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This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.

N‐heterocyclic carbene (NHC) organocatalysis has been developed as a powerful tool in modern synthetic chemistry. NHC catalytic activation of ynals and alkynoic acid derivatives provided versatile reactions that involve acetylenic Breslow and/or acylazolium as key intermediates, and diverse transformations have been established for access to molecules with unique skeletons in efficient fashions. Herein we summarize the recent achievements in NHC‐catalyzed reactions involving acetylenic Breslow and/or acylazolium intermediates. Different reactions belonging to three catalytic modes, including (1) conjugate additions to acetylenic Breslow derived α,β‐unsaturated acylazolium intermediates, (2) β‐umpolung of ynals via acetylenic Breslow intermediates, and (3) conjugate additions to acetylenic acylazolium intermediates, are emphasized with examples and plausible mechanisms cited to guide a better understanding. [ABSTRACT FROM AUTHOR]

In the case of a weak signal from a photon counting lidar and strong noise from the solar background, the signal is completely submerged by noise, potentially resulting in the appearance of multiple peaks in the denoising algorithm of photon counting entropy. Consequently, a clear distinction between the signal and noise may become challenging, leading to significant fluctuation in the ranging error. To solve this problem, this paper proposes an improved offset parameter optimization algorithm under the framework of photon counting entropy, aiming to effectively eliminate peak interference caused by noise and enhancing ranging accuracy. The algorithm includes two aspects. First, we introduce the solar irradiance prediction of an MLP network and least squares linear conversion to accurately estimate the noise rate of the solar background noise. Then, we propose the offset parameter optimization method to effectively mitigate the interference caused by noise. In simulation and experimental analyses, the ranging error of our proposed method is within 5 and 30 cm , respectively. Compared with the denoising method of photon counting entropy, the average ranging error is increased by 81.99% and 73.76%. Furthermore, compared to other anti-noise methods, it exhibits superior ranging capability. [ABSTRACT FROM AUTHOR]

Urban last-mile express delivery in China encounters several challenges. This paper presents the establishment of a sharing logistics center aimed at enhancing the overall efficiency of urban last-mile express delivery while optimizing the utilization of essential resources. The successful implementation of shared delivery within sharing logistics center necessitates the creation of a robust collaborative mechanism. Recognizing that cooperative benefit allocation is dynamically influenced by factors such as resource input, operational efficiency, risk management, and other cost-related considerations, this study introduces a multi-weight H-Shapley value method for benefit allocation. By conducting empirical analyses of urban last-mile express delivery in Beijing within a sharing logistics service framework, our findings reveal that the revised benefit allocation model better aligns with the interests of participating entities and positively correlates with their contributions. Analyzing the impact of delivery volume and express operational costs changes, it is found that when the delivery volume and express operational costs of the sharing logistics center change, the benefits of participating enterprises move in the same direction. The benefit allocation model established in this study enriches the existing body of research in the field of shared delivery and offers valuable insights for benefit allocation issues that necessitate consideration of the dynamic effects of multiple parameter variations. [ABSTRACT FROM AUTHOR]

Building operations account for a large amount of energy use and CO2 emissions, and the morphology of buildings in residential clusters strongly impacts energy efficiency performance. However, little research has focused on the morphology and energy electricity usage of high-rise residential clusters in hot summer and cold winter (HSCW) regions. We investigated 96 residential clusters in Hangzhou, China, and established a corresponding morphology database. Additionally, we obtained annual electricity consumption for 16 of these residential clusters. With this database, we performed optimization of morphological parameters upon energy use intensity (EUI) using a genetic algorithm (GA). Specifically, the cooling, heating, and lighting EUIs of high-rise residential clusters were studied. After implementing the optimized morphological parameters, there was a reduction of up to 7.73% in EUI. According to regression analysis, the average aspect ratio was the most significant factor influencing EUI (r = −0.907), followed by floor area ratio (r = −0.755), average orientation (r = 0.502), and average number of floors (r = −0.453). These results indicate that a higher intensity of land development with a greater floor area ratio, average aspect ratio, and average number of floors can reduce total energy consumption. Additionally, we found that an average building orientation of southwest 15° (with respect to south) is optimal. The findings of this study can assist urban planners and designers in developing more sustainable residential clusters, leading to decreased energy costs and CO2 emissions. [ABSTRACT FROM AUTHOR]

Background and Aims. The aim of this study was to investigate the association between serum phosphate levels and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods and Results. The study sample consisted of 1657 T2DM patients hospitalized between 2017 and 2019. Patients were categorized into quartiles based on their serum phosphate levels (Q1–Q4). An increasing trend in the prevalence of DR was observed across these quartiles. Subsequently, logistic regression analysis was employed to adjust for potential confounders, such as gender, age, BMI, and duration of diabetes, and to evaluate the odds ratios (ORs) associated with these quartiles. The prevalence of DR showed an increasing trend with elevated serum phosphate levels. Logistic regression further confirmed that serum phosphate levels remain an independent risk factor for DR. Conclusion. Elevated serum phosphate levels are closely associated with the prevalence of DR in hospitalized T2DM patients. Further studies are needed to establish causality. This trial is registered with chiCTR2000032374. [ABSTRACT FROM AUTHOR]

High-level ab initio calculations are conducted for studying the kinetics of three linear pentanol radicals generated through H-atom abstraction reactions. The species involved are optimized using the M06-2X/6-311++G(d,p) level of theory, while a relaxed scan at the M06-2X/6-31g level of theory with 10° increments is used for the hindrance potential for low-frequency torsional modes. Single-point energies for all stationary points are obtained through the QCISD(T) and MP2 methods in combination with cc-pVDZ, cc-pVTZ, and cc-pVQZ basis sets, which can be extrapolated to the complete basis set (CBS) limit. The rate constants and branching ratios for isomerization and decomposition reactions are computed over a temperature range of 250–2000 K and a pressure range of 0.01–100 atm. Isomerization reactions are dominant at low temperatures, while decomposition reactions are more dominant at high temperatures. The branching ratio of the isomerization reaction exhibits a slight decrease with increasing pressure, while the trend for decomposition reactions depends on the type of the breaking bond. Based on the calculations for five branched pentanol radicals in part I, kinetics of linear and branched pentanol radicals are compared in this work and the results reveal that, for the same kind of β-scission reaction at similar positions of linear and branched pentanol radicals, the rate constants of branched ones are faster than those of linear ones at low temperatures. The hydroxyl group adjacent to the breaking bond can increase the β-scission reaction rate constants, while the effect can be ignored when the hydroxyl group is not adjacent to the breaking bond. Moreover, compared to when the hydroxyl group is located in the middle of the carbon chain, its positioning at the chain's end yields a more noticeable impact on the products and rate constants of C–O bond and O–H bond β-scission reactions. Besides, when incorporating calculated rate constants into the CRECK model, the updated mechanism shows a better performance for ignition delay times of 1-pentanol in the NTC range but exhibits lower reactivity at higher temperatures. The simulation of speciation profiles also shows better agreement with the experimental data obtained using a flow reactor. [ABSTRACT FROM AUTHOR]

High-belite sulfoaluminate cement (HBSC), with low carbon dioxide emissions, can be used to repair old concrete. Calcium sulfate plays a significant role in hydration of ye'elimite, but a comprehensive study of the effects of the four different types and dosages of calcium sulfate on the hydration of HBSC is lacking. This study investigates the influence of different types (dihydrate, anhydrite, α-hemihydrate, and β-hemihydrate) and doses (2%, 4%, 6%, and 8%) of calcium sulfate on the hydration process and mechanical properties of HBSC. The findings indicate that the addition of calcium sulfate, specifically β-hemihydrate, had a significant impact on the reduction of setting times. The compressive strength of HBSC, particularly early strength, can be significantly increased by varying calcium sulfate dose and type, enhancing the performance of repair materials. The microstructure of ettringite is altered by different types of calcium sulfate, leading to varying pore structures and volume expansions. [ABSTRACT FROM AUTHOR]