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
Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan
et al.
This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.
Seola Lee, Andrew Akerson, Roham Pardakhtim
et al.
Wearable electronics are emerging as essential tools for health monitoring, haptic feedback, and human-computer interactions. While stable contact at the device-body interface is critical for these applications, it remains challenging due to the skin's softness, roughness, and mechanical variability. Existing methods, such as grounding structures or adhesive tapes, often suffer from contact loss, limited repeatability, and restrictions on the types of electronics they can support. Suction-based adhesives offer a promising alternative by generating negative pressure without requiring tight bands or chemical adhesives. However, most existing cup designs rely on rigid-surface assumptions and overlook mechanical interactions between suction cups and skin. Inspired by traditional cupping therapies, we present a suction-based adhesive system that attaches through elastic deformation and recovery. Using analytical modeling, numerical simulations, and experiments, we present a mechanics-based framework showing how suction performance depends on cup geometry, substrate compliance, and interfacial adhesion. We show that cup geometry should be tailored to substrate stiffness. Wide, flat suction cups perform well on rigid surfaces but fail on soft ones like skin due to substrate intrusion into the chamber. Narrow and tall domes better preserve recoverable volume and generate stronger suction. To improve sealing on rough, dry skin, we introduce a soft, tacky interfacial layer informed by a contact mechanics model. Using our design principles for skin suction adhesives, we demonstrate secure attachment of rigid and flexible components including motion sensors, haptic actuators, and electrophysiological electrodes across diverse anatomical regions. These findings provide a fundamental basis for designing the next generation of skin-friendly adhesives for wearable electronics.
This study proposes a semantic pipeline designed to generate domain-oriented and contextually relevant hypotheses by analyzing existing literature on mHealth applications in India. Using a corpus of mHealth texts, the framework extracts hidden semantics through TF-IDF, topic modeling, and contextual mapping with domain ontologies. It then employs prompt-based interactions with large language models (LLMs) to systematically generate and validate hypotheses aligned with identified topic-concept relationships. The results demonstrate the framework’s effectiveness in producing high-quality, structured hypotheses, as validated by expert ratings ranging from 4.2 to 4.6. Most hypotheses were found to be plausible or highly plausible, with low semantic redundancy indicating diversity across topics, except in stakeholder-related areas which showed moderate overlap. Although the inclusion of semantic augmentation increased processing time, it significantly enhanced interpretability and validity. The high lexical density observed (up to 0.90) further reflects the linguistic flexibility of the generated hypotheses. This approach underscores the potential of computational methods in automating hypothesis generation and enabling data-driven discoveries in the mHealth domain.
Rahatara Ferdousi, M. Anwar Hossain, Chunsheng Yang
et al.
A Digital Twin (DT) replicates objects, processes, or systems for real-time monitoring, simulation, and predictive maintenance. Recent advancements like Large Language Models (LLMs) have revolutionized traditional AI systems and offer immense potential when combined with DT in industrial applications such as railway defect inspection. Traditionally, this inspection requires extensive defect samples to identify patterns, but limited samples can lead to overfitting and poor performance on unseen defects. Integrating pre-trained LLMs into DT addresses this challenge by reducing the need for vast sample data. We introduce DefectTwin, which employs a multimodal and multi-model (M^2) LLM-based AI pipeline to analyze both seen and unseen visual defects in railways. This application enables a railway agent to perform expert-level defect analysis using consumer electronics (e.g., tablets). A multimodal processor ensures responses are in a consumable format, while an instant user feedback mechanism (instaUF) enhances Quality-of-Experience (QoE). The proposed M^2 LLM outperforms existing models, achieving high precision (0.76-0.93) across multimodal inputs including text, images, and videos of pre-trained defects, and demonstrates superior zero-shot generalizability for unseen defects. We also evaluate the latency, token count, and usefulness of responses generated by DefectTwin on consumer devices. To our knowledge, DefectTwin is the first LLM-integrated DT designed for railway defect inspection.
Lorenzo Giannessi, Franck Cadoux, Sebastien Cap
et al.
