Periodic variation in the constitutive parameters of a propagating bulk medium over time, while uniform in space, referred to as periodically modulated photonic time crystals, provides a new control modality for wave-matter interaction. Following this, a step-like (square profile) modulation of permittivity and permeability has been utilized to achieve momentum bandgaps (MBGs) in the dispersion diagrams, whereas the modulation of conductivity has been overlooked. In this work, in the beginning, underlying photonic time crystals are extremely modulated with a square profile of permittivity $$\varepsilon (t)$$, permeability $$\mu (t)$$, and conductivity $$\sigma (t)$$—opening simultaneously momentum and frequency bandgaps, which has not been developed to date. The dispersion relation yielding photonic band structure is established with the virtue of the Bloch-Floquet theorem and by exploiting the continuity of electric displacement D(t) and magnetic flux B(t) across simultaneous discontinuities of $$\varepsilon (t)$$, $$\mu (t)$$, and $$\sigma (t)$$—leading to the Krönig-Penney methodology. Furthermore, the amplification of eigenmodes within an MBG is controlled by the conductivity modulation strength $$m_{\sigma }$$ and the temporal duty cycle. In addition, the amplitude distribution of higher-order eigenmodes generated by temporal modulation, namely, a frequency comb, is tailored with $$m_{\sigma }$$ in a specific band. Moreover, the engineering of the electric field E(t) propagation over time (and its frequency-domain spectrum) under multiple modulation strength schemes is presented. Subsequently, the optical response for a spatially finite temporal slab is studied under normal and oblique (s- and p-polarization) incidence of plane waves. The frequency comb in reflection and transmission spectra against operating frequency and incidence angle is analyzed. Finally, the total absorptance for s- and p-polarization is also discussed. Additionally, the modulation strength of the temporal variation ranges between $$-1< m_{\varepsilon ,\mu ,\sigma } < 1$$, whereas earlier studies restricted it to $$0< m_{\varepsilon ,\mu ,\sigma } < 1$$.

Modern oil paintings are characterized by the extensive use of industrial pigments, synthetic binders, and chemical additives introduced during the late nineteenth and twentieth centuries. While these innovations enabled significant artistic experimentation, they also introduced new conservation challenges due to the chemical instability of many modern paint formulations. As a consequence, modern oil paintings frequently exhibit degradation phenomena such as efflorescence, yellowing, blistering, peeling and cracking, and high sensitivity to water and organic solvents. A comprehensive understanding of the materials used in modern oil paintings—including pigments, binders, and additives—is therefore essential for developing effective conservation strategies. In this context, electromagnetic (EM) diagnostic techniques represent powerful tools for the noninvasive or minimally invasive investigation of artworks. These techniques allow researchers to characterize the chemical composition, morphology, and degradation processes affecting paint layers and substrates. This paper provides an overview of the EM techniques most commonly used in the conservation of modern oil paintings. Particular attention is devoted to spectroscopic and imaging methods such as scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, UV-Vis spectroscopy, and X-ray-based techniques, as well as to the laser technique for the delicate cleaning process. Through selected case studies reported in the literature, this review highlights the role of these techniques in pigment identification, degradation analysis, and the development of more effective conservation strategies for modern oil paintings.

