Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries—loop and patch—through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the tradeoff between thermal robustness and mechanical responsiveness. A patch-type FSS strain sensor was designed, fabricated, and characterized under varying temperature and strain. The sensor achieves a strain sensitivity of ~150 MHz per 1%<inline-formula> <tex-math notation="LaTeX">${\varepsilon }_{l}$ </tex-math></inline-formula>, while temperature-induced drift is limited to ~12 MHz over a 200°C range, confirming the effectiveness of the intrinsic compensation strategy. The results provide valuable insights for optimizing FSS-based sensor design in structural health monitoring applications and balancing thermal stability with mechanical sensitivity to ensure reliable performance in thermally dynamic environments.
Instruments and machines, Electrical engineering. Electronics. Nuclear engineering
The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 fuzzy control to accurately handle their inherent fuzziness and environmental disturbances in complex environments. To address this, this paper introduces the backstepping control method based on Type-2 T-S fuzzy control, incorporating the niche situation function as the consequent of the T-S backstepping fuzzy control. The stability analysis of the system is completed by constructing a Lyapunov function, and the adaptive law for the parameters of the niche situation function is derived. This design reflects the tendency of biological individuals to always develop in a direction beneficial to themselves, highlighting the bio-inspired intelligent characteristics of the proposed method. The results of case simulations show that the Type-2 backstepping T-S fuzzy control has significantly superior comprehensive performance in dealing with the complexity and uncertainty of high-order niche situation systems compared with the traditional Type-1 control and Type-2 T-S adaptive fuzzy control. These results not only verify the adaptive and self-development capabilities of biological individuals, as well as their efficiency in environmental utilization, but also endow this control method with a solid practical foundation.
Leonhard Ziegler, Michael Grabatin, Daniela Pöhn
et al.
Abstract While self-sovereign identities (SSI) have been gaining more traction, the topic of SSI security has yet to be addressed. Especially regarding response procedures to security incidents, no prior work is available. However, incident response processes are essential to systematically respond to a security incident in a timely manner. We first evaluate the current state-of-the-art by conducting a literature survey and contacting organizations that offer SSI. The insights underpin the subject’s relevance, highlighting that incident response capabilities are just starting to be developed. Contributing to this development, we identify the challenges of building a security incident response process for SSI. Mainly, the decentralized nature inhibits the utilization of known best practices, which all focus on building a centralized incident response capability. However, even in the case of SSI, some centralized entities may exist. Therefore, we design two variants of SIR processes: one more centralized and one more decentralized. For the latter, the problem size is reduced in the first step by identifying all the stakeholders within an SSI ecosystem and then analyzing possible proactive and reactive measures each participant can access. This procedure leads to the grouping of SSI system participants into three distinct domains of incident response. For each domain, different capabilities for handling incidents are introduced depending on the involved stakeholders, their infrastructure, and their goals. To demonstrate the procedures, incident scenarios for each domain highlight the workflows during incident handling.
This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process. Moreover, to extract facial emotional features more accurately, the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism. This module can deeply mine the dependency between channels and spaces. It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy. The study optimizes UniRepLKNet, a universal large kernel Convolutional Neural Network (CNN), and proposes the EmoRepLKNet neural network structure. In EmoRepLKNet, the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition. Combined with the wide receptive field of large kernel CNN, facial emotions can be recognized effectively. Experimental results show that on the facial emotion recognition dataset FER2013, EmoRepLKNet achieves an accuracy of 76.20%, outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet. Additionally, on the single-label portion of the RAF-DB dataset, the proposed method achieves an accuracy of 89.67%.
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu
et al.
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural operators with denoising diffusion probabilistic models to reconstruct high-resolution ocean states from extremely sparse Lagrangian observations. By conditioning the generative model on neural operator outputs, the framework accurately captures small-scale, high-wavenumber dynamics even at 99% sparsity (for synthetic data) and 99.9% sparsity (for real satellite observations). We validate our method on benchmark systems, synthetic float observations, and real satellite data, demonstrating robust performance under severe spatial sampling limitations as compared to other deep learning baselines.
