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Search for singly produced vectorlike top partners in multilepton final states with 139 fb −1 of 𝑝𝑝 collision data at √𝑠 =13 TeV with the ATLAS detector
A search for the single production of a vectorlike top partner (𝑇) with mass greater than 1 TeV decaying into a 𝑍 boson and a top quark is presented, using the full Run 2 dataset corresponding to 139 fb −1 of 𝑝𝑝 collisions at √𝑠 =13 TeV, collected in 2015–2018 with the ATLAS detector at the Large Hadron Collider. The targeted final state is characterized by the presence of a pair of electrons or muons with opposite-sign charges which form a 𝑍-boson candidate, as well as by the presence of 𝑏-tagged jets and forward jets. Events with exactly two or at least three leptons are categorized into two independently optimized analysis channels. No significant excess above the background expectation is observed and the results from the two channels are statistically combined to set exclusion limits at 95% confidence level on the masses and couplings of 𝑇. The results are interpreted in several benchmark scenarios to set limits on the mass and universal coupling strength (𝜅) of the vectorlike quark. For singlet 𝑇 quarks, 𝜅 values between 0.22 and 0.64 are excluded for masses between 1000 and 1975 GeV. For 𝑇 quarks in the doublet scenario, where the production cross section is much lower, 𝜅 values between 0.54 and 0.88 are excluded for masses between 1000 and 1425 GeV.
Life cycle of streaks in the buffer layer of wall-bounded turbulence
In this work, streaks in the buffer layer of wall-bounded turbulence are tracked in time to study their life cycle. Spatially and temporally resolved direct numerical simulation data are used to analyze the strong wall-parallel movements conditioned to low-speed streamwise flow. The analysis of the streaks shows that there is a clear distinction between wall-attached and detached streaks, and that the wall-attached streaks can be further categorized into streaks that are contained in the buffer layer and the ones that reach the outer region. The results reveal that streaks are born in the buffer layer, coalescing with each other to create larger streaks that are still attached to the wall. Once the streak becomes large enough, it starts to meander due to the large streamwise-to-wall-normal aspect ratio, and consequently the elongation in the streamwise direction, which makes it more difficult for the streak to be oriented strictly in the streamwise direction. While the continuous interaction of the streaks allows the superstructure to span extremely long temporal and length scales, individual streak components are relatively small and short-lived. Tall-attached streaks eventually split into wall-attached and wall-detached components. These wall-detached streaks have a strong wall-normal velocity away from the wall, similar to ejections or bursts observed in the literature. Conditionally averaging the flow fields to these split events show that the detached streak has not only a larger wall-normal velocity compared to the wall-attached counterpart, it also has a larger (less negative) streamwise velocity, similar to the velocity field at the tip of a vortex cluster.
CsV 3 Sb 5 : A $\mathbb{Z}_2$ Topological Kagome Metal with a Superconducting Ground State
Recently discovered alongside its sister compounds KV 3 Sb 5 and RbV 3 Sb 5 , CsV 3 Sb 5 crystallizes with an ideal kagome network of vanadium and antimonene layers separated by alkali metal ions. This work presents the electronic properties of CsV 3 Sb 5 , demonstrating bulk superconductivity in single crystals with a T c = 2.5 K. The normal state electronic structure is studied via angle-resolved photoemission spectroscopy and density-functional theory, which categorize CsV 3 Sb 5 as a Z 2 topological metal. Multiple protected Dirac crossings are predicted in close proximity to the Fermi level (E F ), and signatures of normal state correlation effects are also suggested by a high-temperature charge density wavelike instability. Finally, the implications for the formation of unconventional superconductivity in this material are discussed.
Strong Constraints on Jet Quenching in Centrality-Dependent p + Pb Collisions at 5.02 TeV from ATLAS
Jet quenching is the process of color-charged partons losing energy via interactions with quark-gluon plasma droplets created in heavy-ion collisions. The collective expansion of such droplets is well described by viscous hydrodynamics. Similar evidence of collectivity is consistently observed in smaller collision systems, including p p and p + Pb collisions. In contrast, while jet quenching is observed in Pb + Pb collisions, no evidence has been found in these small systems to date, raising fundamental questions about the nature of the system created in these collisions. The ATLAS experiment at the Large Hadron Collider has measured the yield of charged hadrons correlated with reconstructed jets in 0.36 nb -1 of p + Pb and 3.6 pb -1 of pp collisions at 5.02 TeV. The yields of charged hadrons with ${p}^{ch}_{T}$ > 0.5 GeV near and opposite in azimuth to jets with ${p}^{jet}_{T}$ > 30 or 60 GeV, and the ratios of these yields between p + Pb and pp collisions, I pPb , are reported. The collision centrality of p + Pb events is categorized by the energy deposited by forward neutrons from the struck nucleus. The I pPb values are consistent with unity within a few percent for hadrons with ${p}^{ch}_{T}$ > 4 GeV at all centralities. These data provide new, strong constraints that preclude almost any parton energy loss in central p + Pb collisions.
Search for an eV-Scale Sterile Neutrino Using Improved High-Energy νμ Event Reconstruction in IceCube
This Letter presents the result of a 3+1 sterile neutrino search using 10.7 yr of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and throughgoing events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at sin^{2}(2θ_{24})=0.16 and Δm_{41}^{2}=3.5 eV^{2}, which agrees with previous iterations of this Letter. The result is consistent with the null hypothesis of no sterile neutrinos with a p value of 3.1%.