Metal–Insulator–Metal (MIM) tunneling diodes are among the most promising rectifying devices for long-wave infrared (LWIR) rectenna systems due to their ultrafast response and zero-bias operation. However, their performance is strongly dependent on the choice of electrode and dielectric materials, making the identification of optimal material combinations a key challenge. To address this issue, this theoretical study presents a numerical investigation of a new class of MIM diodes based on a quantum-mechanical tunneling framework. Novel combinations of transition-metal dichalcogenides (NbS2, VSe2, and TaS2) as anode materials (M1), conductive carbides and nitrides (Mo2C, VN, and V) as cathode materials (M2), and rare-earth oxide and oxyhalide compounds (Sc2O3, LaOF, and LaOBr) as tunnel barriers (I) were selected through an extensive literature survey. These materials were combined to design previously unexplored MIM architectures for LWIR rectification. The electrical transport and rectification properties were evaluated using the Simmons tunneling model by calculating the current density–voltage (J–V) and current–voltage (I–V) characteristics, together with key figures of merit (FOMs), including zero-bias resistance, asymmetry factor, nonlinearity, and responsivity, at room temperature (300 K). The effects of tunnel barrier height and dielectric properties on device performance were systematically investigated. Among all the investigated architectures, the TaS2/LaOBr/V MIM diode exhibited the most promising overall performance, achieving an asymmetry factor exceeding 2.5 × 105, a nonlinearity factor of 1, and a zero-bias responsivity of 10 V−1 at 300 K. Furthermore, this structure demonstrated the highest current density and the most favorable I–V characteristics among the proposed material combinations. These results identify the TaS2/LaOBr/V material system as a promising candidate for high-performance LWIR energy harvesting applications, owing to its optimized tunnel barrier height, which promotes efficient electron tunneling while maintaining excellent rectification properties.

Periodic variation in the constitutive parameters of a propagating bulk medium over time, while being uniform in space, referred to as periodically time-modulated photonic crystals (PTMPCs), exhibits a new control modality for wave-matter interaction. Following this, a step-like (square profile) modulation of permittivity and permeability has been utilized to achieve momentum bandgaps (MBGs) in the photonic band structure (PBS), whereas the modulation of conductivity has been overlooked. In this work, in the beginning, underlying PTMPCs are extremely modulated with a square profile of permittivity ε(t), permeability µ(t), and conductivity σ (t)—opening momentum and frequency bandgaps simultaneously, which has not been developed to date. The dispersion relation yielding dispersion diagram (PBS), is established with the virtue of the Bloch-Floquet theorem and by exploiting the continuity of electric displacement D(t) and magnetic flux B(t) across simultaneous discontinuities of ε(t), µ(t), and σ (t)—leading to the Krönig-Penney methodology. Furthermore, the amplification of eigenmodes inside the MBGs is manipulated by conductivity modulation strength (MS) m σ as well as temporal duty cycle. In addition, the amplitude distribution of higher-order eigenmodes generated by temporal modulation, namely, a frequency comb, is tailored with m σ in a specific band. Moreover, the engineering of the electric field E(t) propagation over time (and its frequency domain spectrum) with multiple MS schemes is presented. Subsequently, the optical response for a spatially finite temporal slab is studied under normal and oblique (sand p-polarization) incidence of a plane wave. The frequency comb in reflection and transmission spectra against operating frequency and incidence angle is analyzed. At last, the total absorptance for sand p-polarization is also discussed. Additionally, the MS of the temporal variation ranges between −1 < m ε,µ,σ < 1, whereas earlier studies restricted it to 0 < m ε,µ,σ < 1.

We report an efficient experimental method for generating high-order Laguerre-Gauss (HOLG) modes by simply coupling a Gaussian beam into the cladding of a multimode fiber (MMF). In particular, the order of the HOLG mode remains invariant with respect to input power, propagation distance, and pulse duration. Furthermore, spectral and power measurements confirm that the beam-shaping mechanism is predominantly linear, whereas Kerr nonlinearity primarily affects the longitudinal phase-matching condition and conversion efficiency, without altering the generated mode order. Altogether, these findings establish our approach as a highly robust and scalable platform for generating tailored optical beams.