T2K is a long baseline neutrino experiment, entering Phase II with a Near Detector upgrade. The T2K near detector (ND280) upgrade consists of the installation of three new detector systems: a plastic scintillator neutrino active target (Super-FGD), two time projection chambers (HA-TPC) and a time of flight detector (TOF). The Super-FGD is composed of 2-million 1 cm-cube scintillating cubes read by almost 60 thousand wavelength-shifting (WLS) fibers coupled to an MPPC on one end. Given the large number of channels, the limited space inside magnetic environment, and the limited time from production to installation, the development and testing of the Front-end electronics boards (FEB) for the read-out of the Super-FGD channels represented a challenging task for the success of the upgrade. This work presents the performance tests confirming that the FEB aligns with detector requirements, and the hardware qualification of 240 FEBs through a custom QC test bench designed to detect and locate hardware failures to speed up the repairing process. Installation of the electronics in the detector took place in March 2024, one year after the beginning of the FEB mass production, and the first successful neutrino beam run took place in June of the same year.
Wakefield acceleration methods are known due to some their advantages. The main of them is the high accelerating gradient up to several teravolts per meter. In the paper another important advantage is concluded to the possibility of using a wakefield accelerator as a source of electrons by means of obtaining self injected bunches and their accelera-tion. The result is the simulation of the process of plasma wakefield excitation by a laser pulse with an energy of tens of mJ and a power of 1-2 TW for obtaining the promising electron source. Homogeneous and Gaussian plasma profiles were investigated and compared to increase the energy of the self-injected bunches. The laser parameters were taken that corresponded to the parameters of the laser setup in the Institute of Plasma Electronics and New Methods of Acceleration of the National Scientific Center "Kharkiv Institute of Physics and Technology". Based on the results of the simulation, the possibility of obtaining relativistic self-injected bunches that can be used for further laser acceler-ation experiments, including dielectric laser acceleration, was demonstrated.
Judith Owokuhaisa, Eleanor Turyakira, Frank Ssedyabane
et al.
Abstract Background Cervical cancer continues to threaten women’s health, especially in low-resource settings. Regular follow-up after screening and treatment is an effective strategy for monitoring treatment outcomes. Consequently, understanding the factors contributing to patient non-attendance of scheduled follow-up visits is vital to providing high-quality care, reducing morbidity and mortality, and unnecessary healthcare costs in low-resource settings. Methods A descriptive qualitative study was done among healthcare providers and patients who attended the cervical cancer screening clinic at Mbarara Regional Referral Hospital in southwestern Uganda. In-depth interviews were conducted using a semi-structured interview guide. Interviews were audio-recorded, transcribed verbatim, and thematically analysed in line with the social-ecological model to identify barriers and facilitators. Results We conducted 23 in-depth interviews with 5 healthcare providers and 18 patients. Health system barriers included long waiting time at the facility, long turnaround time for laboratory results, congestion and lack of privacy affecting counselling, and healthcare provider training gaps. The most important interpersonal barrier among married women was lacking support from male partners. Individual-level barriers were lack of money for transport, fear of painful procedures, emotional distress, and illiteracy. Inadequate and inaccurate information was a cross-cutting barrier across the individual, interpersonal, and community levels of the socio-ecological model. The facilitators were social support, positive self-perception, and patient counselling. Conclusions Our study revealed barriers to retention in care after cervical cancer screening, including lack of partner support, financial and educational constraints, and inadequate information. It also found facilitators that included social support, positive self-perception, and effective counselling.
Gynecology and obstetrics, Public aspects of medicine
Siripen Pongpaichet, Boonyapat Sukosit, Chitchaya Duangtanawat
et al.
Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted to utilize online news articles as a reliable and timely source for information on crime activity. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86%, 51%, and 67% F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.
The dangers associated with the entanglement of nuclear and conventional forces have become an area of increasing concern. In this article, I survey the growing nuclear-conventional entanglement risks in Northeast Asia as well as the ways in which entanglement is driving a new era of nuclear arms racing in response. In order to better manage the risks of nuclear crises occurring, I outline the need for a greater emphasis on assurance policies to match the current focus on making deterrent threats. Given the high chance of such crisis nevertheless occurring in Northeast Asia in the years ahead, I make the case for developing what I call “crisis management interoperability” between allies armed with nuclear and strategic non-nuclear weapons. Such interoperability is aimed at ensuring that the difficult task of crisis signalling is not further complicated by alliances with entangled nuclear and conventional forces.
Nuclear engineering. Atomic power, International relations
Edgar Fábian Pinzón Nieto, Laís Cristine Lopes, Adriano dos Santos
et al.
