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Water conservation and appliance efficiency standards programs in the United States encounter challenges in assessing their economic impacts due to the limited availability of representative water and wastewater price data. In this report, we review water and sewage price and price trend data from two established sources: the Water and Sewerage Consumer Price Index from the U.S. Bureau of Labor Statistics and water and wastewater rate survey data from Raftelis/American Water Works Association (AWWA). By leveraging historical Raftelis/AWWA data, we develop an empirical approach to (1) interpolate the data by establishing regional trends that capture the pace of price increases as transparently and consistently as possible, and (2) extrapolate prices for future years for the purpose of quantifying water cost savings resulting from policy decisions. Additionally, our prediction models offer a means to verify data consistency when new rate survey data from Raftelis/AWWA become available.

Wearable continuous gait phase estimation is essential for walking assistance, clinical rehabilitation, and clinical assessment; however, most algorithms have only been validated for straight-line and constant-speed walking, and it is unclear how performance will change in real-life locomotion scenarios. A generalized paradigm is needed to comprehensively assess and recommend wearable continuous gait phase estimation strategies for the diverse array of walking situations. We thus propose a comprehensive evaluation indicator system for eight typical gait activities in daily life including slow walking, standard walking, running, walking with turns, stair descent, stair ascent, stop-and-go, and uneven terrain walking. The indicator system was used to evaluate four commonly used continuous gait phase estimation strategies: adaptive oscillators, phase oscillator, neural network, and time-based estimation. Eleven healthy participants were enrolled in the evaluation. All estimation strategies performed well for constant-speed walking but performance varied for other activities. Time-based estimation was most accurate for slowwalking ( 0.094 +/- 0.011 rad root mean square error, 1.50 +/- 0.18 % of one gait cycle), running ( 0.167 +/- 0.028 rad, 2.66 +/- 0.44 %) and walking with turns ( 0.124 +/- 0.047 rad, 2.00 +/- 0.75 %). Adaptive oscillators were most accurate for standard walking( 0.115 +/- 0.037 rad, 1.83 +/- 0.59%). Phase oscillator was most accurate for stair climbing( 0.280 +/- 0.063 rad, 4.46 +/- 1.00 %) and uneven terrain ( 0.204 +/- 0.069 rad, 4.30 +/- 1.10%). Neural network was most accurate for stop-and-go( 0.27 +/- 0.114 rad, 4.30 +/- 1.81 %). These results can potentially provide guidance for determining suitable gait phase estimation strategies in realistic locomotion scenarios, and in comparing and optimizing the current proposed strategies., QC 20240923

Chengying Hong,1 Yihan Xiong,2 Jinquan Xia,3 Wei Huang,4 Andi Xia,1 Shunyao Xu,1 Yuting Chen,1 Zhikun Xu,1 Huaisheng Chen,1 Zhongwei Zhang5 1Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 2Neurology Department, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 3Department of Clinical Medical Research Center, The Second Clinical Medical College, Jinan University (Shenzhen People’s Hospital), The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 4Department of Clinical Microbiology, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China; 5Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of ChinaCorrespondence: Huaisheng Chen, Department of Critical Care Medicine, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People’s Republic of China, Email sunshinic@hotmail.com Zhongwei Zhang, Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China, Email 716461751@qq.comObjective: The objective of this study was to utilize LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) to identify key variables in septic patients and develop a predictive model f

Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method. © 2024 by the authors.

Pecan Street's field-metered data offer an opportunity to track actual appliance usage patterns and energy consumption over multiple years, and can supplement data from the US Energy Information Administration's Residential Energy Consumption Survey (RECS). This report is based on dishwasher metering data collected from more than 500 households located in Texas, California, New York, and Colorado from 2012 to 2021. The historical dishwasher usage frequency, the COVID-19 period usage change, and the potential seasonal trend in usage were investigated. Dishwasher cycle features such as cycle duration, quick cycle usage frequency, and average per cycle dishwasher energy consumption were observed and characterized. Due to the sample size, the lack of demographic data, and the limited geographic locations of the participating households, the results are not nationally representative. However, when compared with the usage frequency reported by RECS, the field-obtained average annual cycle counts per household are 164 in 2015 and 197 in 2020 for selected households which are consistent with the RECS annual cycle counts of 181 and 191 respectively in 2015 and 2020 for the same geographic locations. Our findings support the use of RECS data to approximate field dishwasher usage. The field data usage frequency for households with infrequent dishwasher use could supplement the RECS information to better characterize the national usage distribution of different cycle selections.

BACKGROUND: More than one-third of children and adolescents consume foods from quick-service restaurants (QSRs) daily, which is associated with an increased risk of diet-related adverse health conditions. OBJECTIVE: To examine trends in the proximity of top-selling QSR chains to all public schools across the United States between 2006 and 2018 by community-, school-, and student-level characteristics. DESIGN: This longitudinal study examined changes in the number QSRs between the 2006-2007 and 2017-2018 school years using data from National Center for Education Statistics, Infogroup US Historical Business Data, and the US Department of Agricultures Economic Research Service. STATISTICAL ANALYSIS: A mixed-model analysis of variance using census tract as a random effect and accounting for repeated measures by school was used to examine the proximity of QSRs near schools. Models adjusted for demographic characteristics and census tract population density. Data were analyzed in 2021. RESULTS: During 2006, 9% of schools had QSRs within 400 m, and 25% of schools in the most populated areas had at least one QSR within 400 m. There were more QSRs near schools with a high percentage of poverty (12%), and near schools with high school students with the highest population of Black or African American (16%) and Hispanic or Latino (18%) students. By 2018, the percent of all public schools within 400 m of QSRs increased to 12%. The increase over time was greater near schools with a high percentage of poverty (16%) and near schools with high school students with the highest population of Black or African American students (22%) and Hispanic or Latino (23%) students. CONCLUSIONS: This is the first nationwide study to examine trends in QSR proximity to all public schools. QSRs were most likely to be located near schools with high school students, near schools with a high percentage of poverty, and near schools with a higher proportion of racial and ethnic minority students. Ove

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion:As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost., QC 20230628

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion:As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost., QC 20230628

In marine biological sampling, the noncontact polyhedral gripper is initiallydeveloped for the purpose of capturing brittle animals and delicate soft-bodiedorganisms. However, the auxiliary driving rods of earlier polyhedral grippers maketheir structure complex, and the unidirectional surrounding capture processmakes it simple to generate water flow with less capture effect. Herein, an origamipolyhedral gripper for midwater sampling is introduced, along with its fabrication,analysis, material selection, and size parameter calculation process. The captureprocess is designed to be omnidirectional surrounding; therefore, the generatedwater flow can be reduced. And the whole mechanism is bi-stable and only needsone actuator to operate. Capture experiments on pet fish with fragile tails wereconducted on a remotely operated vehicle (ROV) and on simply constructingartificial arms to prove its feasibility and high adaptability. Overall, the unitmechanism of this gripper is analyzed using the Screw theory to show how it has abistate feature. And using the same origami-structural unit, additional foldablepolyhedral structures with the same folding modes are also introduced., QC 20230628

The movement mode of snakes is crawling, and the living environment of snakes with numerous branches and stones will cause plenty of wear for the snake scales. There are plenty of surface structures and morphology on snake scales to avoid severe wear. Among them, the research towards the keeled structure on snake scales is missing. Therefore, in this research, the wear resistance improvement of the keeled structure on the snake scales and the overlapped distribution of snake scales are investigated. The keeled and smooth snake scales were 3D printed and they were distributed on the substrate in the overlapped or paralleled ways. Besides these four samples with keeled/smooth scales and overlapped/paralleled distributed, there is also a reference sample with the same thickness. Based on the tribology test, the number of grooves of samples with the keeled structures is higher than that of samples with smooth surfaces, which indicates that the keeled structure dramatically enhances the wear resistance of snake scales, especially during the wear in the vertical direction. The experiment on surface morphology greatly compromised the result of the tribology test. In addition, the bottom portion of the keeled snake scales can be protected by the keeled structure. Besides, the overlapped distribution can protect the central region of snake scales and provide double-layer protection of the snake body. Overall, the keeled structure and the overlapped distribution play a significant part in the improvement of wear resistance of the snake skin. These findings can enhance the knowledge of the reptiles-mimic surface structure and facilitate the application of military uniforms under high-wear conditions., QC 20230627

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion:As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost., QC 20230628

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion:As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost., QC 20230628

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion:As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost., QC 20230628

The fabrication of organic solar cells (OSCs) depends heavily on the use of highly toxic chlorinated solvents, which are incompatible with industrial manufacturing. The reported alternative solvents such as non-halogenated aromatic hydrocarbons and cyclic ethers are also not really “green” according to the “Globally Harmonized System of Classification and Labelling of Chemicals” of the United Nations. Therefore, processing from real green solvents such as water, alcohols, or anisole will constitute a big breakthrough for OSCs. However, it is fundamentally challenging to obtain high-performance photovoltaic materials processable from these solvents. Herein, we propose the incorporation of a B-N covalent bond, which has a dipole moment of 1.84 Debye, into the conjugated backbone of polymer donors to fabricate high-efficiency OSCs from anisole, a real green and eco-compatible solvent recommended by the United Nations. Based on a newly developed B-N-based polymer, the OSCs with a record-high efficiency of 15.65% in the 0.04 cm2 device and 14.01% in the 1.10 cm2 device have thus been realized via real green processing.

This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents. Engaging in a three-stage participatory design processes, we work with with 23 domain experts from public libraries across the U.S., uncovering their fundamental challenges of integrating AI into human workflows. Our findings provide 23 actionable "heuristics for service co-creation with AI", highlighting the nuanced shared responsibilities between humans and AI. We further exemplar 9 foundational agency aspects for AI, emphasizing essentials like ownership, fair treatment, and freedom of expression. Our innovative approach enriches the participatory design model by incorporating AI as crucial stakeholders and utilizing AI-AI interaction to identify blind spots. Collectively, these insights pave the way for synergistic and ethical human-AI co-creation in service contexts, preparing for workforce ecosystems where AI coexists., Comment: V1.0 on Oct 25th, 2023

Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies treat regex as a linear sequence of tokens and generate the final expressions autoregressively in a single pass. They did not take into account the step-by-step internal text-matching processes behind the final results. This significantly hinders the efficacy and interpretability of regex generation by neural language models. In this paper, we propose a new paradigm called InfeRE, which decomposes the generation of regexes into chains of step-by-step inference. To enhance the robustness, we introduce a self-consistency decoding mechanism that ensembles multiple outputs sampled from different models. We evaluate InfeRE on two publicly available datasets, NL-RX-Turk and KB13, and compare the results with state-of-the-art approaches and the popular tree-based generation approach TRANX. Experimental results show that InfeRE substantially outperforms previous baselines, yielding 16.3% and 14.7% improvement in DFA@5 accuracy on two datasets, respectively. Particularly, InfeRE outperforms the popular tree-based generation approach by 18.1% and 11.3% on both datasets, respectively, in terms of DFA@5 accuracy., Comment: This paper has been accepted by ASE'23

This report seeks to inform the potential for motor systems to support decarbonization by estimating their potential energy, electricity cost, and CO2 emissions reduction potential from adoption of proven energy efficiency actions and advanced technologies. This is the third and final report in a series of reports disseminating the findings of the U.S. Department of Energy’s (DOE’s) Motor System Market Assessment (MSMA). The MSMA and this report focus on polyphase motor systems greater than or equal to 1 horsepower (hp) in the industrial and commercial sectors. In the U.S. Industrial and Commercial Motor System Market Assessment Report Volume 1: Characteristics of the Installed Base (Volume 1 report), it was determined that these motor systems consume more than 1,000 terawatt-hours (TWh) annually. This equates to 29% of the U.S. electric grid load and results in 765 million metric tons (MMT) of CO2 emissions and $166 billion in electricity costs. This report finds that substantial reductions to these energy, cost, and emissions impacts are possible, with three areas of significant opportunity being (1) improved load matching, (2) replacing older inefficient motors with more efficient motors, and (3) improving the condition of fluid (e.g., air, water, compressed air) distribution systems.

This report seeks to inform the potential for motor systems to support decarbonization by estimating their potential energy, electricity cost, and CO2 emissions reduction potential from adoption of proven energy efficiency actions and advanced technologies. This is the third and final report in a series of reports disseminating the findings of the U.S. Department of Energy’s (DOE’s) Motor System Market Assessment (MSMA). The MSMA and this report focus on polyphase motor systems greater than or equal to 1 horsepower (hp) in the industrial and commercial sectors. In the U.S. Industrial and Commercial Motor System Market Assessment Report Volume 1: Characteristics of the Installed Base (Volume 1 report), it was determined that these motor systems consume more than 1,000 terawatt-hours (TWh) annually. This equates to 29% of the U.S. electric grid load and results in 765 million metric tons (MMT) of CO2 emissions and $166 billion in electricity costs. This report finds that substantial reductions to these energy, cost, and emissions impacts are possible, with three areas of significant opportunity being (1) improved load matching, (2) replacing older inefficient motors with more efficient motors, and (3) improving the condition of fluid (e.g., air, water, compressed air) distribution systems.

