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Soil monitoring is a key topic from several perspectives, such as moisture level control for irrigation management and anti-contamination purposes. Monitoring the latter is becoming even more important due to increasing environmental pollution. As a direct consequence, there is a strong demand for innovative monitoring systems that are low cost, provide for quasi-real time and in situ monitoring, high sensitivity, and adequate accuracy. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system utilizing a customized bifilar probe and a miniaturized Vector Network Analyzer (m-VNA). The main objective is to relate frequency-domain (FD) measurements to the features of interest, such as the water content and/or the percentage of some polluting substances, through an innovative automatable procedure to retrieve the Debye dielectric parameters of the soil under different conditions. The results from this study confirm the potential of microwave reflectometry for moisture monitoring and contamination detection.

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes’ bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies.

A technique for solving the one-port closed coaxial transmission line sample holder scattering equation for complex permittivity inversion for lossy materials is presented. A non-linear least-squares procedure is used for the determination of parameters for the specification of the spectral functional form of the complex permittivity. The method allows for accurate retrieval of many low- and high-permittivity dielectric materials in the frequency range of 1 GHz to 3 GHz inserted into the coaxial cell. Using this method, the complex permittivity of a number of liquids and a Maltese soil known as Bajjad soil have been extracted by measurements using a short terminated coaxial transmission line sample holder. The proposed novel inversion method is mainly based on the reflection coefficient of the test material. The measured results of the complex permittivity of liquid dielectrics such as ethanol, methanol, and TX100 are validated and compared with previously published data obtained from measurements made by the National Physical Laboratory (NPL) using a two-port measurement setup made with the same commercial coaxial transmission line sample holder used in the one-port setup. Since the technique allows broadband measurements, it has been used to characterise the soil dielectric spectrum in the frequency range of 1–3 GHz, which is also compared with results from a two-port setup of the same coaxial line. The experimental results are a validation of the proposed approach for different types of materials.

In microwave hyperthermia tumor therapy, electromagnetic waves focus energy on the tumor to elevate the temperature above its normal levels with minimal injury to the surrounding healthy tissue. Microwave hyperthermia applicator design is important for the effectiveness of the therapy and the feasibility of real-time application. In this study, the potential of using fractal octagonal ring antenna elements as a dipole antenna array and as a connected array at 2.45 GHz for breast tumor hyperthermia application was investigated. Microwave hyperthermia treatment models consisting of different fractal octagonal ring antenna array designs and a breast phantom are simulated in COMSOL Multiphysics to obtain the field distributions. The antenna excitation phases and magnitudes are optimized using the global particle swarm algorithm to selectively increase the specific absorption rate at the target region while minimizing hot spots in other regions within the breast. Specific absorption rate distributions, obtained inside the phantom, are analyzed for each proposed microwave hyperthermia applicator design. The dipole fractal octagonal ring antenna arrays are comparatively assessed for three different designs: circular, linear, and Cross—array. The 16-antenna dipole array performance was superior for all three 1-layer applicator designs, and no distinct difference was found between 16-antenna circular, linear, or cross arrays. Two-layer dipole arrays have better performance in the deep-tissue targets than one-layer arrays. The performance of the connected array with a higher number of layers exceeds the performance of the dipole arrays in the superficial regions, while they are comparable for deep regions of the breast. The 1-layer 12-antenna circular FORA dipole array feasibility as a microwave hyperthermia applicator was experimentally shown.

Medical devices making use of radio frequency (RF) and microwave (MW) fields have been studied as alternatives to existing diagnostic and therapeutic modalities since they offer several advantages. However, the lack of accurate knowledge of the complex permittivity of different biological tissues continues to hinder progress in of these technologies. The most convenient and popular measurement method used to determine the complex permittivity of biological tissues is the open-ended coaxial line, in combination with a vector network analyser (VNA) to measure the reflection coefficient (S11) which is then converted to the corresponding tissue permittivity using either full-wave analysis or through the use of equivalent circuit models. This paper proposes an innovative method of using artificial neural networks (ANN) to convert measured S11 to tissue permittivity, circumventing the requirement of extending the VNA measurement plane to the coaxial line open end. The conventional three-step calibration technique used with coaxial open-ended probes lacks repeatability, unless applied with extreme care by experienced persons, and is not adaptable to alternative sensor antenna configurations necessitated by many potential diagnostic and monitoring applications. The method being proposed does not require calibration at the tip of the probe, thus simplifying the measurement procedure while allowing arbitrary sensor design, and was experimentally validated using S11 measurements and the corresponding complex permittivity of 60 standard liquid and 42 porcine tissue samples. Following ANN training, validation and testing, we obtained a prediction accuracy of 5% for the complex permittivity.

In this paper, we propose a method for retrieving the dispersion law of a material under test from multi-length TDR measurements in reflection mode, repeated at several frequencies. By replacing the multi-frequency measurements with measurements using multi-length TDR probe, it is possible to retrieve the complex equivalent permittivity of the material in a frequency band of interest. The proposed procedure does not require a priori knowledge of the type of dispersion law of the material, which instead can possibly be inferred from the measured data. The algorithm is validated using numerically simulated data obtained with the commercial code CST Microstudio®.