In this study, we investigate analytically and numerically the Goos-H & auml;nchen Shift (GHS) for reflected and trans-mitted plane waves in a planar uniaxial anisotropic hexagonal boron nitride (hBN) slab placed in air. An arbitrarily polarized plane wave serves as the excitation source. The Transfer Matrix Method is employed to compute Fresnel coefficients for s- and p-polarization, while the Stationary Phase Method is used to analyze the resulting GHS. For both polarizations, the reflected GHS appears at the left and right boundaries of two well-known reststrahlen bands (RBs) in the far-infrared (FIR) frequency range. Additionally, it is observed within RB1

This research proposes an all-metal metamaterial-based absorber with a novel geometry capable of refractive index sensing in the terahertz (THz) range. The structure consists of four concentric diamond-shaped gold resonators on the top of a gold metal plate; the resonators increase in height by 2 µm moving from the outer to the inner resonators, making the design distinctive. This novel configuration has played a very significant role in achieving multiple ultra-narrow resonant absorption peaks that produce very high sensitivity when employed as a refractive index sensor. Numerical simulations demonstrate that it can achieve six significant ultra-narrow absorption peaks within the frequency range of 5 to 8 THz. The sensor has a maximum absorptivity of 99.98% at 6.97 THz. The proposed absorber also produces very high-quality factors at each resonance. The average sensitivity is 7.57/Refractive Index Unit (THz/RIU), which is significantly high when compared to the current state of the art. This high sensitivity is instrumental in detecting smaller traces of samples that have very correlated refractive indices, like several harmful gases. Hence, the proposed metamaterial-based sensor can be used as a potential gas detector at terahertz frequency. Furthermore, the structure proves to be polarization-insensitive and produces a stable absorption response when the angle of incidence is increased up to 60°. At terahertz wavelength, the proposed design can be used for any value of the aforementioned angles, targeting THz spectroscopy-based biomolecular fingerprint detection and energy harvesting applications.

Forthis article, we approximated the field of a leaky-wave antenna (LWA) with the field produced by a uniform linear array (ULA). This article aims to provide an initial framework for applications where the generation of an inhomogeneous wave is wished, but, at the same time, a flexibility is required that is difficult to meet with the conventional LWA design. In particular, two different configurations were considered, one with a simple Menzel antenna operating at 12 GHz, and one, relevant for practical applications, with an antenna operating at 2.4 GHz. This study aimed, in both cases, to highlight the distance at which the field produced by the phased array with the chosen sampling method can approximate effectively the one produced by a leaky-wave antenna and to verify whether this could cause issues for the targeted application.

Purpose: This study aimed to compare: the performance of K-TIRADS, EU-TIRADS and ACR TIRADS when used by observers with different levels of experience compared with the gold standard of cytology, and to evaluate the diagnostic performance of CAD (computer-aided design) compared with TI-RADS systems. Methods and Materials: In total, 323 thyroid nodules were evaluated in patients who were candidates for needle aspiration. Three observers with different levels of experience evaluated the diagnostic accuracy of three risk stratification systems (ACR TI-RADS, EU-TIRADS and K-TIRADS) and CAD software (S-Detect, made by Samsung) in characterizing the nodules. The results were compared with cytology examination. All nodules were characterized in terms of shape, margins, composition, calcifications, size, echogenicity and microcalcifications, and by stratifying individual nodules by using the three TIRADS systems; then S-detect software was applied and the data were compared with each other and with the gold standard. Results: Through cytology, 308 benign and 33 malignant nodules were identified. ACR-TIRADS showed a sensitivity of 100%, a specificity of 86%, a positive predictive value of 43% and a negative predictive value of 100%. EU-TIRADS showed a sensitivity of 100%, a specificity of 79%, a positive predictive value of 33% and a negative predictive value of 100%. K-TIRADS showed a sensitivity of 100%, a specificity of 89%, a positive predictive value of 50% and a negative predictive value of 100%. S-Detect combined with EU-TIRADS showed a high agreement (>95%) with the gold standard. Conclusions: K-TIRADS’s positive predictive power was slightly better than the other TIRADS, suggesting greater accuracy in correctly diagnosing positive cases. S-DETECT combined with EU-TIRADS has similar results to S-Detect with ACR- and K-TIRADS in terms of sensitivity, specificity and negative predictive power. However, it has a slightly better positive predictive power, suggesting greater accuracy in correctly diagnosing positive cases than the ACR- and K-TIRADS classification systems. In general, S-Detect cannot yet be considered a substitute for the human observer but only as an important support for human evaluation and an excellent and fast help to provide a comprehensive and complete report. Clinical Relevance/Application: S-Detect is a valuable tool for characterizing thyroid nodules when integrated with radiologist evaluation. It is also an important support tool for less experienced observers. Particularly interesting is the approach of use in integrated combination of the K-TIRADS by the human observer with S-Detect using EU-TIRADS, which could increase the overall diagnostic efficiency of the systems.

