Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving.

Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software - Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to their daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts.

Can China's carbon generalized system of preferences reduce urban residents' carbon emissions? Evidence from a quasi-natural experiment.

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010-2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10-2Mt in 2010-85,241.20 ×10-2Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10-2Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10-2, 26,600.17 ×10-2, 17,081.34 ×10-2 and 9393.36 ×10-2Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted.

Food Waste and Lunar Phases: Evidence from a Randomized Controlled Trial.

To examine a potential correlation between food waste and lunar phases, we have devised a randomized controlled trial. The experiment spanned from 31 March to 10 July 2022, during which we employed the direct weighing method to collect 1903 valid data points on food waste. Utilizing propensity score matching, we meticulously controlled for various factors, including dining dates, the number of diners, dining times, spending levels, and store activities. The study revealed a close relationship between lunar phases and food waste. During the new moon phase, there was an increase in both orders and waste generated by consumers. Specifically, individuals, on average, squandered an additional 6.27% of animal protein (0.79 g), 24.5% of plant protein (1.26 g), 60.95% of starchy foods (3.86 g), and 61.09% of vegetables (5.12 g), resulting in an aggregate food waste of 32.14% (10.79 g). Conversely, during the full moon phase, consumers decreased their orders and subsequently decreased food waste. On average, individuals wasted 44.65% less animal protein (5.76 g), 43.36% less plant protein (2.5 g), 85.39% less seafood (0.73 g), and 8.43% less vegetables (0.93 g), resulting in a 20.52% (7.81 g) reduction in food waste. Furthermore, we validated our conclusions through various validation methods, including model replacement, to ensure robustness and reliability.

Smartphone-based low-cost and rapid quantitative detection of urinary creatinine with the Tyndall effect.

This research aims to develop a robust and quantitative method for measuring creatinine levels by harnessing the enhanced Tyndall effect (TE) phenomenon. The envisioned sensing assay is designed for practical deployment in resource-limited settings or homes, where access to advanced laboratory facilities is limited. Its primary objective is to enable regular and convenient monitoring of renal healthcare, particularly in cases involving elevated creatinine levels. The creatinine sensing strategy is achieved based on the aggregation of gold nanoparticles (AuNPs) triggered via the direct crosslinking reaction between creatinine and AuNPs, where an inexpensive laser pointer was used as a handheld light source and a smartphone as a portable device to record the TE phenomenon enhanced by the creatinine-induced aggregation of AuNPs. After evaluation and optimization of parameters such as AuNP concentrations and TE measurement time, the subsequent proof-of-concept experiments demonstrated that the average gray value change of TE images was linearly related to the logarithm of creatinine concentrations in the range of 1-50 µM, with a limit of detection of 0.084 µM. Meanwhile, our proposed creatinine sensing platform exhibited highly selective detection in complex matrix environments. Our approach offers a straightforward, cost-effective, and portable means of creatinine detection, presenting an encouraging signal readout mechanism suitable for point-of-care (POC) applications. The utilization of this assay as a POC solution exhibits potential for expediting timely interventions and enhancing healthcare outcomes among individuals with renal health issues.

The association of coagulation indicators with in-hospital acute kidney injury and malignant events of patients with acute aortic dissection: a retrospective cohort study.