Abstract Matrix geometric means between two positive definite matrices can be defined from distinct perspectives—as solutions to certain nonlinear systems of equations, as points along geodesics in Riemannian geometry, and as solutions to certain optimisation problems. We devise quantum subroutines for the matrix geometric means, and construct solutions to the algebraic Riccati equation—an important class of nonlinear systems of equations appearing in machine learning, optimal control, estimation, and filtering. Using these subroutines, we present a new class of quantum learning algorithms, for both classical and quantum data, called quantum geometric mean metric learning, for weakly supervised learning and anomaly detection. The subroutines are also useful for estimating geometric Rényi relative entropies and the Uhlmann fidelity, in particular achieving optimal dependence on precision for the Uhlmann and Matsumoto fidelities. Finally, we provide a BQP-complete problem based on matrix geometric means that can be solved by our subroutines.
Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.
Information technology, Electronic computers. Computer science
Matthew Dvorsky, Daniela Munalli, Mohammad Tayeb Al Qaseer
et al.
In this paper synthetic aperture radar (SAR) polarimetry techniques are applied to detect and characterize fiber misalignment in both carbon fiber sheets and glass fiber reinforced polymer (GFRP) composites. The principle behind SAR polarimetry technique to characterize fiber orientation is described, making use of the fact that carbon and glass fibers are polarizing when irradiated with a microwave signal. The difficulties in using 2D polarimetry techniques to make the 3D orientation measurements, required to characterize out-of-plane fiber misalignment, are discussed as well. Subsequently, the feasibility of a recently-developed 3D SAR polarimetry method for this purpose is demonstrated. Several carbon fiber sheet and glass fiber reinforced polymer (GFRP) samples were manufactured with both in- and out-of-plane fiber misalignment. Polarimetric SAR images of the samples were then produced to show the spatially-varying relative orientation (both in-plane and out-of-plane) of the fibers for each sample. These images can be used to both detect and characterize any fiber misalignment, successfully demonstrating the potential for SAR polarimetry as a tool for the inspection of carbon and glass fiber reinforced composites.
Instruments and machines, Electrical engineering. Electronics. Nuclear engineering
A Vehicular Ad-Hoc Network(VANET) is a Mobile Ad-Hoc Network(MANET) composed of mobile vehicular nodes.It does not rely on infrastructure to either establish a communication link orrealize communication. Owingto the high mobility of vehicles and limited wireless-communication resources, it is difficult for VANETs to guarantee Quality of Service(QoS).To solve this problem, this paper introduces a Software-Defined Network(SDN).In particular, amulti-constrained QoS routing algorithm suitable for Doftware-Defined Vehicular Ad-Hoc Network(SDN-VANET) is proposedthatharnessesthe advantages of SDN control and forwarding separation to ensurevehicle QoS.First, the SDN controller schedules a vehicle's service based on deadline constraints.Second, this paper proposesan Adaptive Hybrid Shuffled Frog-Leaping Algorithm(AH-SFLA).The SDN controller calculates the appropriate value of the data on the transmission link according to the QoS index and the global topology information and uses this as a benchmark to search for an optimized path.At the same time, alternative link mechanisms and QoS resource consumption thresholds are set to implement routing maintenance in order toreduce the probability of network failures.Finally, mininet-wifi and SUMO are combined to build an SDN-VANET environment, and the AH-SFLA routing algorithm is compared with the performances ofIGA and IICSFLA.The experimental results show that compared with IGA and IICSFL, AH-SFLA can improve the average end-to-end delay index by 57.74% and 46.6%, reduce the packet-loss rate by 29.9% and 18.6%, and increase the cost of standardized routing by 36.93% and 27.2%, respectively, effectively guaranteeingQoS in VANET.
We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all recent algorithmic advances (e.g. neural nets, forests). We show that the estimated effect model is robust to estimation errors in the auxiliary models, by showing that the loss satisfies a Neyman orthogonality criterion. Our approach can be used to estimate projections of the true effect model on simpler hypothesis spaces. When these spaces are parametric, then the parameter estimates are asymptotically normal, which enables construction of confidence sets. We applied our method to estimate the effect of membership on downstream webpage engagement on TripAdvisor, using as an instrument an intent-to-treat A/B test among 4 million TripAdvisor users, where some users received an easier membership sign-up process. We also validate our method on synthetic data and on public datasets for the effects of schooling on income.