Agriculture is a major consumer of freshwater and is often associated with low water productivity. To prevent drought, farmers tend to over-irrigate, putting a strain on the ever-depleting groundwater resources. To improve modern agricultural techniques and conserve water, quick and accurate estimates of soil water content (SWC) should be made, and irrigation timed correctly in order to optimize crop yield and water use. In this study, soil samples common to the Maltese Islands having different clay, sand, and silt contents were, primarily, investigated to: (a) deduce whether the dielectric constant can be considered as a viable indicator of the SWC for the soils of Malta; (b) determine how soil compaction affects the dielectric constant measurements; and (c) to create calibration curves to directly relate the dielectric constant and the SWC for two different soil types of low and high density. The measurements, which were carried out in the X-band, were facilitated by an experimental setup comprising a two-port Vector Network Analyzer (VNA) connected to a rectangular waveguide system. From data analysis, it was found that for each soil investigated, the dielectric constant increases notably with an increase in both density and SWC. Our findings are expected to aid in future numerical analysis and simulations aimed at developing low-cost, minimally invasive Microwave (MW) systems for localized SWC sensing, and hence, in agricultural water conservation. However, it should be noted that a statistically significant relationship between soil texture and the dielectric constant could not be determined at this stage.

There is growing interest in measuring the temperature-dependent dielectric properties of bio-tissues using dual-mode techniques (scattering measurements and thermal treatment). Uncooled coaxial antennas are preferred for their direct contact with the measured medium and reduced complexity; however, they exhibit structural changes during ablation due to the thermal expansion of polytetrafluoroethylene (PTFE). This paper presents an experimental study on PTFE expansion in an uncooled coaxial insulated monopole antenna in response to changes in the tissue’s thermal environment. Furthermore, it presents a methodology to mitigate these effects through coaxial annealing. The investigation consists of two distinct experiments: characterising PTFE expansion and assessing the effects of annealing through microwave ablation. This was achieved by simulating the thermal effects experienced during ablation by immersing the test antenna in heated peanut oil. PTFE expansion was measured through camera monitoring and using a toolmaker’s microscope, revealing two expansion modalities: linear PTFE expansion and non-linear plastic deformation from manufacturing processes. The return loss during ablation and consequential changes in the ablated lesion were also assessed. Antenna pre-annealing increased resilience against structural changes in the antenna, improving lesion ellipticity. Therefore, this study establishes a fabrication method for achieving an uncooled thermally stable antenna, leading to an optimised dual-mode ablation procedure, enabling quasi-real-time permittivity measurement of the surrounding tissue.

Background Public health has multicultural origins. By the close of the nineteenth century, Schools of Public Health (SPHs) began to emerge in western countries in response to major contemporary public health challenges. The Flexner Report (1910) emphasized the centrality of preventive medicine, sanitation, and public health measures in health professional education. The Alma Ata Declaration on Primary Health Care (PHC) in 1978 was a critical milestone, especially for low and middle-income countries (LMICs), conceptualizing a close working relationship between PHC and public health measures. The Commission on Social Determinants of Health (2005–2008) strengthened the case for SPHs in LMICs as key stakeholders in efforts to reduce global health inequities. This scoping review groups text into public health challenges faced by LMICs and the role of SPHs in addressing these challenges. Main text The challenges faced by LMICs include rapid urbanization, environmental degradation, unfair terms of global trade, limited capacity for equitable growth, mass displacements associated with conflicts and natural disasters, and universal health coverage. Poor governance and externally imposed donor policies and agendas, further strain the fragile health systems of LMICs faced with epidemiological transition. Moreover barriers to education and research imposed by limited resources, political and economic instability, and unbalanced partnerships additionally aggravate the crisis. To address these contextual challenges effectively, SPHs are offering broad based health professional education, conducting multidisciplinary population based research and fostering collaborative partnerships. SPHs are also looked upon as the key drivers to achieve sustainable development goals (SDGs). Conclusion SPHs in LMICs can contribute to overcoming several public health challenges being faced by LMICs, including achieving SDGs. Most importantly they can develop cadres of competent and well-motivated public health professionals: educators, practitioners and researchers who ask questions that address fundamental health determinants, seek solutions as agents of change within their mandates, provide specific services and serve as advocates for multilevel partnerships. Funding support, human resources, and agency are unfortunately often limited or curtailed in LMICs, and this requires constructive collaboration between LMICs and counterpart institutions from high income countries.

Accurate segmentation of the left ventricle (LV) in cardiac CT images is crucial for assessing ventricular function and diagnosing cardiovascular diseases. Creating a sufficiently large training set with accurate manual labels of LV can be cumbersome. More efficient semi-automatic segmentation, however, often includes unwanted structures, such as papillary muscles, due to low contrast between the LV wall and surrounding tissues. This study introduces a two-input-channel method within a Hybrid-Fusion Transformer deep-learning framework to produce refined LV labels from a combination of CT images and semi-automatic rough labels, effectively removing papillary muscles. By leveraging the efficiency of semi-automatic LV segmentation, we train an automatic refined segmentation model on a small set of images with both refined manual and rough semi-automatic labels. Evaluated through quantitative cross-validation, our method outperformed models that used only either CT images or rough masks as input.