The ECFA Higgs, electroweak, and top Factory Study ran between 2021 and 2025 as a broad effort across the experimental and theoretical particle physics communities, bringing together participants from many different proposed future collider projects. Activities across three main working groups advanced the joint development of tools and analysis techniques, fostered new considerations of detector design and optimisation, and led to a new set of studies resulting in improved projected sensitivities across a wide physics programme. This report demonstrates the significant expansion in the state-of-the-art understanding of the physics potential of future e+e- Higgs, electroweak, and top factories, and has been submitted as input to the 2025 European Strategy for Particle Physics Update.

Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed. Aim: This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and/or evaluated in a real-world context. Method: We perform a systematic mapping study to examine the literature on anomaly detection in cloud-based systems, particularly focusing on the usage of real-world monitoring data, with the aim of identifying key data categories, tools, data preprocessing, and anomaly detection techniques. Results: Based on a review of 104 papers, we categorize monitoring data by structure, types, and origins and the tools used for data collection and processing. We offer a comprehensive overview of data preprocessing and anomaly detection techniques mapped to different data categories. Our findings highlight practical challenges and considerations in applying these techniques in real-world cloud environments. Conclusion: The findings help practitioners and researchers identify relevant data categories and select appropriate data preprocessing and anomaly detection techniques for their specific operational environments, which is important for improving the reliability and performance of cloud-based systems.

Automatic modulation classification (AMC) is a key technique for identifying the modulation schemes of wireless signals, enabling improved performance and security in communication systems by accurately classifying signal types. However, most existing AMC research assumes modulation classes are part of a closed set, which can cause classifiers to misidentify unknown modulation schemes as known ones, undermining both the security and reliability of communication systems. To address this, we propose a novel open set AMC (OS-AMC) method based on deep metric learning and OpenMax (M-OpenMax). The proposed M-OpenMax-based OS-AMC method utilizes crossentropy loss and center loss to extract separable and discriminative signal features and uses OpenMax to adjust the nonnormalized score output of the model to achieve the classification of known signals and removal of unknown signals. Experimental results demonstrate that the proposed M-OpenMax-based OSAMC method outperforms other open-set AMC techniques, particularly in its ability to handle unknown modulation types.

Radio frequency fingerprinting (RFF) presents a promising solution for advancing specific emitter identification (SEI) methods, which are crucial for securing the Internet of Things (IoT). While deep learning (DL)-based SEI approaches have demonstrated strong potential, they heavily depend on large, labeled datasets, which are often difficult to obtain in real-world scenarios. This reliance limits the robustness of existing SEI methods. To overcome this challenge, we propose a robust few-shot SEI (FS-SEI) method leveraging multi-view feature fusion with attention (MFFA). By integrating interpretable signal processing (SP) features with DL features and incorporating an attention mechanism for adaptive multi-view fusion, the proposed approach enhances both identification accuracy and robustness in few-shot scenarios. Experimental results validate the effectiveness of the method, showing consistent robustness under noisy conditions and significant gains in identification accuracy. These findings highlight its strong potential for practical applications in dynamic and challenging environments.

This study investigates the influence of specific surface area (SSA) and aluminum hydroxide particle size on sodium aluminate’s purification efficiency in the Bayer process. This research examines how variations in SSA affect the adsorption and incorporation of contaminants such as Cu, Fe, and Zn, as well as the optimal balance between effective purification and excessive Al2O3 loss. Different SSA values and purification durations are analyzed to optimize the purification process and determine conditions that maximize impurity removal while maintaining system stability. Additionally, solid residue characterization using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) provides insights into impurity incorporation mechanisms, including isomorphic replacement, surface adsorption, and co-crystallization. This study highlights key process parameters that influence impurity behavior and crystallization dynamics, offering valuable guidance for refining industrial purification strategies and improving aluminum hydroxide quality.