Background: Acute kidney injury (AKI) is a prevalent complication of acute aortic dissection (AAD) and is associated with poor outcomes. The onset of AAD may result in endothelial injury due to the formation of the false lumen, which can activate the coagulation pathway and lead to coagulation dysfunction. It serves as a valuable diagnostic and prognostic marker for AAD, but also plays a role in the pathological mechanisms underlying AKI. We aimed to investigate the potential value of coagulation indicators at admission for assessing in-hospital AKI and malignant events after AAD. Methods: We identified patients with AAD admitted to the First Affiliated Hospital of Shantou University Medical College from January 2015 to October 2020 and divided them into two groups according to coagulation function. Univariable and multivariable analyses were used to analyze the association between coagulation indicators and AKI and malignant events in patients with AAD. Chi-squared or Fisher exact test and receiver operating characteristic (ROC) curve analysis was conducted to assess the value of coagulation indicators in predicting in-hospital AKI and malignant events. Results: A total of 487 patients were enrolled in this study, including 309 cases with normal coagulation. After the multivariable adjustment, the incidence of in-hospital AKI in the abnormal coagulation group was significantly higher [model 1: 2.061 (1.214-3.501), P=0.007; model 2: 1.833 (1.058-3.177), P=0.031; model 3: 1.836 (1.048-3.216), P=0.034]. The incidence of malignant events was higher in the abnormal prothrombin time (PT) group [model 1: 4.283 (0.983-18.665), P=0.053; model 2: 7.342 (1.467-36.749), P=0.015; model 3: 6.996 (1.377-35.537), P=0.019]. Chi-squared and Fisher exact test showed that PT and abnormal coagulation score (ACS) were statistically different among the AKI groups and malignant event groups. Under ROC analysis, coagulation indicators were helpful to predict AKI (AUC =0.668; P<0.001). Conclusions: Our study confirmed the presence of coagulation dysfunction is associated with an increased risk of AKI and malignant events. It suggested the severity of coagulation dysfunction is positively correlated with the incidence of in-hospital AKI in AAD patients. These results highlight the importance of considering coagulation dysfunction as a potential mechanism underlying AKI and malignant events after AAD.

Keeping pace with innovations in data visualizations: A commentary for mathematics education in times of crisis.

The mathematical medium of data visualization and other data representations (DV) has served as a primary means of communicating about the COVID-19 crisis. DVs about the pandemic are highly visible across news journalism and include an increasingly innovative and diverse set of representational forms. These representational forms employ multimodal, interactive, and narrative elements, among others, that create new possibilities for data storytelling. Building on current efforts to expand the teaching and learning of data practices in K-12 mathematics education, we argue that innovative DVs create new opportunities for teaching and learning mathematics, particularly during times of crisis. We illustrate our argument using three examples of innovative DVs from news journalism. We discuss how these DVs could serve as complementary resources alongside conventional graphs to support students as they use mathematics and mathematical representations to make sense of crises such as the COVID-19 pandemic. Our commentary seeks to bring current trends in data representation to bear in mathematics education. Leveraging such trends offers artifacts useful for teaching and opens up space for elevating emotion and experience as important aspects of mathematics curricula.

Evaluation of Occupational Health and Safety Management of Listed Companies in China's Energy Industry Based on the Combined Weight-Cloud Model: From the Perspective of FPE Information Disclosure.

Protecting labor safety and health and actively carrying out occupational safety and health management (OSHM) is a common need worldwide, and it is also one of the important efforts of Chinese enterprises under the background of promoting the implementation of the Healthy China strategy. Based on in-depth thinking on the current stage of OHSM, this study incorporated "management framework, management process, management effectiveness" (FPE) into an integrated framework and constructed an FPE evaluation system for enterprise OHSM. This study innovatively collected and refined FPE information from the perspective of information disclosure and used the combined weight cloud model to evaluate the occupational health and safety management level (OHSML) of 69 listed companies in China's energy industry from 2009-2019. The results showed the following. (1) The OHSML of most listed companies in China's energy industry was still at a low-end level. Among the companies that have issued relevant information reports, only 5.58% (S = 30) of the sample companies' OHSML were at an acceptable level (Level IV) or declarable level (Level V). The OHSML comprehensive evaluation level of 92.56% (S = 498) of the sample companies was between the transitional level (Level III) and the improved level (Level II). (2) During 2009-2019, although the annual OHSML of listed companies in China's energy industry showed an upward trend, the growth rate was low, and even the OHSML of some listed companies in the energy industry showed the characteristics of reduced fluctuations. (3) From the perspective of the PFT three-dimensional subsystem level of OHSM, the evaluation level of the governance framework subsystem was the highest, whereas the evaluation level of the management process subsystem and the management effectiveness subsystem were relatively low. Finally, according to the relevant results, some suggestions were proposed to improve the OHSML of listed companies in China's energy industry. These findings can provide guidance for companies to improve their OSHM performance.

Myocardial strain analysis of echocardiography based on deep learning.

Background: Strain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos. Methods: Three-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively. Results: The DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias -1.2 ± 1.5%. Conclusion: In conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts.