Evaluation of resources and environmental carrying capacity of marine ranching in China: An integrated life cycle assessment-emergy analysis.

Resources and environmental carrying capacity (RECC) describes the ability of a system to achieve healthy and sustainable development. Various marine ranching enterprises have emerged in China in recent years, which have aroused concern and debate about the RECC of marine ranching systems. By taking the environmental impact calculated by life cycle assessment (LCA) into consideration in emergy analysis (EA), this study evaluated the comprehensive RECC performance of the whole system and each stage of a marine ranching system in China. The resource use efficiency (RUE) and system carrying ratio (SCR) of the system were reasonably good. However, its environmental loading ratio (ELR), emergy yield ratio (EYR), and emergy sustainability index (ESI) were unsatisfactory. First, the nonrenewable resources dominated the emergy input. Second, the emergy input from the purchased resources was much greater than that of local resources. Third, the potential environmental impact mainly came from the construction stage. Fourth, serious overload of RECC was observed in the maintenance stage. The results indicate that the system is efficient, and its RECC is in a safe state, but the system has deficiencies in environmental protection and the exploitation and utilization of local resources. The proposed analysis framework helps us comprehensively understand the marine ranching RECC performance and provides a research paradigm reference for the sustainability analysis of other complex eco-economic systems.

Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine.

In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.

Extracting Medical Information from Paper COVID-19 Assessment Forms.

OBJECTIVE: This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. METHODS: An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. RESULTS: The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. CONCLUSION: Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.

Plasmon-coupled 3D porous hotspot architecture for super-sensitive quantitative SERS sensing of toxic substances on real sample surfaces.

This paper reports a facile, fast, and cost-effective method for the synthesis of three-dimensional (3D) porous AgNPs/Cu composites as SERS substrates for the super-sensitive and quantitative detection of food organic contaminations. Due to the 3D porous hotspot architecture and the strong plasmonic coupling between Ag and Cu, the porous AgNPs/Cu substrate achieves ultrasensitive detection of multiple analytes as low as 10-11 M (crystal violet, CV), 10-9 M (malachite green, MG), 10-11 M (acephate), and 10-9 M (thiram) even with a portable Raman device. Moreover, this 3D solid substrate has good signal uniformity (RSD < 11%) and superior stability (<14% signal loss), allowing for practical SERS detections. Importantly, by simply wiping the real sample surface using the substrate, it successfully detects CV and MG residues on crayfish, and the limit of detection (LOD) of CV and MG is determined to be 1.14 × 10-9 M and 0.94 × 10-7 M, respectively. Further, the substrate can also be applied to detect acephate on eggplant with a LOD of 1.41 × 10-9 M and thiram on an apple surface with a LOD of 1.04 × 10-7 M. Note that all these SERS detections on real samples have a broad dynamic concentration range and a good linear dependence. As a "proof of concept", multi-component detection on a real sample has also been demonstrated. This 3D solid substrate possesses excellent detection sensitivity, diversity, and accuracy, which allows rapid and reliable determination of toxic substances in foods.