Privacy-preserving distributed optimization for economic dispatch in smart grids

The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Daniele Casagrande under the direction of Editor Florian Dorfler.This paper discusses a distributed economic dispatch problem (EDP) of smart grids while preventing sensitive information from being leaked during the communication process. In response to the problem, a novel privacy-preserving distributed economic dispatch strategy is developed via adding an exponentially decaying random noise to minimize the total cost of the grid while ensuring the privacy of sensitive state information. The quantitative relationship between the privacy and the estimation accuracy of eavesdroppers is profoundly disclosed in the framework of (ς, σ)-data-privacy. Furthermore, a sufficient condition on the iteration step size is achieved to ensure that the well-designed algorithm can converge to the optimal value of the addressed EDP exactly by resorting to the classical Lyapunov stability theory. Finally, simulation results verify the effectiveness of the carefully constructed privacy-preserving scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 62373251, U21A2019, 62222312 and 62473285; in part by the National Key Research and Development Program of China under Grant 2022YFB4501704; in part by the Shanghai Science and Technology Innovation Action Plan Project of China under Grant 22511100700; and in part by Fundamental Research Funds for the Central Universities

Association between total daily sedentary time and cardiometabolic biomarkers in older adults: A systematic review and meta-analysis

Supplementary Materials are available online at: https://journals.humankinetics.com/view/journals/jpah/22/9/article-p1086.xml?content=supplementary-materials .Background: Older adults engage in the highest levels of sedentary behavior across all age groups. Yet, the extent to which sedentary time is associated with cardiometabolic health in older adults is unclear. This systematic review and meta-analysis examined associations between daily sedentary time and cardiometabolic biomarkers in older adults. Methods: Peer-reviewed articles which studied the association between daily sedentary time and ≥1 cardiometabolic biomarker in participants aged ≥60 years were eligible. Five electronic databases (PubMed, CINAHL, MEDLINE, Web of Science, and PsycINFO) were searched. Screening, data extraction, and study quality were undertaken independently by 2 reviewers. Meta-analyses were undertaken using random-effects models based on correlation and regression coefficients. Methodological quality was assessed using the Joanna Briggs Institute checklist. Results: Twenty-eight articles were included with sample sizes ranging from 30 to 62,754 participants. Increasing daily sedentary time was adversely associated with body mass index (Hedge g: 0.32; P = .001), waist circumference (Hedge g: 0.45; P < .001), body fat percentage (Hedge g: 0.61; P = .012), and fat mass (Hedge g: 0.30; P = .018). There were also unfavorable associations with systolic blood pressure (Hedge g: 0.37; P = .047), blood glucose (Hedge g: 0.30; P = .044), triglycerides (Hedge g: 0.36; P = .039), and high-density lipoprotein cholesterol (Hedge g: 0.34; P = .034). Conclusions: Increased daily sedentary time is adversely associated with body composition, systolic blood pressure, and blood biomarkers in older adults. Therefore, limiting sedentary behavior should be considered an important target in this population group for improved cardiometabolic health

The Effectiveness of Physical Activity and Nutrition Interventions for Children and Adolescents With Cerebral Palsy to Improve Physical Health and Cognitive Outcomes: A Systematic Review

Purpose: Using systematic review methodology, we set out to describe the evidence for physical activity and nutrition interventions for children and adolescents with cerebral palsy (CP) as compared with no intervention or exposure that reports physical health and cognitive outcomes. Method: Quantitative, primary studies that explored the effectiveness of these interventions, replicable in school and home contexts, in comparison to any other or no intervention or exposure in children and adolescents between the ages of 6–18 years old with a diagnosis of cerebral palsy were included (PROSPERO CRD42022322143). Risk of bias was assessed by Joanna Briggs Institute and QualSyst. Results: A total of 16 international heterogeneous studies (13 physical activity and 3 nutrition) with interventions ranging from a single exposure to 8 months, with quality 58% to 89% and effectiveness, D = 0.03 to 0.97, were included. Outcome measures were varied. Conclusion: The review brings together a number of high-quality studies on physical activity and nutrition interventions and promising findings of impact on cardiovascular, musculoskeletal, and cognitive outcomes. Evidence supports implementation of these interventions in community contexts. Future research would benefit from agreement on the use of core outcome measures for meta-synthesis

Privacy-Preserving Distributed Energy Management for Battery Energy Storage Systems over Time-Varying Networks

This article addresses the privacy-preserving energy management problem of battery energy storage systems (BESSs). An autonomous privacy-preserving distributed optimization (APPDO) scheme is developed over time-varying networks with the aim of regulating the power output of local BESS to fulfill the total load demand at the minimum economic cost under battery capacity constraints without privacy leakage. To this end, a linearly convergent distributed algorithm is proposed by combining the gradient descent algorithm with leaderless and leader-following consensus schemes. This algorithm is applicable to both islanded and grid-connected modes of BESSs. Furthermore, a novel privacy-preserving approach is constructed by injecting well-designed perturbation sequences into the data exchanged between neighboring nodes, making it effective against malicious eavesdroppers. Furthermore, a comprehensive analysis framework is established to evaluate the convergence, optimality, and privacy-preserving performance of the APPDO algorithm. Finally, numerical studies are conducted to demonstrate the effectiveness of the developed APPDO scheme.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62303210 and 62188101); Shenzhen Science and Technology Program (Grant Number: JCYJ20241202125309014 and KQTD20221101093557010); 10.13039/501100012245-Science and Technology Planning Project of Guangdong Province (Grant Number: 2024B1212010002); Future Resilient Systems at the Singapore-ETH Centre; 10.13039/501100000266-Engineering and Physical Sciences Research Council; Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Multi-sensor Particle Filtering for Nonlinear Complex Networks With Heterogeneous Measurements Under Non-Gaussian Noises