Cultural Heritage (CH) represents the identity of populations; it is a heritage not only for the culture that produced it, but also for the entire human civilization. Still, preserving it is not an easy task; several factors hinder its preservation, from time and natural disasters to wars and neglect. Science can play a leading role in preserving CH, and among the different techniques available, Electromagnetic (EM) techniques are particularly suitable for this purpose because of their efficacy, safety for both people and materials, and their applicability to artifacts made from different materials and of complex and irregular shapes. Although usually associated with diagnostic applications, EM techniques also have a crucial role in restoration applications thanks to EM radiation treatments for the recovery and consolidation of materials such as wood, paper, parchment, stone, ceramics, and mummies. The state-of-the-art of radiation technologies shows efficacy for the elimination of pests, mold, fungi and bacteria, and for the consolidation of damaged or weakened artifacts. This paper aims to provide a useful tool for a first yet rigorous understanding of the contribution of EM techniques to CH recovery and lifetime extension, also comparing them with traditional methods and highlighting main issues in their application, such as lack of protocols and distrust, and potential risks in their application.

We report our observation of a direct transformation of a Gaussian-like laser beam, initially injected into the cladding of a graded-index multimode fiber (MMF), into a high-order Laguerre-Gauss (LG) mode within the fiber core.

When discussing Cultural Heritage (CH), the risk of causing damage is inherently linked to the artifact itself due to several factors: age, perishable materials, manufacturing techniques, and, at times, inadequate preservation conditions or previous interventions. Thorough study and diagnostics are essential before any intervention, whether for preventive, routine maintenance or major restoration. Given the symbolic, socio-cultural, and economic value of CH artifacts, non-invasive (NI), non-destructive (ND), or As Low As Reasonably Achievable (ALARA) approaches—capable of delivering efficient and long-lasting results—are preferred whenever possible. Electromagnetic (EM) techniques are unrivaled in this context. Over the past 20 years, radiography, tomography, fluorescence, spectroscopy, and ionizing radiation have seen increasing and successful applications in CH monitoring and preservation. This has led to the frequent customization of standard instruments to meet specific diagnostic needs. Simultaneously, the integration of terahertz (THz) technology has emerged as a promising advancement, enhancing capabilities in artifact analysis. Furthermore, Artificial Intelligence (AI), particularly its subsets—Machine Learning (ML) and Deep Learning (DL)—is playing an increasingly vital role in data interpretation and in optimizing conservation strategies. This paper provides a comprehensive and practical review of the key achievements in the application of EM techniques to CH over the past two decades. It focuses on identifying established best practices, outlining emerging needs, and highlighting unresolved challenges, offering a forward-looking perspective for the future development and application of these technologies in preserving tangible cultural heritage for generations to come.

This study presents a cost-effective Hybrid Metamaterial Absorber (HMA) featuring a simple circular-patterned cylindrical design, comprising an indium antimonide (InSb) resonator on a thin copper sheet. Through numerical simulations, we demonstrate that the structure exhibits temperature-tunable properties and refractive index sensitivity. At 300 K (refractive index = 1), a peak absorption of 99.94% is achieved at 1.797 THz. Efficient operation is observed across a 40 K temperature range and a refractive index spectrum of 1.00-1.05, relevant for thermal imaging and spatial bio-sensing. The simulated temperature sensing sensitivity is 13.07 GHz/K, and the refractive index sensitivity is 1146 GHz/RIU. Parametric analyses reveal tunable absorption through adjustments of the InSb resonator design parameters. Owing to its high efficiency and sensitivity demonstrated in simulations, this HMA shows promise for sensing applications in biotechnology, semiconductor fabrication, and energy harvesting.

