Development and Validation of Knowledge Assessment Scales for Dementia and Urinary Incontinence in Community Older People.

To develop and validate scales for reliably assessing dementia and urinary incontinence knowledge of older adults in the community. Items were generated through a literature review, refined through a Delphi study (n = 19), and then revised through a pilot study (n = 29). Item analysis and exploratory factor analysis were applied to finalize the scales (n = 244). Construct validity, reliability, and acceptability were evaluated (n = 243). The two knowledge assessment scales for dementia and urinary incontinence, respectively, comprised 12 items and 8 items. Model fit indicators of both met the criteria of confirmatory factor analysis. Cronbach's α were .82 and .70, respectively. Completion ratio and completion time of the two scales was 83.51% and 4.22 ± 1.90 minutes. The knowledge assessment scales for dementia and urinary incontinence with satisfactory validity, reliability, and acceptability, could be served as valid tools for disease prevention and management among older adults in the community.

Rapid authenticity assessment of PGI Hongyuan yak milk based on SICRIT-QTOF MS.

Hongyuan (HY) yaks live in a pollution-free environment, making HY yak milk a green food, but their short milk production period and low milk yield make yak milk precious and expensive. The phenomenon of counterfeiting HY yak milk with ordinary milk from other origins has already occurred, so the authenticity assessment of HY yak milk is necessary. This study developed a rapid soft ionisation by chemical reaction in transfer quadrupole time-of-flight mass spectrometry (SICRIT-QTOF MS) for HY yak milk differences assessment. Principal component analysis and orthogonal least squares discriminant analysis showed differences between HY milk and the other three origins. Twenty-eight differential compounds were screened out by variable importance in projection, fold change, P-value, and database matching. Furthermore, six characteristic compounds (proline, 2-hydroxy-3-methylbutyric acid, and l-isoleucine, etc.) of HY samples were putatively identified. The study demonstrated that SICRIT-QTOF MS has great potential for rapidly distinguishing the milk origin.

Development and validation of frailty and malnutrition knowledge assessment scale for community-dwelling older adults.

There is a lack of reliable tools to assess the knowledge of frailty and malnutrition in community-dwelling older adults. To develop and validate reliable frailty and malnutrition knowledge assessment scales for this population, two scales were developed and validated through five phases. Phase 1: the item pools were constructed through a literature review and research panel based on the symptom interpretation model. Phase 2: the expert consultation was performed to select the items. Phase 3: a pilot survey was conducted to assess the clarity of the items and further revise the scales. Phase 4: 242 older adults were surveyed to finalize the items. Phase 5: 241 older adults were surveyed to test the psychometric properties. The two scales each comprise 3 dimensions (symptoms, risk factors, and management strategies) and 11 items. They had good construct validity, with all indicators of correlation analysis and confirmatory factor analysis meeting their specific criteria. The reliability of the frailty and malnutrition knowledge assessment scales was good, with composite reliability coefficients all >0.60, Cronbach's alpha being 0.81 and 0.83, and the Spearman-Brown coefficient being 0.74 and 0.80, respectively. Their acceptability was good, with both having a completion rate of 92.18% and an average completion time of 3 min. The two scales are reliable tools to assess the knowledge of frailty and malnutrition among community-dwelling older adults, especially for large-scale surveys. They can help identify knowledge gaps in older adults and provide a basis for developing targeted educational interventions.

Collaborative optimal allocation of water resources and sewage discharge rights in watershed cities: considering equity among water sectors.

Water supply systems in watershed cities face challenges due to increasing water demand and arbitrary sewage discharge allocations. Previous studies have primarily focused on water resource allocation and sewage discharge rights, neglecting the intricate interactions between the two. This study introduces a novel approach by integrating sewage discharge rights into the watershed's water resource allocation mechanism. A multi-objective optimization model was developed, employing the Gini coefficient to balance the equitable and economic aspects across various water sectors. This model takes into account the distinct water demands and sewage discharge requirements of different sectors. The findings of this study are as follows: (a) the Gini coefficients for water demand allocation and sewage discharge rights allocation exhibit simultaneous optimization and display consistent trends; (b) when the importance of sewage discharge relative to other water users increases, the return on investment for domestic and industrial water use decreases, but the fairness of water distribution improves; (c) proper allocation of sewage discharge rights can effectively enhance the economic value of agricultural water use. Overall, this strategy has the potential to enhance both the equality and economic benefits of the water supply system while ensuring the sustainable utilization of water and sewage rights in the basin cities.