Motor systems are an integral part of our industrial and commercial facilities. They provide the motive force behind the fans, pumps, compressors, chillers, and conveyors in these facilities. Given their centrality to any facility’s operations, they are a critical energy end use to understand, particularly when developing technologies and policies to meet sustainability goals, improve productivity, and enhance resilience. In the late 1990s, the U.S. Department of Energy (DOE) conducted two seminal studies to better understand the installed stock and energy savings opportunities of industrial and commercial motor systems. In the industrial sector, The United States Industrial Electric Motor Systems Market Opportunities Assessment used primary data collected through onsite assessments and led to a greater understanding of the installed base of motor systems, their characteristics, and the opportunities for energy savings (U.S. Department of Energy, 2002). Notable findings included the following: • Industrial motor systems consumed 679 billion kilowatt-hours (kWh) in 1994, representing 23 percent of U.S. electricity consumption. • Cost-effective energy efficiency measures could result in 62-104 billion kWh of energy savings annually. • Sixty-two percent of the energy savings potential was from fan, pump, and compressor end use equipment. • Nearly half of motor system electricity consumption was attributable to approximately 3,500 facilities, or 1.5 percent of U.S. manufacturing facilities. Opportunities for Energy Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors provided an evaluation of the installed stock of motor driven equipment in U.S. commercial and residential buildings and opportunities for utilization of high efficiency motors and variable speed technologies (Arthur D. Little, 1999). Notable findings included the following: • Commercial motor systems consumed 343 billion kWh in 1995, with refrigeration and space conditioni

Intuitive self-assembly devices are of great significance to the emerging applications of self-assembly theory. In this paper, a novel intuitive device with an aerodynamic system is fabricated for the self-assembly experiment. Table tennis balls were used as the objects to be assembled during the self-assembly process. To understand more about the system, two experiments were designed—the directed assembly experiment was conducted to organize a specific structure and to explore the influences of environmental variables, and the indirect assembly experiment repeated with the “bottom-up” self-organization process and expressed the characteristics of “the optimization” and “the emergence” in the self-organization process. This article expressed a novel self-assembly approach at a macroscale and created a new choice or idea for the structural design and the optimization method., QC 20230627

Intuitive self-assembly devices are of great significance to the emerging applications of self-assembly theory. In this paper, a novel intuitive device with an aerodynamic system is fabricated for the self-assembly experiment. Table tennis balls were used as the objects to be assembled during the self-assembly process. To understand more about the system, two experiments were designed—the directed assembly experiment was conducted to organize a specific structure and to explore the influences of environmental variables, and the indirect assembly experiment repeated with the “bottom-up” self-organization process and expressed the characteristics of “the optimization” and “the emergence” in the self-organization process. This article expressed a novel self-assembly approach at a macroscale and created a new choice or idea for the structural design and the optimization method., QC 20230627

Intuitive self-assembly devices are of great significance to the emerging applications of self-assembly theory. In this paper, a novel intuitive device with an aerodynamic system is fabricated for the self-assembly experiment. Table tennis balls were used as the objects to be assembled during the self-assembly process. To understand more about the system, two experiments were designed—the directed assembly experiment was conducted to organize a specific structure and to explore the influences of environmental variables, and the indirect assembly experiment repeated with the “bottom-up” self-organization process and expressed the characteristics of “the optimization” and “the emergence” in the self-organization process. This article expressed a novel self-assembly approach at a macroscale and created a new choice or idea for the structural design and the optimization method., QC 20230627

Intuitive self-assembly devices are of great significance to the emerging applications of self-assembly theory. In this paper, a novel intuitive device with an aerodynamic system is fabricated for the self-assembly experiment. Table tennis balls were used as the objects to be assembled during the self-assembly process. To understand more about the system, two experiments were designed—the directed assembly experiment was conducted to organize a specific structure and to explore the influences of environmental variables, and the indirect assembly experiment repeated with the “bottom-up” self-organization process and expressed the characteristics of “the optimization” and “the emergence” in the self-organization process. This article expressed a novel self-assembly approach at a macroscale and created a new choice or idea for the structural design and the optimization method., QC 20230627

Intuitive self-assembly devices are of great significance to the emerging applications of self-assembly theory. In this paper, a novel intuitive device with an aerodynamic system is fabricated for the self-assembly experiment. Table tennis balls were used as the objects to be assembled during the self-assembly process. To understand more about the system, two experiments were designed—the directed assembly experiment was conducted to organize a specific structure and to explore the influences of environmental variables, and the indirect assembly experiment repeated with the “bottom-up” self-organization process and expressed the characteristics of “the optimization” and “the emergence” in the self-organization process. This article expressed a novel self-assembly approach at a macroscale and created a new choice or idea for the structural design and the optimization method., QC 20230627

