Volatile guest molecule mediated strategy to convert covalent organic framework into nitrogen, sulfur-doped carbon as metal-free oxygen reduction electrocatalysts.

Covalent organic framework (COF) derived metal-free carbon materials have emerged as promising electrocatalysts for the oxygen reduction reaction (ORR). Herein, a volatile guest molecule mediated-pyrolysis strategy was explored on a designed thiophene-rich and imine-linked COF. Through the modulation of guest mediators (iodine and sulfur), the properties of the as-obtained carbon materials can be well regulated. The optimized nitrogen and sulfur dual-doped carbon electrocatalyst demonstrates remarkable ORR activity with a half-wave potential of 0.87 V and impressive durability, with only an 8% current loss over 21 h. The corresponding assembled zinc-air battery has a comparable power density (60 mW cm-2) to that of the commercial Pt/C. It is proposed that the coexistence of the guest mediators iodine and sulfur in the channels of COFs could prevent the loss of N species. The enhanced N content and N/S ratio are assumed to be responsible for the ORR performance. This study puts forward a novel strategy to prepare COF-derived carbon materials mediated by volatile guest molecules, which may provide new insights into the development of metal-free ORR catalysts.

Toward parallel intelligence: An interdisciplinary solution for complex systems.

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in the artificial systems, computational experiments, and parallel execution (ACP) approach has been developed. The method cultivates a cycle termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

An Analysis of Music Perception Skills on Crowdsourcing Platforms.

Music content annotation campaigns are common on paid crowdsourcing platforms. Crowd workers are expected to annotate complex music artifacts, a task often demanding specialized skills and expertise, thus selecting the right participants is crucial for campaign success. However, there is a general lack of deeper understanding of the distribution of musical skills, and especially auditory perception skills, in the worker population. To address this knowledge gap, we conducted a user study (N = 200) on Prolific and Amazon Mechanical Turk. We asked crowd workers to indicate their musical sophistication through a questionnaire and assessed their music perception skills through an audio-based skill test. The goal of this work is to better understand the extent to which crowd workers possess higher perceptions skills, beyond their own musical education level and self reported abilities. Our study shows that untrained crowd workers can possess high perception skills on the music elements of melody, tuning, accent, and tempo; skills that can be useful in a plethora of annotation tasks in the music domain.

Strategy evaluation and optimization with an artificial society toward a Pareto optimum.

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty, unreliable predictions, and poor decision-making. To address this problem, we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models. The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs. As an example, by modeling coronavirus 2019 mitigation, we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data. Our work suggests that a nation's intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments. Our solution has been validated for epidemic control, and it can be generalized to other urban issues as well.

Seeking medical services among rural empty-nest elderly in China: a qualitative study.

BACKGROUND: The number of empty-nest elderly in China is rapidly increasing. Empty-nest elderly could not receive adequate daily care, economic support and spiritual consolation from their children. Rural empty-nest elderly are facing more serious health challenges than those in urban areas. OBJECTIVE: This study aimed to understand the experiences of rural empty-nest elderly in seeking medical services in China. METHODS: The method of inductive content analysis was used to collect and analyze data. Data were collected by in-depth interviews. A total number of 16 participants were involved in this study. A semi-structured interview guideline, which was discussed in depth and agreed upon by all researchers, was used to encourage participants to talk about their experiences in seeking medical services. RESULTS: Rural empty-nest elderly is facing a great challenge in seeking medical services in China. There are some barriers for rural nest elderly to get access to healthcare services, such as low-income status, high expenditure of medical treatment and inadequate health insurance coverage. Due to the absence of the companionship of their adult children, empty-nest elderly have to rely on their neighbors and relatives to seek medical services. CONCLUSIONS: Rural empty-nest elderly have great difficulty in seeking medical services in China. More efforts should be made to get medical services more accessible to rural empty-nest elderly.

Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection.

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.

Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e., the back propagation (BP) network and support vector regression (SVR) with different input selections (i.e., original monitoring parameters and integrated Gaussian parameters) are proposed in this paper. To compare the performances of presented ML models in field cases, these models are evaluated using the Prairie Grass and Indianapolis field data sets. The influence of the training set scale on the performances of ML models is analyzed as well. Results demonstrate that the integrated Gaussian parameters indeed improve the prediction accuracy in the Prairie Grass case. However, they do not make much difference in the Indianapolis case due to their inadaptability to the complex terrain conditions. In addition, it can be summarized that the SVR shows better generalization ability with relatively small training sets, but tends to under-fit the training data. In contrast, the BP network has a stronger fitting ability, but sometimes suffers from an over-fitting problem. As a result, the model and input selection presented in this paper will be of great help to environmental and public health protection in real applications.

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries.

Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker's parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker's observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants.

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster.

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

Playing Chemical Plant Environmental Protection Games with Historical Monitoring Data.

The chemical industry is very important for the world economy and this industrial sector represents a substantial income source for developing countries. However, existing regulations on controlling atmospheric pollutants, and the enforcement of these regulations, often are insufficient in such countries. As a result, the deterioration of surrounding ecosystems and a quality decrease of the atmospheric environment can be observed. Previous works in this domain fail to generate executable and pragmatic solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations (i.e., daily management plans) for inspection agencies. First, so-called Stackelberg Security Games (SSGs) in conjunction with source estimation methods are applied into this research. Second, high-accuracy air quality monitoring stations as well as gas sensor modules are modeled in the CPEP game. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP game. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work in daily practice. Results show that playing a CPEP game can reduce operational costs of high-accuracy air quality monitoring stations. Moreover, evidence suggests that playing the game leads to more compliance from the chemical plants towards the inspection agencies. Therefore, the CPEP game is able to assist the environmental protection authorities in daily management work and reduce the potential risks of gaseous pollutants dispersion incidents.