Development and validation of knowledge assessment scales on sarcopenia and fall for Chinese community-dwelling older adults.

AIM: This study aimed to develop and validate sarcopenia and fall knowledge assessment scales for community-dwelling older adults. METHODS: A five-phase, systematic and standardized process was used. Phase 1: item pools were constructed based on the Symptom Interpretation Model. Phase 2: the Delphi expert consultation was carried out for items selection and revision. Phase 3: a pilot survey was carried out to further select and revise the items. Phase 4: older adults were surveyed to finalize the items. Phase 5: older adults were surveyed to test the psychometric properties of the two developed scales, including construct validity, reliability and acceptability. RESULTS: Both scales comprise three dimensions (symptom, risk factor and management strategy), with 10 items for the sarcopenia knowledge assessment scale and 14 items for the fall knowledge assessment scale. They had acceptable construct validity, with all indicators meeting their specific criteria. Their reliability was acceptable, with the Cronbach's α coefficients being 0.82 for both scales, the value of spilt-half reliability being 0.86 for the sarcopenia knowledge assessment scale and 0.85 for the fall knowledge assessment scale. Their acceptability was good, with both scales having a completion rate of 94.35% and an average completion time of 5 min. DISCUSSION: Two Chinese knowledge assessment scales with acceptable validity, reliability and acceptability have been developed, which will facilitate the assessment of the knowledge on sarcopenia and fall among community-dwelling older adults, especially for large-scale surveys. Geriatr Gerontol Int 2023; 23: 430-436.

Unstable decoupling of CO2 emissions from sectoral economic growth calls for decarbonization policies based on multi-perspective accounting: a case study of Zhejiang, China.

Decoupling CO2 emissions from economic growth is an important cornerstone of a country's decarbonization policy. Existing research in this avenue has mainly focused on decoupling the production-based (PB) CO2 emissions from economic growth at the national level, while decoupling the consumption-based (CB) and income-based (IB) CO2 emissions from economic growth, especially at the sectoral level, has received less attention. Using China's Zhejiang province as a case study, we conduct one of the first studies focused on decoupling the PB, CB, and IB CO2 emissions from economic growth at the sectoral level. Our results reveal that (1) during 2002-2017, the sectoral decoupling level varies greatly among the three different perspectives; (2) most of the examined sectors were in unstable decoupling states while some sectors even reverted to coupling states; and (3) the drivers of CO2 emissions at the sectoral level reveal important factors, such as emissions intensity, production structure, and final demand structure, that contribute toward decarbonization. Our study demonstrates to policymakers how utilizing a multi-perspective evaluation of the decoupling of emissions from sectoral economic growth can improve the accuracy of decarbonization policies and identify critical sectors toward CO2 reduction objectives.

Dynamic Prediction of Internet Financial Market Based on Deep Learning.

P2P lending is an important part of Internet finance, which is popular among users because of its efficiency, low cost, wide range, and ease of operation. The problem of predicting loan defaults is affected by many factors, such as the linear and nonlinear nature of the data itself and time dependence and multiple external factors, which have not been well captured in the previous work. In this paper, we propose a multiattention mechanism to capture the different effects of various time slices and various external factors on the results, introduce ARIMA and LSTM to capture the linear and nonlinear characteristics of the lending data respectively, and establish a Time Series Multiattention Prediction Model (MAT-ALSTM) based on LSTM and ARIMA. This paper uses the Lending Club dataset from the United States to prove that our model is superior to ANN, SVM, LSTM, GRU, and ARIMA models in the prediction effect of MAE, RMSE, and DA.

1-Year Outcomes of Blinded Physiological Assessment of Residual Ischemia After Successful PCI: DEFINE PCI Trial.

