Search Results (33)

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. Full article

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. Full article

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. Full article

For this 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. Full article

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. Full article

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. Full article

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. Full article

Background: Stereotactic body radiotherapy (SBRT) is increasingly used to treat lung metastasis (LM) in patients with soft tissue sarcoma (STS) and bone sarcoma (BS). Methods: This retrospective study evaluated the outcomes of patients with BS and STS treated with SBRT for LM between 2010 and 2023. Results: We enrolled 102 patients (51 each with STS and BS), of whom 71 were males and 31 were females (median age, 40 years; range, 11–81 years). At diagnosis, 76 and 26 patients had localized and metastatic disease, respectively, with a median of 4 recurrences (range, 1–12). Before SBRT, 75 patients received chemotherapy and 52 underwent surgery for LM, with 276 nodules treated with SBRT (median dose, 48 Gy; range, 40–52). Local control of irradiated LM was 86% at 1 year and 78% at 2 years. By 31 December 2023 (median follow-up, 4.8 years), 60 patients had died and 42 survived (20 without ongoing disease). From the first LM relapse, the median overall survival (OS) was 4.8 years and the 5-year OS was 49% (95% confidence interval, 39–60%), with no difference between STS and BS; the median OS was 2.9 years and the 5-year OS was 36% after SBRT. Chemotherapy before SBRT was a negative prognostic factor by multivariate analysis. Conclusions: Long-term follow-up shows that SBRT as part of a multimodal treatment approach has reasonable survival rates in patients with LM due to sarcoma. Compared with historical results using only surgery and chemotherapy, SBRT has improved the 5-year OS. Full article

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. Full article

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions. Full article

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. Full article

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. Full article

This study presents a performance analysis for an Internet-of-Things wildlife radio-tracking mission using drones, satellites and stratospheric platforms for data relay with Spread Spectrum Modulation devices. The performance analysis is presented with link and data budgets, calculations of the area coverage, an estimation of the time resolution and allowable data amount of each collar, a power and energy budget and consequent battery pack and collar weight estimations, cost budgets, and considerations on synergetic approaches to incorporate more mission segments together. The paper results are detailed with example species to target with each collar weight range, and with design drivers and guidelines to implement improved mission segments. Full article

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice. Full article

In this paper, we exploit the enhanced penetration reachable through inhomogeneous waves to induce hyperthermia in biological tissues. We will present a leaky-wave antenna inspired by the Menzel antenna which has been shortened through opportune design and optimizations and that has been designed to optimize the penetration at the interface with the skin, allowing penetration in the skin layer at a constant temperature, and enhanced penetration in the overall structure considered. Past papers both numerically and analytically demonstrated the possibility of reducing the attenuation that the electromagnetic waves are subject to when travelling inside a lossy medium by using inhomogeneous waves. In those papers, a structure (the leaky-wave antenna) is shown to allow the effect, but such a radiator suffers from low efficiency. Also, at the frequencies that are most used for hyperthermia application, a classical leaky-wave antenna would be too long; here is where the idea of the shortened leaky-wave arises. To numerically analyze the penetration in biological tissues, this paper considers a numerical prototype of a sample of flesh, composed of superficial skin layers, followed by fat and an undefined layer of muscles. Full article