Quantum rate theory encompasses the electron-transfer rate constant concept of electrochemical reactions as a particular setting, besides demonstrating that the electrodynamics of these reactions obey relativistic quantum mechanical rules. The theory predicts a frequency $ν= E/h$ for electron-transfer reactions, in which $E = e^2/C_q$ is the energy associated with the density-of-states $C_q/e^2$ and $C_q$ is the quantum capacitance of the electrochemical junctions. This work demonstrates that the $ν= E/h$ frequency of the intermolecular charge transfer of push-pull heterocyclic compounds, assembled over conducting electrodes, follows the above-stated quantum rate electrodynamic principles. Astonishingly, the differences between the molecular junction electronics formed by push-pull molecules and the electrodynamics of electrochemical reactions observed in redox-active modified electrodes are solely owing to an adiabatic setting (strictly following Landauer's ballistic presumption) of the quantum conductance in the push-pull molecular junctions. An appropriate electrolyte field-effect screening environment accounts for the resonant quantum conductance dynamics of the molecule-bridge-electrode structure, in which the intermolecular charge transfer dynamics within the frontier molecular orbital of push-pull heterocyclic molecules follow relativistic quantum mechanics in agreement with the quantum rate theory.
Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored to identify cervical cancer in order to enhance the conventional testing procedure. In order to attain remarkable classification results, most current CAD systems require pre-segmentation steps for the extraction of cervical cells from a pap smear slide, which is a complicated task. Furthermore, some CAD models use only hand-crafted feature extraction methods which cannot guarantee the sufficiency of classification phases. In addition, if there are few data samples, such as in cervical cell datasets, the use of deep learning (DL) alone is not the perfect choice. In addition, most existing CAD systems obtain attributes from one domain, but the integration of features from multiple domains usually increases performance. Hence, this article presents a CAD model based on extracting features from multiple domains not only one domain. It does not require a pre-segmentation process thus it is less complex than existing methods. It employs three compact DL models to obtain high-level spatial deep features rather than utilizing an individual DL model with large number of parameters and layers as used in current CADs. Moreover, it retrieves several statistical and textural descriptors from multiple domains including spatial and time–frequency domains instead of employing features from a single domain to demonstrate a clearer representation of cervical cancer features, which is not the case in most existing CADs. It examines the influence of each set of handcrafted attributes on diagnostic accuracy independently and hybrid. It then examines the consequences of combining each DL feature set obtained from each CNN with the combined handcrafted features. Finally, it uses principal component analysis to merge the entire DL features with the combined handcrafted features to investigate the effect of merging numerous DL features with various handcrafted features on classification results. With only 35 principal components, the accuracy achieved by the quatric SVM of the proposed CAD reached 100%. The performance of the described CAD proves that combining several DL features with numerous handcrafted descriptors from multiple domains is able to boost diagnostic accuracy. Additionally, the comparative performance analysis, along with other present studies, shows the competing capacity of the proposed CAD.
As one kind of advanced high temperature structural and functional materials, it is necessary for fiber reinforced silicon carbide matrix composites (SiC CMCs) in the field of thermal management (TM) to combine the efficient heat transfer and high temperature heat resistance. Common fibers reinforced SiC CMCs, such as carbon fibers reinforced SiC CMCs (Cf/SiC or Cf/C-SiC), silicon carbide based fiber reinforced SiC CMCs (SiCf/SiC), etc., have a low degree of graphitization of the reinforcing fiber and are difficult to form an effective heat transport network. The latest research progress on the preparation and properties of fiber reinforced SiC CMCs with highly thermal conductivity was reviewed in this paper. The heat transport ability of fiber reinforced SiC CMCs can be improved by introducing highly thermal conductive phase, optimizing interfacial structure, making silicon carbide crystal coarse-grained, and designing preform structure. Moreover, the development of the fiber reinforced SiC CMCs with highly thermal conductivities was prospected, that is, comprehensively considering the factors that affect the performance of SiC CMCs, flexibly using the structure-activity relationship between the microstructure and properties of the composites, in order to prepare fiber reinforced SiC CMCs with stable size, excellent properties.
Materials of engineering and construction. Mechanics of materials
Abstract The laser welded joint of 2000MPa cold rolled annealed hot pressed steel (PHS) is easy to break during cold rolling. In this paper, the laser welding method is used to butt weld four kinds of PHS2000 with a thickness of 3.5mm. The four kinds of PHS2000 steel are added with elements of 0% Nb, 0.04% Nb, 0.06% Nb + Cr and 0.08% Nb + Cr. The microstructure of the four kinds of welded joints is compared and analyzed. The mechanical properties of the four kinds of joints are compared through hardness test and tensile test. The results show that after adding 0.04% Nb, residual austenite appears in the weld zone and fully quenched zone, the width of columnar crystal decreases, the average hardness of the weld zone decreases from 595HV to 408HV, and the tensile strength increases from 608MPa to more than 800MPa. For chromium containing steel, the increase of niobium content can reduce the size of columnar crystal in weld zone.
Materials of engineering and construction. Mechanics of materials
N. Kannaiya Raja, E. Laxmi Lydia, Thumpala Archana Acharya
et al.
In recent years, drones or Unmanned Aerial Vehicles (UAVs) got significant attention among researchers because of their extensive application in commercial applications, border surveillance, etc. As the conventional terrestrial communication system does not work effectively on heavy calamities namely floods, landslides, cyclones, earthquakes, etc., UAVs can offer a potential solution for inexpensive, rapid, and wireless communication. Despite the drones’ benefits in emergency monitoring, security is been a main factor because of the existence of wireless connections for transmission. Therefore, this article introduces optimal deep learning with image encryption-based secure drone communication (ODLIE-SDC) technique. The major intention of the ODLIE-SDC technique lies in the effectual secure communication and classification process in emergency monitoring scenarios. To accomplish this, the presented ODLIE-SDC technique designs a hyperchaotic map-based image encryption technique and its optimal keys are produced by the use of a rider optimization algorithm (ROA). The image classification process is performed encompassing EfficientNet-B4-CBAM feature extraction and enhanced stacked autoencoder (ESAE) classification. Finally, the hyperparameter tuning of the EfficientNet-B4-CBAM technique takes place using the Bayesian optimization (BO) algorithm. The experimental validation of the ODLIE-SDC technique is tested on the AIDER dataset. The comprehensive comparative analysis reported the enhanced performance of the ODLIE-SDC technique over other existing approaches.
Florian Huber, Artem Yushchenko, Benedikt Stratmann
et al.
Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided by various satellites circumnavigating the world are a cheap and reliable way to obtain data for yield prediction. The field of yield prediction is currently dominated by Deep Learning approaches. While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods. The limitations can be overcome by proposing a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning. Feature importances expose the near-infrared spectrum of light as an important feature within our models. The reported results hint at the capabilities of XGBoost for yield prediction and encourage future experiments with XGBoost for yield prediction on other crops in regions all around the world.
Andreas Bablich, Maurice Müller, Paul Kienitz
et al.
Abstract A large and growing number of applications benefit from simple, fast and highly sensitive 3D imaging sensors. The Focus-Induced Photoresponse (FIP) can achieve 3D sensing functionalities by simply evaluating the irradiance dependent nonlinear sensor response in defect-based materials. Since this advantage is intricately associated to a slow response, the electrical bandwidth of present FIP detectors is limited to a few $${\text{kHz}}$$ kHz only. The devices presented in this work enable modulation frequencies of 700 kHz and beat frequency detection up to at least 3.8 MHz, surpassing the bandwidth of reported device architectures by more than two orders of magnitude. The sensors achieve a SNR of at least $$\sim 53\;{\text{dB}}$$ ∼ 53 dB at $$115\;{\text{cm}}$$ 115 cm and a DC FIP detection limit of 0.6 µW/mm2. The mature and scalable low-temperature a-Si:H process technology allows operating the device under ambient air conditions waiving additional back-end passivation, geometrical fill factors of $$100\%$$ 100 % and tailoring the FIP towards adjustable 3D sensing applications.
Shamiul Alam, Md Shafayat Hossain, Srivatsa Rangachar Srinivasa
et al.
The surging interest in quantum computing, space electronics, and superconducting circuits has led to new developments in cryogenic data storage technology. Quantum computers promise to far extend our processing capabilities and may allow solving currently intractable computational challenges. Even with the advent of the quantum computing era, ultra-fast and energy-efficient classical computing systems are still in high demand. One of the classical platforms that can achieve this dream combination is superconducting single flux quantum (SFQ) electronics. A major roadblock towards implementing scalable quantum computers and practical SFQ circuits is the lack of suitable and compatible cryogenic memory that can operate at 4 Kelvin (or lower) temperature. Cryogenic memory is also critically important in space-based applications. A multitude of device technologies have already been explored to find suitable candidates for cryogenic data storage. Here, we review the existing and emerging variants of cryogenic memory technologies. To ensure an organized discussion, we categorize the family of cryogenic memory platforms into three types: superconducting, non-superconducting, and hybrid. We scrutinize the challenges associated with these technologies and discuss their future prospects.