The implementation of the United Nations (UN) 2030 Agenda for Sustainable Development (2030 Agenda), with its 17 sustainable development goals (SDGs), faces challenges such as insufficient data, limited research methodologies, and uneven progress across regions. Earth observation (EO), particularly scientific satellites, offers unique advantages in supporting global sustainable development by providing objective, dynamic, and large-scale datasets for SDG evaluations and policymaking, as well as by facilitating the study of Earth’s environmental systems and their interactions with human activity. Sustainable Development Science Satellite 1 (SDGSAT-1), the world’s first scientific satellite dedicated to supporting the 2030 Agenda, was designed and developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS). Its three advanced EO sensors, i.e., Thermal Infrared Spectrometer (TIS), Glimmer Imager (GLI), and Multispectral Imager (MSI), furnish high-quality data, enabling continuous monitoring of human activity and environmental changes to bolster SDG-related research and global sustainability initiatives. As of November 2025, SDGSAT-1 has collected over 480 000 global terrain coverage images since its launch in November 2021. All its datasets have been shared free of charge with the Global Scientific Community through the SDGSAT-1 Open Science Program initiated in September 2022. The datasets have enabled researchers from more than 110 countries, 10 UN agencies, and various international organizations to publish over 180 scientific articles, 17 UN reports, and numerous public data products. These have demonstrated applications in urban development, disaster response, environmental monitoring, agriculture, and marine conservation. This article reviews the technical innovations and mission specifications of the SDGSAT-1 satellite, demonstrates its contribution in leveraging space technology for SDG monitoring and evaluation, and discusses the future evolution of development of EO systems, specifically the planned Sustainable Development Satellite Constellation, for supporting the global achievement of SDGs.

Human motion assessment (e.g., elderly motion assessment) refers to the process of systematically assessing and analyzing human motion posture, balance, and stability to uncover risks in motion, standardize human motion, and guide human motion. However, current assessment methods for human locomotor status are often based on joint angle kinematics analysis of skeletal joints and gait analysis based on gait parameters. These methods focus on a single type of parameter analysis, making it difficult to reflect both gait information and engineering risks simultaneously. This limits the ability to provide comprehensive motion status assessments. In this work, we focus on motion state assessment for the elderly. Through collaboration with domain experts, we identified three challenges: (a) integrating skeletal joint angles and gait parameters for motion state assessment analysis; (b) providing a correlated and intuitive visual representation of the changes in motion state parameters; and (c) offering customizable and configurable assessment criteria adaptable to different scenarios. To address these challenges, we propose MotionLens, an interactive visual analysis system designed to reveal the association between motion states, skeletal joint angles, and gait parameters. Our two case studies based on real datasets, along with expert interviews, demonstrate the effectiveness of MotionLens in assessing elderly motion states and identifying potential risks.

Nighttime light plays an important role in social economy and human activities by observing artificial light at night. Accurate radiometric calibration is the basis of quantitative applications and influences the accuracy of surface information revision. A nighttime light payload, named Glimmer Imager (GLI) is onboard the Sustainable Development Goals Satellite (SDGSAT-1). The payload can acquire nighttime light observations with a spatial resolution of 10 m in the panchromatic bands, and 40 m in Red/Green/Blue bands. In this paper, we proposed a vicarious radiometric calibration method for the GLI of SDGSAT-1 based on point light source, and conducted experiment in the Dunhuang radiometric calibration site. Atmospheric profile, aerosol optical depth (AOD), and total column water vapor content were measured during the in-situ measurements, and were used to simulate the at-sensor radiance at the entrance of satellite payload via MODerate resolution atmospheric TRANsmission (MOTRAN). Our result shows that there is a strong linear correlation between the simulated atsensor radiance and the DN value, with R2larger than 0.91. Moreover, uncertainties of the vicarious radiometric calibration induced by relative spectral response shift, AOD content measurements, atmospheric profile measurements, AOD type assumption, and specialized point light itself are also discussed. Results show that the overall uncertainty of vicarious radiometric calibration for nighttime light payload onboard SDGSAT-1 based on point light sources is between 4.00% and 5.50%. The comparison between GLI/SDGSAT-1 and DNB/VIIRS at Athens, Denver, and Milan shows a strong correlation with R2larger than 0.80. Our analysis reveals the feasibility of vicarious radiometric calibration method based on point light source for nighttime light payload.

