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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.

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]

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]

In this work, we introduce a novel coherent perfect absorber, accentuating its novelty by emphasizing the broad bandwidth, reduced thickness, tunable property, and straightforward design achieved through the use of an asymmetric graphene metasurface. This design incorporates both square and circular graphene patches arranged on either side of a silicon substrate. With an optimized structural design, this absorber consistently captures over 90% of incoming waves across the frequency range of 1.65 to 4.49 THz, with a graphene Fermi level of 0.8 eV, and the whole device measures just 1.5 um thick. This makes our absorber significantly more effective and compact than previous designs. The absorber's effectiveness can be significantly enhanced by combining the metasurface's geometric design with the graphene Fermi level. It is anticipated that this ultrathin, wideband coherent perfect absorption device will play a crucial role in emerging on-chip THz communication technologies, including light modulators, photodetectors, and so on. [ABSTRACT FROM AUTHOR]

To address the situation that the degraded system cannot be repaired as new and the degradation is accelerated after repair in engineering practice, an imperfect condition-based maintenance model is proposed based on the Gamma process by introducing the maintenance improvement factor and the accelerated degradation factor. The maintenance improvement factor describes the recovery degree of the degradation amount after imperfect maintenance, then gives a quantitative representation method of the system maintenance improvement effect and the determination method of the maximum number of imperfect maintenance. The accelerated degradation factor is combined with the geometric process to describe the accelerated degradation process of the system after imperfect maintenance. The Monte Carlo method is used to determine the optimal number of imperfect preventive maintenance decisions. Through the demonstration of calculation examples, the influence of accelerated degradation factor and maintenance improvement factor on the long-term operation maintenance rate and the optimal number of imperfect maintenance is analyzed. The results show that the model can more comprehensively describe the degradation and maintenance process of degraded systems, and it can effectively reduce maintenance cost ratio under long-term operation. It provides a certain theoretical support for formulating an economical and reasonable maintenance strategy that is more in line with the actual degradation and maintenance process in engineering practice. [ABSTRACT FROM AUTHOR]

Background It is imperative for public health to identify the factors that contribute to the progression of sarcopenia among middle-aged and older adults. Our study aimed to investigate the association between pain characteristics and the progression to sarcopenia and its subcomponents among middle-aged and older adults in China. Methods We included 5 568 participants from the China Health and Retirement Longitudinal Study. All participants completed assessments for pain characteristics and sarcopenia. Pain assessment included pain status (baseline pain, incident pain, and pain persistence) and pain distribution (single-site pain and multisite pain) using a self-report questionnaire. Diagnosis of sarcopenia followed The Asian Working Group for Sarcopenia 2019 consensus. The odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by logical regression analysis. Results Participants who reported baseline pain, multisite pain, pain persistence, or multisite pain persistence were more likely to progress to sarcopenia than those without pain, with ORs of 1.33 (95% CI: 1.08–1.65), 1.44 (95% CI: 1.15–1.80), 1.63 (95% CI: 1.23–2.14), and 1.59 (95% CI: 1.19–2.11), respectively. Even after adjusting for other covariates such as gender, age, residential area, education level, marital status, smoking, alcohol consumption, comorbidities, and falls, these associations remained significant. Additionally, pain persistence and multisite pain persistence were significantly associated with low grip strength and clinically meaningful Short Physical Performance Battery decline, but not with low muscle mass. Conclusions Our study showed that pain, especially pain persistence, was closely correlated to the increased risk of progression to sarcopenia in Chinese middle-aged and older adults. [ABSTRACT FROM AUTHOR]

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time–frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time–frequency spectra of the enhanced signals, obtaining the optimal time–frequency spectra. Finally, all sample data are transformed into the optimal time–frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method. [ABSTRACT FROM AUTHOR]