Background: Metastatic melanoma has a high mortality rate and poor survival. This is associated with efficient metastatic colonization, but the underlying mechanisms remain elusive. Communication between cancer stem cells (CSCs) and cancer cells plays an important role in metastatic dissemination. Whether cancer stem cells can alter the metastatic properties of non-CSC cells; and whether exosomal crosstalk can mediate such interaction, have not been demonstrated in melanoma prior to this report. Results: The results revealed that exosomes secreted by highly metastatic melanoma CSCs (OL-SCs) promoted the invasiveness of the low metastatic melanoma cells (OL) and accelerated metastatic progression. miR-1268a was up-regulated in cells and exosomes of OL-SCs. Moreover, OL-SCs-derived exosomal miR-1268a, upon taking up by OL cells, promoted the metastatic colonization ability of OL cells in vitro and in vivo. In addition, the pro-metastatic activity of exosomal miR-1268a is achieved through inhibition of autophagy. Conclusion: Our study demonstrates that OL cells can acquire the “metastatic ability” from OL-SCs cells. OL-SCs cells achieves this goal by utilizing its exosomes to deliver functional miRNAs, such as miR-1268a, to the targeted OL cells which in turn augments metastatic colonization by inactivating the autophagy pathway in OL cells.

Yi Jiang,1 Lijie Wei,1 Huiting Zhang,1 Yuting Chen,1 Peng Gao,1 Jingyi Zhang,1 Xuan Zhou,1 Shenglan Zhu,1 Yuanyuan Du,1 Chenyun Fang,1 Jiaqi Li,2 Ling Feng,1 Mengzhou He,1 Shaoshuai Wang,1 Jun Yu1 1Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China; 2Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 33006, People’s Republic of ChinaCorrespondence: Jun Yu, Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Qiaokou District, Wuhan, Hubei Province, People’s Republic of China, Email yjtj2010@163.comIntroduction: Gestational diabetes mellitus (GDM) is one of the common metabolic disorders of pregnancy and results in poor pregnancy outcomes for both mother and fetus. MiR-17-5p is considered as the strongest predictor of metabolic syndrome status, but the relationship between GDM and miR-17-5p remains unclear. TXNIP, which leads to activation of NLRP3, is considered as a potential target of miR-17-5p, and the miR-17-5p/TXNIP/NLRP3 axis has been shown to play a major role in the occurrence and development of many metabolic diseases but has not been validated in GDM.Methods: MiR-17-5p was detected by RT-qPCR. The expression of TXNIP and NLRP3 in placenta was detected by immunofluorescence, RT-qPCR and Western blot. To explore the effect of miR-17-5p on TXNIP and NLRP3 and glucose uptake of HTR8/SVneo cells, miR-17-5p mimic and miR-17-5p inhibitor were transfected to achieve overexpression and inhibition. The interaction between miR-17-5p and TXNIP was confirmed by dual-luciferase reporter assay. Besides, glucose consumption of trophoblast cells was detected by glucose assay kit.Results: MiR-17-5p expression was down-regulated, while the expression of TXNIP and NLRP3 was up-regulat

Accurately modeling the growth process of plants in interaction with their environment is important for predicting their biophysical characteris-tics, referred to as phenotype prediction. Most models are described by dis-crete dynamic systems in general state-space representation with important domain-specific characteristics: First, plant model parameters have usually clear functional meanings and may be of genetic origins, thus necessitating a precise estimation. Second, critical growth variables, specifically biomass production and dynamic allocation to organs, are hidden variables not acces-sible to measure. Finally, the difficulty to assess the local plant environment may imply the introduction of process noises in models. Therefore, a precise understanding of the system’s behavior requires the joint estimation of functional parameters, hidden states, and noise parameters. In this paper we describe how a full Bayesian method of estimation can accurately estimate all these key model variables using Markov chain Monte Carlo (MCMC) tech-niques. In the presence of both process and observation noises, it requires to use adequate particle MCMC (PMCMC) algorithms to efficiently sample the hidden states which, consequently, allows for a precise estimation of all noise parameters involved. Thanks to the Bayesian framework, appropriate choices of prior distributions for the noise parameters have enabled analytical pos-terior distributions and only simple updates are required. Furthermore, this estimation strategy can be easily generalized and adapted to different types of plant growth models, such as organ-scale or compartmental, provided that they are formulated as hidden Markov models. Our estimation method im-proves on those classically used in plant growth modeling in several aspects: First, by building upon a general probabilistic framework the estimation results allow proper statistical analyses. It is useful in prediction, no only for uncertainty and risk analysis

Motor systems are an integral part of our industrial and commercial facilities. They provide the motive force behind the fans, pumps, compressors, chillers, and conveyors in these facilities. Given their centrality to any facility’s operations, they are a critical energy end use to understand, particularly when developing technologies and policies to meet sustainability goals, improve productivity, and enhance resilience. In the late 1990s, the U.S. Department of Energy (DOE) conducted two seminal studies to better understand the installed stock and energy savings opportunities of industrial and commercial motor systems. In the industrial sector, The United States Industrial Electric Motor Systems Market Opportunities Assessment used primary data collected through onsite assessments and led to a greater understanding of the installed base of motor systems, their characteristics, and the opportunities for energy savings (U.S. Department of Energy, 2002). Notable findings included the following: • Industrial motor systems consumed 679 billion kilowatt-hours (kWh) in 1994, representing 23 percent of U.S. electricity consumption. • Cost-effective energy efficiency measures could result in 62-104 billion kWh of energy savings annually. • Sixty-two percent of the energy savings potential was from fan, pump, and compressor end use equipment. • Nearly half of motor system electricity consumption was attributable to approximately 3,500 facilities, or 1.5 percent of U.S. manufacturing facilities. Opportunities for Energy Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors provided an evaluation of the installed stock of motor driven equipment in U.S. commercial and residential buildings and opportunities for utilization of high efficiency motors and variable speed technologies (Arthur D. Little, 1999). Notable findings included the following: • Commercial motor systems consumed 343 billion kWh in 1995, with refrigeration and space conditioni

Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios.

PurposeWalking-aid exoskeletons can assist and protect effectively the group with lower limb muscle strength decline, workers, first respondersand military personnel. However, there is almost no united control strategy that can effectively assist daily walking. This paper aims to propose ahybrid oscillators’ (HOs) model to adapt to irregular gait (IG) patterns (frequent alternation between walking and standing or rapid changing ofwalking speed, etc.) and generate compliant and no-delay assistive torque. Design/methodology/approachThe proposed algorithm, HOs, combines adaptive oscillators (AOs) with phase oscillator through switchingassistive mode depending on whether or not the AOs' predicting error of hip joint degree is exceeded our expectation. HOs can compensate fordelay by predicting gait phase when in AOs mode. Several treadmill and free walking experiments are designed to test the adaptability andeffectiveness of HOs model under IG. FindingsThe experimental results show that the assistive strategy based on the HOs is effective under IG patterns, and delay is compensatedtotally under quasiperiodic gait conditions where a smoother human–robot interaction (HRI) force and the reduction of HRI force peak are observed.Delay compensation is found very effective at improving the performance of the assistive exoskeleton. Originality/valueA novel algorithm is proposed to improve the adaptability of a walking assist hip exoskeleton in daily walking as well asgenerate compliant, no-delay assistive torque when converging., QC 20230629

Due to the extremely high area-to-thickness, nanosheets have become one of the rising research fields with vast applications. In comparison with other bulk materials, the 2D structure reveals unique physical (e.g., ultra-thin thickness, high flexibility and stretchability, excellent adhesion, etc.), electrochemical properties (e.g., superior electrical, catalytic properties, etc.) and expected to have immense potential for high-performance sensors. In this review, the state-of-the-art research in nanosheet-based sensing applications are discussed into three subtopics of the electrocatalytic sensor, gas sensor and epidermal sensor., QC 20230629

Background: Pathological gaits of children m:y lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for, QC 20230630

Origami/kirigami, the ancient art of paper folding and cutting techniques, has provided considerable inspiration for structural design routes in the engineering and medical fields over the last few decades. The practicability of the methods and concepts of origami/kirigami has been demonstrated in several emerging classes of technologies, e.g., stretchable electronics, deformable devices, self-assembly fabrication, etc. More and more related products are produced pursuing a folding form for better storage, deformation capacity, and multifunction realization. Herein, the innovative creased patterns of origami/kirigami designs are distinguished and discussed, and the four most widely used creased pattern types are introduced, which may potentially provide origami/kirigami related inspiration and additional solutions toward many research fields., QC 20230627

Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios.

Software developers frequently adopt deep learning (DL) libraries to incorporate learning solutions into software systems. However, misuses of these libraries can cause various DL faults. Among them, tensor shape faults are most prevalent. Tensor shape faults occur when restriction conditions of operations are not met, leading to many system crashes. To support efficient detection and fixing of these faults, we conduct an empirical study to obtain a deep insight. We construct SFData, a set of 146 buggy programs with crashing tensor shape faults (i.e., those causing programs to crash). By analyzing the faults in SFData, we categorize them into four types and get some valuable observations.

Region-specific Research and Development (R&D) of microalga-derived product systems are crucial if “biotech's green gold” is to be explored in a rational and economically viable way. Coastal zones, particularly the locations around the equator, are typically considered to be optimum cultivation sites due to stable annual temperature, light, and ready availability of seawater. However, a ‘cradle-to-grave’ assessment of the development of microalgal biotechnology in these areas, not only under the laboratory conditions, but also in the fields has not yet been demonstrated. In this study, to evaluate the viability of microalga-derived multi-product technology, we showed the development of microalgal biotechnology in coastal zones for aquaculture and food. By creating and screening a (sub)tropical microalgal collection, a Chlorella strain MEM25 with a robust growth in a wide range of salinities, temperatures, and light intensities was identified. Evaluation of the economic viability and performance of different scale cultivation system designs (500 L and 5000 L closed photobioreactors and 60,000 L open race ponds, ORPs) at coastal zones under geographically specific conditions showed the stable and robust characteristics of MEM25 across different production system designs and various spatial and temporal scales. It produces high amounts of proteins and polyunsaturated fatty acids (PUFAs) in various conditions. Feeding experiments reveal the nutritional merits of MEM25 as food additives where PUFAs and essential amino acids are enriched and the algal diet improves consumers' growth. Economic evaluation highlights an appreciable profitability of MEM25 production as human or animal food using ORP systems. Therefore, despite the pros and cons, sound opportunities exist for the development of market-ready multiple-product systems by employing region-specific R&D strategies for microalgal biotechnology.

Any permutation polynomial is an $ n $-cycle permutation. When $n$ is a specific small positive integer, one can obtain efficient permutations, such as involutions, triple-cycle permutations and quadruple-cycle permutations. These permutations have important applications in cryptography and coding theory. Inspired by the AGW Criterion, we propose criteria for $ n $-cycle permutations, which mainly are of the form $ x^rh(x^s) $. We then propose unified constructing methods including recursive ways and a cyclotomic way for $ n $-cycle permutations of such form. We demonstrate our approaches by constructing three classes of explicit triple-cycle permutations with high index and two classes of $ n $-cycle permutations with low index.

SMT-based model checkers, especially IC3-style ones, are currently the most effective techniques for verification of infinite state systems. They infer global inductive invariants via local reasoning about a single step of the transition relation of a system, while employing SMT-based procedures, such as interpolation, to mitigate the limitations of local reasoning and allow for better generalization. Unfortunately, these mitigations intertwine model checking with heuristics of the underlying SMT-solver, negatively affecting stability of model checking. In this paper, we propose to tackle the limitations of locality in a systematic manner. We introduce explicit global guidance into the local reasoning performed by IC3-style algorithms. To this end, we extend the SMT-IC3 paradigm with three novel rules, designed to mitigate fundamental sources of failure that stem from locality. We instantiate these rules for the theory of Linear Integer Arithmetic and implement them on top of SPACER solver in Z3. Our empirical results show that GSPACER, SPACER extended with global guidance, is significantly more effective than both SPACER and sole global reasoning, and, furthermore, is insensitive to interpolation., Comment: Published in CAV 2020

Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. However, most existing studies overlook the intrinsic association of the scenarios: the log data collected from platforms can be naturally divided into different scenarios (e.g., country, city, culture). We observed that the scenarios are heterogeneous because of the huge differences among them. Therefore, a unified model is difficult to effectively capture complex correlations (e.g., differences and similarities) between multiple scenarios thus seriously reducing the accuracy of recommendation results. In this paper, we target the problem of multi-scenario recommendation in e-commerce, and propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios. We first introduce scenario-aware feature representation, which transforms the embedding and attention modules to map the features into both global and scenario-specific subspace in parallel. Then we introduce an auxiliary network to model the shared knowledge across all scenarios, and use a multi-branch network to model differences among specific scenarios. Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network. We conduct extensive experiments on both public and industrial datasets, empirical results show that SAML consistently and significantly outperforms state-of-the-art methods.

Since 2006, the U.S. Environmental Protection Agency (EPA) has operated WaterSense® in partnership with manufacturers, utilities, and consumer groups. Similar to EPA’s ENERGY STAR® role for energy-efficient products, WaterSense® employs a labeling system to identify water-efficient products, homes, and services. As of 2015, the WaterSense® program can claim credit for a total savings of 1.5 trillion gallons of water and $32.6 billion in consumer water and energy bills. Savings are tracked in the National Water Savings (NWS) model that combines innovative analyses with methodologies established in the energy sector. Merging life-cycle cost and national impact analysis models, the NWS model estimates savings from a bottom-up accounting method for individual products. The model extends those savings to the national level by employing parameters such as frequency of product use by number of people and building type, product lifetime, stock accounting, and market saturation. The NWS model tracks the water and consumer monetary savings of WaterSense-labeled products for residential and commercial water use both indoors and out.

Since 2006, the U.S. Environmental Protection Agency (EPA) has operated WaterSense® in partnership with manufacturers, utilities, and consumer groups. Similar to EPA’s ENERGY STAR® role for energy-efficient products, WaterSense® employs a labeling system to identify water-efficient products, homes, and services. As of 2015, the WaterSense® program can claim credit for a total savings of 1.5 trillion gallons of water and $32.6 billion in consumer water and energy bills. Savings are tracked in the National Water Savings (NWS) model that combines innovative analyses with methodologies established in the energy sector. Merging life-cycle cost and national impact analysis models, the NWS model estimates savings from a bottom-up accounting method for individual products. The model extends those savings to the national level by employing parameters such as frequency of product use by number of people and building type, product lifetime, stock accounting, and market saturation. The NWS model tracks the water and consumer monetary savings of WaterSense-labeled products for residential and commercial water use both indoors and out.

Since 2006, the U.S. Environmental Protection Agency (EPA) has operated WaterSense® in partnership with manufacturers, utilities, and consumer groups. Similar to EPA’s ENERGY STAR® role for energy-efficient products, WaterSense® employs a labeling system to identify water-efficient products, homes, and services. As of 2015, the WaterSense® program can claim credit for a total savings of 1.5 trillion gallons of water and $32.6 billion in consumer water and energy bills. Savings are tracked in the National Water Savings (NWS) model that combines innovative analyses with methodologies established in the energy sector. Merging life-cycle cost and national impact analysis models, the NWS model estimates savings from a bottom-up accounting method for individual products. The model extends those savings to the national level by employing parameters such as frequency of product use by number of people and building type, product lifetime, stock accounting, and market saturation. The NWS model tracks the water and consumer monetary savings of WaterSense-labeled products for residential and commercial water use both indoors and out.

Benefiting from the abundance of sodium, rechargeable sodium ion batteries (SIBs) are preferred over lithium ion batteries (LIBs) in large-scale storage of electrical energy. Low-cost Prussian blue (PB) is a promising cathode because of its ease of synthesis and rigid open framework. Nevertheless, the less activated low spin Fe in FeC 6 octahedron lowers the average potential and the overall capacity, while the lattice vacancies and side reactions between low spin Fe and the electrolyte shortens PB's cycling life. Herein, PB with a controllable gradient nickel substitution, which features a high-Ni-content outer layer and low-Ni-content inner layer in a single grain, is demonstrated for high performance sodium storage. The high-Ni-content outer layer efficiently prevents side reactions, ensuring excellent cycling stability, while a small amount of nickel ions in the inner layer efficiently activates the low spin Fe for achieving high capacity. Taking the above synergistic advantages, our gradient-PB exhibits a high reversible capacity of 114 mA h g -1 at 100 mA g -1 and 84 mA h g -1 at 1 A g -1 with very stable cycling for up to 1000 cycles. Gradient substitution in PB analogues provides a novel strategy for exploring high performance PB-based cathodes for SIBs.

Benefiting from the abundance of sodium, rechargeable sodium ion batteries (SIBs) are preferred over lithium ion batteries (LIBs) in large-scale storage of electrical energy. Low-cost Prussian blue (PB) is a promising cathode because of its ease of synthesis and rigid open framework. Nevertheless, the less activated low spin Fe in FeC 6 octahedron lowers the average potential and the overall capacity, while the lattice vacancies and side reactions between low spin Fe and the electrolyte shortens PB's cycling life. Herein, PB with a controllable gradient nickel substitution, which features a high-Ni-content outer layer and low-Ni-content inner layer in a single grain, is demonstrated for high performance sodium storage. The high-Ni-content outer layer efficiently prevents side reactions, ensuring excellent cycling stability, while a small amount of nickel ions in the inner layer efficiently activates the low spin Fe for achieving high capacity. Taking the above synergistic advantages, our gradient-PB exhibits a high reversible capacity of 114 mA h g -1 at 100 mA g -1 and 84 mA h g -1 at 1 A g -1 with very stable cycling for up to 1000 cycles. Gradient substitution in PB analogues provides a novel strategy for exploring high performance PB-based cathodes for SIBs.