In this article, the multisensor particle filtering problem is investigated for a class of nonlinear complex networks with multirate heterogeneous measurements. The underlying complex networks are subject to non-Gaussian noises and randomly switching couplings, while the multirate heterogeneous measurements (including fast-rate binary measurements and slow-rate integral measurements) are transmitted to remote filters via imperfect wireless communication channels. Both the deterministic and stochastic channel gains, along with possible transmission failures, are taken into account to characterize the properties of wireless communication channels. The purpose of this article is to propose a channel-related filtering scheme in the particle filtering framework to address these engineering-oriented complexities. To achieve this, a mixture distribution is established to reflect the effects of randomly switching couplings and generate new particle candidates. By utilizing the Monte Carlo approximation method, two types of update expressions for importance weights are explicitly derived based on the channel properties and the likelihood functions. Finally, numerical simulations are presented to demonstrate the viability and effectiveness of the proposed particle filtering algorithms.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62203016, 62425301, U2241214, 62373008 and 61933007); 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021TQ0009); 10.13039/501100001809-Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Design of the ECCE Detector for the Electron Ion Collider

The file archived on this institutional repository is a preprint version of the article submitted to Nuclear Instruments and Methods A is available at arXiv:2209.02580v3 [physics.ins-det], https://arxiv.org/abs/2209.02580 ([v3] Sat, 20 Jul 2024 16:48:46 UTC (40,987 KB)). It has not been certified by peer review.The EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been designed to address the full scope of the proposed Electron Ion Collider (EIC) physics program as presented by the National Academy of Science and provide a deeper understanding of the quark–gluon structure of matter. To accomplish this, the ECCE detector offers nearly acceptance and energy coverage along with excellent tracking and particle identification. The ECCE detector was designed to be built within the budget envelope set out by the EIC project while simultaneously managing cost and schedule risks. This detector concept has been selected to be the basis for the EIC project detector.We acknowledge support from the Office of Nuclear Physics in the Office of Science in the Department of Energy, the National Science Foundation, USA, and the Los Alamos National Laboratory Directed Research and Development (LDRD) 20200022DR. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The work of AANL group are supported by the Science Committee of RA, in the frames of the research project # 21AG-1C028. And we gratefully acknowledge that support of Brookhaven National Lab and the Thomas Jefferson National Accelerator Facility which are operated under contracts DE-SC0012704 and DE-AC05-06OR23177 respectively

Testing and estimating structural breaks in time series and panel data in Stata

Supplementary Material is available online under a Creative Commons License at: https://journals.sagepub.com/doi/10.1177/1536867X251365449#supplementary-materials .Howto install: The latest version of the xtbreak package can be obtained by typing the following in Stata: net from https://janditzen.github.io/xtbreak/ Updates and further documentation can be found on GitHub.A preprint version of the article is available at arXiv:2110.14550v3 [econ.EM], https://arxiv.org/abs/2110.14550 ([v3] Wed, 22 Jan 2025 09:53:16 UTC (538 KB)). It has not been certified by peer review.Identifying structural change is a crucial step when analyzing time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed because of major disruptive events such as the 2007–2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the existence of breaks and dating them is therefore necessary for not only estimation but also understanding drivers of change and their effect on relationships. In this article, we introduce a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing multiple structural breaks in time series and panel data. xtbreak can detect the existence of breaks, determine their number and location, and provide break-date confidence intervals. We use xtbreak in examples to explore changes in the relationship between COVID-19 cases and deaths in the US using both aggregate and state-level data and in the relationship between approval ratings and consumer confidence using a panel of eight countries.Ditzen acknowledges financial support from Italian Ministry MIUR under the PRIN project Hi-Di NET- Econometric Analysis of High Dimensional Models with Network Structures in Macroeconomics and Finance (grant 2017TA7TYC). Westerlund acknowledges financial support from the Knut and Alice Wallenberg Foundation through a Wallenberg Academy Fellowship

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62132004); Jiangsu Major Project on Basic Researches (Grant Number: BK20243059)

Unlocking Business Success: How Networking and Branding Capabilities Drive Performance Through Product Innovativeness

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.In today's fast-paced market, developing innovative products with significant advantages over existing alternatives is essential for a strong market presence. This study, based on the resource-based and dynamic capability view, examines how market and technological innovativeness contribute to differentiation advantage and improved business performance. It also investigates the roles of complementary capabilities in enhancing these relationships. Primary data were collected through an on-site questionnaire survey of Iranian research and development-intensive manufacturing firms. Using 125 valid responses from senior managers, partial least squares structural equation modeling tested the proposed model. Findings indicate that networking and branding capabilities enhance technological and market innovativeness, respectively, thereby strengthening differentiation advantage. Moreover, differentiation advantage is a crucial mechanism for translating innovativeness into improved business performance. These results provide theoretical insights and practical guidance for developing effective product innovativeness strategies to augment international competitiveness and performance

Collecting real-time infant feeding and support experience: co-participatory pilot study of mobile health methodology

Data availability: The quantitative dataset supporting the conclusions of this article is available in the OSF project https://osf.io/yqsnd/ [https://doi.org/10.17605/OSF.IO/YQSND].Supplementary Information is available online at: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-025-00707-7#Sec31 .Background: Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges. Methods: In April–May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot. Results: Participants (n = 14) appreciated the feature’s simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women’s enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation. Conclusions: Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.We acknowledge the funding by the BA/Wellcome Trust small grants for supporting this project (reference SRG2021/210128)