Background: Triple‐negative breast cancer (TNBC) is a pathological subtype with a high mortality, and the development of inhibitors in the ubiquitin–proteasome system (UPS) component could be a novel therapeutic tool. Methods: Triple‐negative breast cancer data were obtained from The Cancer Genome Atlas (TCGA), and subtype analysis was performed by consistent clustering analysis to identify molecular subtypes of TNBC according to UPS characteristics. Differential analysis, COX and least absolute shrinkage and selection operator (LASSO) COX regression analyses were performed to select genes associated with overall survival in TNBC. The final prognostic model (UPS score) was determined using the LASSO COX model. The model performance was assessed using receiver operating characteristic (ROC) curves and survival curves. In addition, the results of the UPS score on analyzing the abundance of immune cell infiltration and immunotherapy were explored. Finally, we developed a nomogram for TNBC survival prediction. Results: Two UPS subtypes (UPSMS1 and UPSMS2) showing significant survival differences were classified. COX regression analysis on differentially expressed genes in UPSMS1 and UPSMS2 filtered five genes that affected overall survival. Based on the regression coefficients and expression data of the five genes, we built a prognostic assessment system (UPS score). The UPS score showed consistent prognostic and therapeutic guidance values. Finally, the ROC curve of the nomogram and UPS score showed the highest predictive efficacy compared with traditional clinical prognostic indicators. Conclusion: The UPS score represented a promising prognostic tool to predict overall survival and immune status and guide personalized treatment selection in TNBC patients, and this study may provide a more practical alternative for clinical monitoring and management of TNBC. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: As the third generation new battery, the power conversion efficiency (PCE) of metal halide perovskite solar cells (PSCs) has increased from 3.8% in 2009 to 25.8% currently certified, which fully shows that they have great research value and development prospect. As one of the main components of high‐efficiency PSCs, hole transport materials (HTMs) play an important role in extracting and transporting holes and inhibiting charge recombination. However, commonly used HTMs require doping, and the hygroscopicity and corrosiveness of the dopants will destroy the stability of PSCs and hinder their commercialization. Therefore, it is of great significance to develop dopant‐free HTMs. In this review, the dopant‐free HTMs in recent six years are reviewed and summarized systematically, including organic small molecules, polymers and cross‐linkable materials. We focus on the design of the molecular cores and discuss their structure–property correlation, conductivity, and photovoltaic performance. Finally, how to design an ideal HTM is summarized. We hope that this review can provide reference for the development of low‐cost and dopant‐free HTMs to prepare efficient and stable PSCs. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: As the third generation new battery, the power conversion efficiency (PCE) of metal halide perovskite solar cells (PSCs) has increased from 3.8% in 2009 to 25.8% currently certified, which fully shows that they have great research value and development prospect. As one of the main components of high‐efficiency PSCs, hole transport materials (HTMs) play an important role in extracting and transporting holes and inhibiting charge recombination. However, commonly used HTMs require doping, and the hygroscopicity and corrosiveness of the dopants will destroy the stability of PSCs and hinder their commercialization. Therefore, it is of great significance to develop dopant‐free HTMs. In this review, the dopant‐free HTMs in recent six years are reviewed and summarized systematically, including organic small molecules, polymers and cross‐linkable materials. We focus on the design of the molecular cores and discuss their structure–property correlation, conductivity, and photovoltaic performance. Finally, how to design an ideal HTM is summarized. We hope that this review can provide reference for the development of low‐cost and dopant‐free HTMs to prepare efficient and stable PSCs. [ABSTRACT FROM AUTHOR]