OBJECTIVES: The aim of this study was to identify the post-percutaneous coronary intervention (PCI) target value of instantaneous wave-free ratio (iFR) that would best discriminate clinical events at 1 year in the DEFINE PCI (Physiologic Assessment of Coronary Stenosis Following PCI) study. BACKGROUND: The impact of residual ischemia detected by iFR post-PCI on clinical and symptom-related outcomes is unknown. METHODS: Blinded iFR pull back was performed after successful stent implantation in 500 patients. The primary endpoint was the rate of residual ischemia, defined as iFR ≤0.89, after operator-assessed angiographically successful PCI. Secondary endpoints included clinical events at 1 year and change in Seattle Angina Questionnaire angina frequency (SAQ-AF) score during follow-up. RESULTS: As reported, 24.0% of patients had residual ischemia (iFR ≤0.89) after successful PCI, with 81.6% of cases attributable to angiographically inapparent focal lesions. Post-PCI iFR ≥0.95 (present in 182 cases [39%]) was associated with a significant reduction in the composite of cardiac death, spontaneous myocardial infarction, or clinically driven target vessel revascularization compared with post-PCI iFR <0.95 (1.8% vs 5.7%; P = 0.04). Baseline SAQ-AF score was 73.3 ± 22.8. For highly symptomatic patients (baseline SAQ-AF score ≤60), SAQ-AF score increased by ≥10 points more frequently in patients with versus without post-PCI iFR ≥0.95 (100.0% vs 88.5%; P = 0.01). CONCLUSIONS: In DEFINE PCI, despite angiographically successful PCI, highly symptomatic patients at baseline without residual ischemia by post-PCI iFR had greater reductions in anginal symptoms at 1 year compared with patients with residual ischemia. Achieving post-PCI iFR ≥0.95 was also associated with improved 1-year event-free survival. (Physiologic Assessment of Coronary Stenosis Following PCI [DEFINE PCI]; NCT03084367).

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine.

With the national goal of "carbon peak by 2030 and carbon neutral by 2060 in China", studies on carbon prices of China's Emissions Trading System (ETS) pilots have shown growing interest in the related fields. Carbon price fluctuations reflect the scarcity of carbon resources, and accurate prediction can improve carbon asset management capabilities. Therefore, in order to clarify the dynamics of carbon markets and assign carbon emissions allocation rationally, we propose a hybrid feature-driven forecasting model with the framework of decomposition-reconstruction-prediction-ensemble. In this paper, the non-stationary, nonlinear and chaotic characteristics of carbon prices in China's ETS pilots have been verified, and then the prediction model is built based on the tested features. Firstly, the original carbon price series are decomposed by Variational Mode Decomposition (VMD), and then reconstructed by Sample Entropy (SE). Next, Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) is conducted to predict the subsequences. Lastly, the forecasting series of every subseries are summed to obtain the final results. The empirical results based on carbon prices of China's ETS pilots proved that the proposed model performs more efficiently than the current benchmark models. As carbon prices are expected to increase across all ETS during the post-COVID-19 recovery stage, the new prediction model will be useful for improving the guiding principles of the existing government policies including the likely introductions of Border Carbon Adjustment (BCA) in the EU and the US, and governing the large global public companies to deliver their "net zero" commitments.

Socio-economic drivers of rising CO2 emissions at the sectoral and sub-regional levels in the Yangtze River Economic Belt.

As the world's largest inland shipping channel, the Yangtze River Economic Belt (YREB) is strategic to China's sustainable development where policymakers are increasingly emphasizing not only this region's economic development but also its CO2 emission reduction targets. To achieve emission targets in the YREB region, it is essential to identify the driving forces of its CO2 emissions. However, existing studies are not very refined and only examine the overall effects of drivers on CO2 emission changes, while neglecting the sub-regional and sectoral level effects across China. More refined research, therefore, will provide better-targeted policies for emission reduction relevant to regional levels such as the YREB region. Towards this end, this paper integrates the methods of structural decomposition analysis and attribution analysis to demonstrate the driving forces, at both sub-regional and sectoral levels, for YREB's emission changes from 2002 to 2012. Our results reveal the following: (1) Jiangsu Province has always been the main regional source of increasing CO2 emissions in the YREB, accounting for more than 20% of total CO2 emission growth. (2) The Electricity and Heat Production and Supply sector is responsible for most of the increases in CO2 emissions, both in 2002-2007 (609.8 Mt, 54.8%) and 2007-2012 (287.6 Mt, 34%). (3) During the period of 2007-2012, changes in per capita final demand were the primary driving force for the increases in CO2 emissions, while changes in CO2 emission intensity were the largest driving force for decreasing CO2 emissions, respectively accounting for 179.9% and -119.4% of total emission changes in the YREB region. (4) Moreover, the effect of emission intensity mainly exists in the Electricity and Heat Production and Supply sector in the Jiangsu and Zhejiang provinces, accounting for respectively, 10% and 10.4% of the total effects across all economic sectors. Considering the diverse impacts of driving forces in different sub-regions and economic sectors, policymakers should apply more refined measures to utilize varying driving forces in different sub-regions and economic sectors towards sustainable development.