Retrieving Top-k Hyperedge Triplets: Models and Applications
Complex systems frequently exhibit multi-way, rather than pairwise, interactions. These group interactions can- not be faithfully modeled as collections of pairwise interactions using graphs and instead require hypergraphs. However, methods that analyze hypergraphs directly, rather than via lossy graph reductions, remain limited. Hypergraph motifs hold promise in this regard, as motif patterns serve as building blocks for larger group interactions which are inexpressible by graphs. Recent work has focused on categorizing and counting hypergraph motifs based on the existence of nodes in hyperedge intersection regions. Here, we argue that the relative sizes of hyperedge inter- sections within motifs contain varied and valuable information. We propose a suite of efficient algorithms for finding top-k triplets of hyperedges based on optimizing the sizes of these intersection patterns. This formulation uncovers interesting local patterns of interaction, finding hyperedge triplets that either (1) are the least similar with each other, (2) have the highest pairwise but not groupwise correlation, or (3) are the most similar with each other. We formalize this as a combinatorial optimization problem and design efficient algorithms based on filtering hyperedges. Our comprehensive experimental evaluation shows that the resulting hyperedge triplets yield insightful information on real-world hypergraphs. Our approach is also orders of magnitude faster than a naive baseline implementation.
Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures
The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.
Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults
Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event data with appropriate labeling, an unsupervised approach is highly valuable. This approach enables using event data without labeling, which is far easier to obtain. This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data is used to train the CVAE that identifies different faults as different clusters in the latent space. The clusters are then used as the foundation model to categorize the real-world data.
Power System Resilience Evaluation Framework and Metric Review
Power system resilience has been an emerging hot topic in recent years to investigate the increasing threats of extreme events, such as natural disasters, severe weather, and cyberattacks. Although much research has been done to define, model, and quantify resilience from different aspects, the lack of universally accepted evaluation methods and resilience metrics makes it difficult to assess and compare resilience across different power systems, such as what is typically done in power system reliability studies. In this paper, first, we review the definitions of resilience, and we summarize two core concepts shared by most of the literature. Then, we develop a new framework to assess power system resilience from two perspectives - i.e., pre-event estimation and post-event evaluation - to capture system resilience performance in both general and specific fashions. We conduct a thorough review of existing resilience metrics and categorize them using the proposed framework, where recommendations are also proposed to capture core concepts of resilience.
Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge
Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.
Bidirectional Three-phase Current Source Converter based Buck-boost AC/DC System using Bidirectional Switches
The 1.2 kV 4H-SiC BiDirectional Field Effect Transistor (BiDFET) is the first monolithic SiC bidirectional switch, which offers a lower voltage drop and semiconductor devices count alternative to the reverse-voltage-blocking (RB) switch used in the current-source converters (CSC). The bidirectional switch based CSC also allows DC-link current reversal for bidirectional power flow and provides multiple system-level benefits in a buck-boost AC/DC system consisting of buck-type DC/DC converter and CSC. This paper discusses the selection of buck converter duty cycle and CSC modulation index for the system’s buck-boost operation with a wide variation in DC voltage. CSC modulation schemes categorized based on the number of hard-turn-on transitions per switching cycle are also analyzed along with the three-step and four-step commutation schemes that are essential for the CSC commutation cells. Finally, the different schemes are evaluated and compared through the experimental results of a 10 kW, 480 VRMS, LL/ (400 − 800) V AC/DC system.
Envisioning the Future Renewable and Resilient Energy Grids—A Power Grid Revolution Enabled by Renewables, Energy Storage, and Energy Electronics
Today’s power grids are facing tremendous challenges because of the ever-increasing power demand, system complexity, infrastructure cost, knowledge base, and policy and regulatory issues to achieve supply–demand power balance and resiliency with respect to more frequent extreme weather events and cyberattacks. It is particularly challenging when the transition toward 100% intermittent renewable energy sources is considered. Many countries are calling for building up more transmission and distribution lines to increase power delivery capacities. This article is an attempt to answer two urgent questions: Is more transmission and distribution infrastructure really needed to meet the increasing power demand? What kind of future grid infrastructure should we envision and build? This article attempts to answer these questions and proposes the concept of community-centric asynchronous renewable and resilient energy grids. By clearly differentiating the concepts of grid resilience and reliability, the importance of building resilient power electronics’ devices and robust system-level control algorithms to achieve 100% renewable energy integrated resilient grids is presented. To identify the shortcomings and propose advancements, power electronics’ technologies are categorized using the proposed concepts of natural source frequencies (NSf), energy storage, direct energy conversion/control and fault protection (DeCaFp), and high-efficiency energy consumption and buffering (heECaB) technology. The ability of networked microgrids to greatly reduce power outages and power system restoration time is demonstrated by leveraging robust decentralized and centralized control algorithms, identified through a comprehensive literature review. Future research areas are proposed to further enhance grid stability, controllability, cybersecurity, and protection against faults in the presence of 100% renewable sources by leveraging the advanced capabilities of NSf, DeCaFp, and heECaB devices and system-level control algorithms.
DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions
Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.
Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis
The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.
Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery
Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.
Vision Foundation Models in Remote Sensing: A survey
Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.
A Comprehensive Review of Permanent Magnet Transverse Flux Machines: Use in Direct-Drive Applications
The use of direct-drive machines in renewable and industrial applications is increasing at a rapid rate. Transverse flux machines (TFMs) are ideally suited for direct-drive applications due to their high torque density. This article presents a comprehensive review of permanent magnet (PM) TFMs for direct-drive applications. It introduces TFMs and their operating principle and reviews the different types of TFMs proposed in the literature. TFMs are categorized by the number of stator sides, types of stator cores, and magnet arrangement in the rotor. The review covers different design topologies, materials used for manufacturing, structural and thermal analyses, modeling, design optimization, and cogging torque minimization in TFMs. The article also examines various applications and comparisons for TFMs presented in the literature.