The synthesis of low-cost and high-efficiency multicolour emitting graphene quantum dots (GQDs) is of utmost importance for their prospective applications. Lignin, a renewable biomass, contains aromatic compounds that are well-suited for the preparation of polycyclic aromatic GQDs. In this study, we successfully synthesized red, yellow, green, and blue light-emitting single-layered GQDs with high crystallization using hydrothermal methods, thereby covering the full spectrum of visible light. High-resolution transmission electron microscopy (HRTEM) images revealed the presence of a two-dimensional crystal lattice with the 100 (0.21 nm) and 002 (0.24 nm) facets of graphite, indicating the high crystallization of the synthesized GQDs. Additionally, the average height of all GQDs was found to be below 1 nm, confirming their single-layered structure. This observation supports the notion that mono-benzene compounds tend to form fused polycyclic arenes under high pressure and temperature conditions. Moreover, by analyzing X-ray Photoelectron Spectroscopy (XPS) and Fourier Transform Infrared (FT-IR) spectra, we investigated the formation mechanism of GQDs derived from lignin at the molecular level. The cleavage of ether bonds, aldol condensation, benzylic acid rearrangement, and dehydration were identified as vital intermediate processes. Furthermore, we successfully demonstrated the application of multicolour-emitting GQDs derived from lignin in light-emitting diodes (LEDs). This synthetic strategy for producing multicolour-emitting GQDs from lignin opens up a new path for the conversion of sustainable biomass into high-value nanomaterials. [ABSTRACT FROM AUTHOR]

Sarcopenia and chronic pain are prevalent among older adults, and despite numerous studies, the potential epidemiological link between the two conditions remains a topic of controversy. Therefore, we performed a comprehensive systematic review and meta-analysis to assess the relationship between chronic pain and sarcopenia in the elderly.Methods: EMBASE, Web of Science, PubMed, and the Cochrane Library were searched through 22 March 2023 with additional manual searches of reference lists of included studies and relevant reviews. We used a random effects model to conduct the meta-analysis and evaluated heterogeneity across studies with Cochran's Q statistic and I2. Subgroup analyses were conducted based on income level, diagnostic criteria for sarcopenia, and pain site.Results: 17 observational studies (33,600 participants, 49% female) were included, of which 6 articles were retrieved for narrative review. The pooled prevalence of sarcopenia and the pooled odds ratios (OR) between chronic pain and sarcopenia were extracted from the remaining 11 studies. The pooled prevalence of sarcopenia among older adults suffering from chronic pain was 0.11 (95% CI, 0.08– 0.18). Our analysis revealed a statistically significant positive association between chronic pain and an increased risk of sarcopenia, yielding a pooled OR of 1.52 (95% CI, 1.31– 1.76). Furthermore, our subgroup analysis demonstrated that the low-income countries group showed a stronger association (OR, 1.73; 95% CI, 1.54– 1.95) between chronic pain and sarcopenia than the high-income countries group (OR, 1.38; 95% CI, 1.20– 1.60).Conclusion: Older adults with chronic pain have a significantly higher prevalence of sarcopenia and risk of developing sarcopenia compared to those without pain. These findings highlight the importance of prioritizing the assessment and early detection of chronic pain in older people, as well as implementing proactive intervention measures in clinical practice. In addition, our results suggest that older people with chronic pain should be actively screened for sarcopenia.Prospero Registration Number: CRD42021239807. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) affects around 40% of people with diabetes, the final outcome of which is end‐stage renal disease. The deficiency of autophagy and excessive oxidative stress have been found to participate in the pathogenesis of DN. Sinensetin (SIN) has been proven to have strong antioxidant capability. However, the effect of SIN on DN has not been studied. We examined the effect of SIN on cell viability and autophagy in the podocyte cell line, MPC5 cells, treated with high glucose (HG). For in vivo studies, DN mice models were established by intraperitoneal injected with streptozotocin (40 mg/kg) for 5 consecutive days and fed with a 60% high‐fat diet, and SIN was given (10, 20, and 40 mg/kg) for 8 weeks via intraperitoneal injection. The results showed that SIN could protect MPC5 cells against HG‐induced damage and significantly improve the renal function of DN mice. Moreover, SIN remarkably restored the autophagy activity of MPC5 cells which was inhibited under HG conditions. Consistent with this, SIN efficiently improved autophagy in the kidney tissue of DN mice. In brief, our findings demonstrated the protective effect of SIN on DN via restoring the autophagic function, which might provide a basis for drug development. Highlights: Sinensetin (SIN) could protect podocytes from high glucose (HG) injury.SIN could restore the autophagic function injured by HG.SIN could protect the mitochondria of podocytes, kidney damage, and damaged autophagy in the experimental diabetic nephropathy mice model. [ABSTRACT FROM AUTHOR]