Abstract Background Humans spend up to 90% of their time indoors and are exposed to a significant number of microbes in their homes, which can have important implications for their health. Aim This study focused on analysing the influence of environmental factors on microbiome diversity and abundance in bed dust and linking the exposure to dust bacteria with asthma. Subjects and methods A total of 90 dust samples were collected from homes of asthmatic patients (n = 59) and controls (n = 31) aged 5–18 years. The bacterial fraction of the microbiome was analysed using 16S rRNA gene high-throughput sequencing on the Illumina MiSeq platform and downstream analyses in QIIME2 and R. Microbiome profiles were associated with asthma and relevant environmental and household data. Results Higher bacterial β-diversity in the environment was shown to be inversely associated with asthma (p = 0.009). Also, living environment (p = 0.002), housing type (p = 0.004), presence of pets in the household (p = 0.001), and cleaning practices (p = 0.006 for dusting and p = 0.011 for vacuuming) were prominent environmental factors affecting the bed dust microbiome. Conclusion Our results suggest significant differences in bacterial community composition between individuals with and without asthma and the interaction between indoor microbiome and asthma is mediated by environmental factors in the household.

English has assumed the role of a global business lingua franca (BELF) at the turn of the 21st century, with an ever-increasing number of multinational corporations (MNCs) adopting English as either their official corporate language, or, the working language as a natural byproduct of a company’s linguascape. This paper investigates the use of English in a business context drawing from the BELF paradigm, i.e. it sets out to compare and contrast the frontstage and backstage English in a multinational organization in written (email) communication, as an answer to a call by Kankaanranta et al. (2018), as this specific kind of study within this genre is still underrepresented and under-researched within the Global South setting. The emails used in this study were collected from a small-sized Turkish-Bosnian international company based in B&H with a total of 10 employees. The approach adopted for the analysis of the study is discourse-analytical in its essence, supported by corpus analysis instruments. The analysis showed that the backstage English, primarily used among employees for internal communication, is indeed in most cases characterized by BELF features. Conversely, frontstage English, was shown to be aligned more closely with native English norms due to its role in corporate branding and external communication, although showing some variability as well. It is expected that the results of the study will help in understanding English communication nuances within this particular business context and help businesses foster clearer, more effective interactions across linguistic and cultural boundaries.

The development of smart grids poses great challenges to the scientific and professional community. Increasingly strict requirements from regulators and consumers require appropriate actions from the Distribution System Operator (DSO), infrastructure development, and large investments in the modernization and digitalization of electrical distribution systems. The connection of a large number of electricity sources to the existing distribution grid causes problems that are reflected in unauthorized voltage changes or overloads in the network, as well as compromised power quality. Communication infrastructure, as well as the technologies themselves, are often not satisfactory for the requirements that arise in real networks, and the development of smart grids requires appropriate/advanced information and communication infrastructure. The development of smart grids requires an interdisciplinary approach, experts of different profiles, and clearly defined long-term strategies. This paper provides an overview of existing AI technologies which are proposed for application in power systems, as well as an overview of areas where AI can be implemented to support the operation of power systems in the future (such as maintenance, forecasting, optimization, protection, etc.). In a separate section, a simulation of the production of small PV systems connected to consumer households in weak low-voltage grids (LVG) is presented as an illustrative example. An overview of proposed AI applications in LVGs is provided, along with a discussion of possible improvements and overcoming issues that arise in existing grids with prosumers.

The paper deals with a simplified procedure for the assessment of steady-state temperature in a single-layer parallelepiped human body model exposed to electromagnetic (EM) radiation using analytical approach. The related bioheat transfer equation variant is solved analytically using the classical theory of ordinary differential equations. The results are given for the case of constant power density and for power density exponentially decreasing with the tissue depth. The proposed approach leads to the overestimation of steady-state temperature in the body exposed to EM radiation, compared to other analytical methods.

Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0\% (0.175\,ms vs.\ 0.318\,ms), achieves comparable accuracy ($-16.78$\,dB vs.\ $-17.30$\,dB), and requires $8\times$ fewer parameters (0.11M vs.\ 0.88M), demonstrating its suitability for low-latency, real-time deployment.

The rapid growth of clinical explainable AI (XAI) models raised concerns over unclear purposes and false hope regarding explanations. Currently, no standardised metrics exist for XAI evaluation. We developed a clinician-informed, 14-item checklist including clinical, machine and decision attributes. This is the first step toward XAI standardisation and transparent reporting XAI methods to enhance trust, reduce risks, foster AI adoption, and improve decisions to determine the true clinical potential of applied XAI.