, appropriate weighting factors have been affiliated. The results show that agricultural land decreased by more than two thirds in the period considered. However, particularly the western rural districts Bình Chánh and Hóc Môn still offer a great amount of valuable agricultural land suitable for protection. The proposed method can serve as a scientific framework for planning departments of fast growing cities to zone agricultural land for protection on an early planning stage in order to ensure sustainable land use development in the future.