In this paper, we propose a parallel non-convex approximation framework (NCAQ) for optimization problems where the objective is to minimize a convex function plus the sum of non-convex functions. Based on the structure of the objective function, our framework transforms the non-convex constraints to the logarithmic barrier function and approximates the non-convex problem by a parallel quadratic approximation scheme, which will allow the original problem to be solved by accelerated inexact gradient descent in the parallel environment. Moreover, we give a detailed convergence analysis for the proposed framework. The numerical experiments show that our framework outperforms the state-of-art approaches in terms of accuracy and computation time on the high dimension non-convex Rosenbrock test functions and the risk parity problems. In particular, we implement the proposed framework on CUDA, showing a more than 25 times speed-up ratio and removing the computational bottleneck for non-convex risk-parity portfolio design. Finally, we construct the high dimension risk parity portfolio in China’s stock market that consistently outperforms the equal weight portfolio.

It is agreed that portfolio selection models are of great importance for the financial market. In this article, a constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the trading restrictions and quantifies future security returns by fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile, it considers terminal wealth, conditional value at risk (CVaR), and skewness as tricriteria for decision making. Obviously, the proposed model is computationally challenging. This situation gets worse when investors are interested in a larger financial market since the data they need to analyze may constitute typical big data. Whereafter, a novel intelligent hybrid algorithm is devised to solve the presented model. In this algorithm, the uncertain objectives of the model are approximated by a simulated annealing resilient back propagation (SARPROP) neural network which is trained on the data provided by fuzzy random simulation. An improved imperialist competitive algorithm, named IFMOICA, is designed to search the solution space. The intelligent hybrid algorithm is compared with the one obtained by combining NSGA-II, SARPROP neural network, and fuzzy random simulation. The results demonstrate that the proposed algorithm significantly outperforms the compared one not only in the running time but also in the quality of obtained Pareto frontier. To improve the computational efficiency and handle the large scale securities data, the algorithm is parallelized using MPI. The conducted experiments illustrate that the parallel algorithm is scalable and can solve the model with the size of securities more than 400 in an acceptable time.

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to -0.01 K with an RMSE of 0.13 and -0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

In this paper, we describe a method for radiometric calibration of Fengyun-3D (FY-3D) MERSI-II TIR, bands 24 and 25 that combines vicarious and cross-calibration of the corresponding bands of Aqua MODIS, bands 31 and 32. A field campaign was conducted on lake Qinghai, China, on August 18, 2019. The surface measurements were performed before and after the passing time of Terra MODIS and FY-3D MERSI-II. Path radiance and transmittance were then calculated via the radiative transfer code MODTRAN 4.0, and the difference caused by the relative spectral response (RSR) of the sensors was eliminated by the spectral matching. Our experimental results indicate that the obtained radiometric calibration accuracy of FY-3D MERSI-II is stable during on-orbit operation.

Summary Current assessments of supercomputing (high‐performance computing) primarily focus on system performance. Quantitative methods to measure the impact of supercomputing in a broad context have not been well developed. In this paper, the basic meaning of supercomputing development is analyzed. An evaluation index system for assessing the development status of supercomputing is constructed innovatively, and the SuperComputing Development Index (SCDI) is proposed to measure supercomputing development status. SCDI is a composite index combining various indicators into one benchmark measure that monitors and compares supercomputing development in the past years. This appears to be the first attempt to quantitatively measure the supercomputing ecosystem. As an example, the SCDI of the Chinese Academy of Sciences is obtained, which is based on the data collected from 130 research groups about and covers the period from 2006 to 2012. The results have demonstrated that the proposed evaluation index system is objectively reasonable. The constructed SCDI provides a scientific method to quantitatively evaluate the development status of supercomputing for institutions or organizations.

Summary American option pricing is a high‐dimensional problem, and its computational challenges have attracted significant attention. We examine this problem using a stochastic mesh method enhanced with bias reduction within the classic Black–Scholes framework. We present Many Integrated Core (MIC) parallelization and acceleration techniques, which result in significant numerical acceleration for large‐scale simulations. In particular, we observe speed‐ups of 21‐fold and 28‐fold for CPU and MIC, respectively, over conventional means. Convergence performance is also examined. Copyright © 2014 John Wiley & Sons, Ltd.