ABSTRACT This paper intends to illustrate the didactic potential of Python-based Jupyter notebooks in teaching translation technology, machine translation in particular, to translation students. It discusses the basic makeup of Jupyter notebooks and shows how these notebooks can be set up for students who have had little to no prior exposure to the Python programming language. Then, the paper discusses the general didactic benefits of Jupyter notebooks for both students and lecturers in a translation studies context. It shows how students can interact with these notebooks, which translation technological and translation-oriented natural language processing (NLP) concepts can be taught using them and to what extent interaction with these notebooks can help students understand, in a very general way, some basic principles of (NLP-oriented) Python programming. Finally, the paper presents the results of a pilot study on the use of Jupyter notebooks in a machine translation course in an MA programme in specialised translation.
Raúl Comas Rodríguez, Sharon Dinarza Álvarez Gómez, Edgar Ramón Arredondo Domínguez
et al.
The subject “Education and Society” is part of the syllabus of the “Basic Education” program of the Autonomous University of the Andes, Ecuador. This subject has the complexity of linking different aspects of society, some of them not free from contradictions among themselves, and imprecision and uncertainties in assessing students’ performance. For this reason, the concepts and aspects to be evaluated on this subject are represented through the use of plithogenic numbers. Plithogenic sets were defined to model concepts arising from the dynamic interaction among other simpler ones, which may have contradictions with each other and include neutralities or indeterminacies. This research aims to propose a novel method for the evaluation of the subject “Education and Society” through the use of plithogenic numbers and their operators. The lecturers will be able to perform the evaluations with the use of natural language; in the same way, the results will be given using a linguistic scale, which will facilitate the understanding and representation of the evaluations. On the other hand, plithogenic numbers will allow capturing the complexity, imprecision, and uncertainty that lecturers may face in their evaluations.
The end of 2020 and the beginning of 2021 was a challenging time for many countries in Europe, as the combination of colder weather, holiday celebrations, and the emergence of more transmissible virus variants conspired to create a perfect storm for virus transmission across the continent. At the same time lockdowns appeared to be less effective than they were earlier in the pandemic. In this paper we argue that one contributing factor is that existing ways of communicating risk—case numbers, test positivity rates, hospitalisations etc.—are difficult for individuals to translate into a level of personal risk, thereby limiting the ability of individuals to properly calibrate their own behaviour. We propose an new more direct measure of personal risk, exposure risk, to estimate the likelihood that an individual will come into contact with an infected person, and we argue that it can play an important role, alongside more conventional statistics, to help translate complex epidemiological data into a simple measure to guide pandemic behaviour. We describe how exposure risk can be calculated using existing data and infection prediction models, and use it to evaluate and compare the exposure risk associated with 39 European countries.
Martin Jedwabny, Pierre Bisquert, Madalina Croitoru
Normative ethics has been shown to help automated planners take ethically aware decisions. However, state- of-the-art planning technologies don’t provide a sim- ple and direct way to support ethical features. Here, we propose a new theoretical framework based on a con- struct, called ethical rule, that allows to model prefer- ences amongst ethically charged features and capture various ethical theories. We show how the framework can model and combine the strengths of these theories. Then, we demonstrate that classical planning domains extended with ethical rules can be compiled into soft goals in PDDL.
Jason S. Hartford, Victor Veitch, Dhanya Sridhar
et al.
Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable "exclusion" assumptions that rule out any relationship between the instrument variable and the response that is not mediated by the treatment. In this paper, we show how to perform consistent IV estimation despite violations of the exclusion assumption. In particular, we show that when one has multiple candidate instruments, only a majority of these candidates---or, more generally, the modal candidate-response relationship---needs to be valid to estimate the causal effect. Our approach uses an estimate of the modal prediction from an ensemble of instrumental variable estimators. The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently. As such, it is compatible with recent machine-learning based estimators that allow for the estimation of conditional average treatment effects (CATE) on complex, high dimensional data. Experimentally, we achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators, including on a challenging simulated Mendelian Randomization problem.