AbstractHere, inhomogeneous waves, and, in particular, leaky waves are initially described and natural phenomena that can be expressed in terms of inhomogeneous improper waves are also introduced. The description of the physical properties of inhomogeneous waves and leaky waves and their physical characteristics will finally allow us to describe the propagation features and the radiation patterns generated by leaky‐wave antennas (LWAs), as they are conventionally defined in terms of leaky waves. Furthermore, this chapter will go into the description of LWAs families and features.

The analytical-numerical evaluation of the scattering of electromagnetic waves by multiple spheres requires the computation of numerous coefficients. For this purpose, many contributions, available in the literature, have traditionally employed the recursion method. In the present paper, we introduce a novel approach, based on primes and indices, which can be conveniently applied to the computation of the Wigner 3-j symbols, the Wigner D-function, and the Gaunt coefficients. By considering a series-expansion form, our method proves to be easily applicable to a variety of similar problems. We provide examples of coefficient calculations and compare the results with those retrieved from previous publications, demonstrating the advantages of our approach.

Background Hepatocellular carcinoma (HCC) exhibits an exceptional intratumoral heterogeneity that might influence diagnosis and outcome. Advances in digital microscopy and artificial intelligence (AI) may improve the HCC identification of liver cancer cells. Aim Two AI algorithms were designed to perform computer-assisted discrimination of tumour from non-tumour nuclei in HCC. Methods Healthy livers and HCCs from commercially available tissue arrays were stained with an antibody against proliferating cell nuclear antigen and DRAQ5 dye with high affinity for double-stranded DNA, acquired by confocal microscopy imaging and then used to design machine learning (ML) and deep learning (DL) algorithms. Results Nuclei were segmented and then used to develop the Model 1 and Model 2 algorithms, using ML and DL respectively. Model 1 was trained with some texture nuclear features extracted using discrete wavelet transform and grey-level co-occurrence matrix. Model 2 was trained with the segmented images without any additional information. The comparative analysis of the models showed that DL was more effective than ML, achieving an average accuracy of 88 % in discriminating healthy from neoplastic nuclei in HCC samples. Conclusion Our research shows that AI techniques and nuclear fluorescent staining could be useful tools for automatically detecting HCC cells in liver tissues.

Diffractive optical elements that divide an input beam into a set of replicas are used in many optical applications ranging from image processing to communications. Their design requires time-consuming optimization processes, which, for a given number of generated beams, are to be separately treated for one-dimensional and two-dimensional cases because the corresponding optimal efficiencies may be different. After generalizing their Fourier treatment, we prove that, once a particular divider has been designed, its transmission function can be used to generate numberless other dividers through affine transforms that preserve the efficiency of the original element without requiring any further optimization.

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.

Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most explored medical application is cancer detection, for which several CAD systems have been proposed. Among them, deep neural network (DNN)-based systems for skin cancer diagnosis have demonstrated comparable or superior performance to that of experienced dermatologists. However, the lack of transparency in the decision-making process of such approaches makes them “black boxes” and, therefore, not directly incorporable into clinical practice. Trying to explain and interpret the reasons for DNNs’ decisions can be performed by the emerging explainable AI (XAI) techniques. XAI has been successfully applied to DNNs for skin lesion image classification but never when additional information is incorporated during network training. This field is still unexplored; thus, in this paper, we aim to provide a method to explain, qualitatively and quantitatively, a convolutional neural network model with feature injection for melanoma diagnosis. The gradient-weighted class activation mapping and layer-wise relevance propagation methods were used to generate heat maps, highlighting the image regions and pixels that contributed most to the final prediction. In contrast, the Shapley additive explanations method was used to perform a feature importance analysis on the additional handcrafted information. To successfully integrate DNNs into the clinical and diagnostic workflow, ensuring their maximum reliability and transparency in whatever variant they are used is necessary.

Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios.