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In recent years there has been much interest in investigating the extent to which social status or prestige are related to an individual's degree of integration in social networks. It has been shown that, among hunter-gatherers, social characteristics of an individual based on social status or prestige, such foraging reputation, friendship popularity, and pro-social reputation, can influence the extent to which an individual is embedded in a social network. However, little is known regarding the extent to which height, a physical trait that in Western societies is often associated with social status, is associated in integration in social networks among small-scale hunter gatherers. Here, we investigated the relationship between height and a position an individual occupies in proximity networks among Hadza men (n = 30), hunter-gatherers living in Northern Tanzania. The results of our study show that height is not related to the position an individual maintains in proximity networks. We argue that, in a relatively egalitarian small-scale hunter-gatherer societies such as the Hadza, social interactions driving proximity networks might be influenced by social traits, such as popularity and hunting reputation, rather than physical traits, such as height., (© 2024 The Author(s). American Journal of Human Biology published by Wiley Periodicals LLC.)

Age at peak height velocity (APHV) is an indicator of maturity timing which is applicable to both sexes, and which is influenced by environmental factors. The objective of this study was to assess variation in APHV associated with several indicators of socioeconomic status (SES) in a longitudinal sample of Polish adolescents. The sample included 739 boys born in 1983 and followed annually from 12 to 16 years, and 597 girls born in 1985 and followed annually from 9 to 13 years. The height records were fitted with the SITAR model to estimate APHV. SES was estimated using principal component analysis of indicators of familial status based on parental education, family size, living conditions and household possessions. Statistical analyses included analysis of variance (one-way for general SES and three-way for parental education and family size) and Tukey post-hoc tests for unequal samples. General SES (p <.001) and family size (p < .05) significantly influenced APHV among boys, while only maternal education (p < .05) significantly influenced APHV among girls. Among youth from families of higher SES, as defined by the respective indicators, APHV was attained significantly earlier, on average, than in peers from families of lower SES. Overall, the results showed a sex-dependent effect of SES on APHV, and highlighted the influence of favorable socioeconomic conditions for optimal growth and maturation during adolescence., (© 2024 Wiley Periodicals LLC.)

Contribution: While research indicates usefulness of remote laboratories in teaching of digital hardware systems, their main application is to complement stationary classes. This work presents a low-cost, scalable architecture that supports rapid transformation of teaching to a model based solely on remote access mechanisms. Background: Adaptation of online laboratory solutions from the literature to en-masse teaching of digital circuits is time-consuming and expensive. Solutions that permit cheap and rapid conversion of courses to remote environments seem to be of high value, especially when social distancing renders direct teaching impossible. Intended Outcomes: Demonstration of a flexible and cheap architecture that permits rapid transformation of digital circuits laboratories to the remote environment. Validation of system's performance and usefulness on a large group of students. Application Design: Remote digital circuits laboratories from the literature are designed to complement stationary classes. For successful teaching during pandemic, a low-cost, flexible, and efficient solution to online laboratory based on commonly available technologies is required. Findings: The proposed remote laboratory architecture enables rapid conversion of on-site teaching to online model, while supporting in-situupgrades and functionality enhancements. The presented solution proved to be a convenient substitute for conventional laboratories during pandemic.

Developing contemporary antennas is a challenging endeavor that requires considerable engineering insight. The most laborious stage is to devise an antenna architecture that delivers the required functionalities, e.g., multiband operation. Iterative by nature (hands-on topology modifications, parametric studies, trial-and-error geometry selection), it typically takes many weeks and requires considerable engagement from a human expert. Consequently, only a few possible design options concerning the fundamental antenna geometry may be considered. Automated topology rendition and geometry parameter optimization are highly relevant, especially from the industrial perspective. Therein, reducing time-to-market and limiting the involvement of trained experts is critical. This research proposes an innovative procedure for unsupervised development of planar antennas. Our method leverages flexible antenna parameterization based on re-sizable elliptical patches. It permits the realization of a massive number of geometries of diverse shapes and complexities using a small number of decision variables. Computational intelligence methods are employed to conduct antenna evolution exclusively based on specifications and possible constraints (e.g., maximum size). Fine-tuning of the structure geometry is achieved through low-cost local search routines. Our methodology is demonstrated by designing several antennas featuring distinct characteristics (broadband, single-, dual- and triple-band). The obtained results, supported by experimental data, underscore the presented approach's versatility and capability to render unconventional topologies at reasonably low computational expenses. As mentioned earlier, the design process is fully automated without human expert involvement., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The development of contemporary electronic components, particularly antennas, places significant emphasis on miniaturization. This trend is driven by the emergence of technologies such as mobile communications, the internet of things, radio-frequency identification, and implantable devices. The need for small size is accompanied by heightened demands on electrical and field properties, posing a considerable challenge for antenna design. Shrinking physical dimensions can compromise performance, making miniaturization-oriented parametric optimization a complex and heavily constrained task. Additionally, the task is multimodal due to typical parameter redundancy resulting from various topological modifications in compact antennas. Identifying truly minimum-size designs requires a global search approach, as the popular nature-inspired algorithms face challenges related to computational efficiency and the need for reliable full-wave electromagnetic (EM) simulation to evaluate device's characteristics. This study introduces an innovative machine learning procedure for cost-effective global optimization-based miniaturization of antennas. Our technique includes parameter space pre-screening and the iterative refinement of kriging surrogate models using the predicted merit function minimization as an infill criterion. Concurrently, the design task incorporates design constraints implicitly by means of penalty functions. The combination of these mechanisms demonstrates superiority over conventional techniques, including gradient search and electromagnetic-driven nature-inspired optimization. Numerical experiments conducted on four broadband antennas indicate that the proposed framework consistently yields competitive miniaturization rates across multiple algorithm runs at low costs, compared to the benchmark., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

This communication presents an ultra-miniaturized two-way frequency tunable antenna diplexer based on cavity-backed slots and dielectric fluids. The proposed antenna utilizes two half-mode substrate-integrated rectangular cavities loaded with slots and fluidic pockets. The conventional size reduction is achieved by employing half-mode cavities, whereas ultra-miniaturization is obtained by applying the slots, which provides additional capacitive loading. As the cavities are of unequal sizes, a weak cross-coupling path is created between the ports to obtain high isolation (> 30 dB). The isolation is further enhanced by loading the slots. Two mechanisms are analyzed to tune the frequency bands individually or simultaneously. Firstly, the width of the slots can be altered to tune the frequency bands. However, this method involves modification of the physical dimensions of the antenna. Secondly, fluidic vias are created on the bottom plane of the cavities. These can be filled with various dielectric liquids to achieve frequency reconfigurability without altering the physical dimensions of the antenna. To demonstrate the concepts considered, the prototype of the proposed antenna was fabricated and experimentally validated. The structure has a footprint of 0.045λ g 2 and an isolation exceeding 33.4 dB. The operating frequencies are tunable in the range from 3.08 to 3.84 GHz (lower band) and from 4.97 to 6.33 GHz (upper band) by varying the dimensions of the slots whereas the operating frequencies are reconfigurable in the range from 2.74 to 3.38 GHz (lower band) and from 4.54 to 5.58 GHz (upper band), by employing microfluidic approach. As a result, the working frequencies may be varied in the range from 2.74 to 3.84 GHz (lower band) and from 4.54 to 6.33 GHz (upper band), making this antenna diplexer a competitive candidate for several communication systems. The cross-polarization levels, front-to-back ratio, and realized gain are greater than 19 dB, 18 dB, and 2. dBi, respectively. Excellent consistency is observed between full-wave simulation results and the measurement data., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic (EM) simulation models. Surrogate-assisted methods offer acceleration, yet constructing reliable metamodels is hindered in higher-dimensional spaces and systems with highly nonlinear characteristics. This work suggests an innovative technique for global antenna optimization embedded within a machine-learning framework. It involves iteratively refined kriging surrogates and particle swarm optimization for generating infill points. The search process operates within a reduced-dimensionality region established through fast global sensitivity analysis. Domain confinement enables the creation of accurate behavioral models using limited training data, resulting in low CPU costs for optimization. Additional savings are realized by employing variable-resolution EM simulations, where low-fidelity models are utilized during the global search stage (including sensitivity analysis), and high-fidelity ones are reserved for final (gradient-based) tuning of antenna parameters. Comprehensive verification demonstrates the consistent performance of the proposed procedure, its superiority over benchmark techniques, and the relevance of the mechanisms embedded into the algorithm for enhancing search process reliability, design quality, and computational efficiency., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO 2 ), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO 2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO 2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO 2 detectors. The methodology was showcased using a purpose-built platform housing NO 2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO 2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO 2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m 3 . This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO 2 monitoring., (© 2024. The Author(s).)

A conformal reflective metasurface fed by a dual-band multiple-input multiple-output (MIMO) antenna is proposed for low-cost beam steering applications in 5G Millimeter-wave frequency bands. The beam steering is accomplished by selecting a specific port of MIMO antenna. Each MIMO port is associated with a beam that points in a different direction due to a conformal reflective metasurface. This novel conformal metasurface antenna design has the advantages of higher gain, lower cost, a simpler feeding source, and a lower profile when compared to traditional reflective metasurfaces using bulky horn antennas and phased arrays with complex feeding networks and phase shifters for beam steering. The proposed beam steering antenna consists of a compact five-element dual-band MIMO and a 32 × 32 unit-cell conformal dual-band reflective metasurface placed at the top of the MIMO antenna to obtain the beam steering capability as well as gain enhancement. The proposed reflective metasurface has a stable response under oblique incidence angles of up to 60 0 at 24 GHz and 38 GHz and its symmetric, single-layer structure, ensures polarization insensitivity and stable response under conformal conditions. The presented MIMO antenna design is not only compact but also offers a wideband response effectively covering the desired 5G mm-wave frequency bands. The overall size of the MIMO antenna alone is 70 × 12 mm 2 with a maximum gain of 5.4 and 7.2 dB. It is further improved up to 13.1 and 14.2 dB at 24 and 38 GHz respectively, with a beam steering range of ś 40 0 by using a conformal reflective metasurface. Unlike the existing beam steering strategies, the suggested method is not only cost-effective but also increases the overall directivity and gain of the source MIMO antenna. The measured results agree with the simulated results, making it a potential candidate in the 5G and beyond beam steering applications., (© 2024. The Author(s).)

Ultra-wideband (UWB) technology is extensively used in indoor navigation, medical applications, and Internet of Things devices due to its low power consumption and resilience against multipath fading and losses. This paper examines a multiple-input multiple-output (MIMO), circularly polarized (CP) dielectric resonator antenna for UWB systems. Compact form factor, high gain, wideband response, improved port isolation, and high data rates are the major design goals. This arrangement consists of two identical DRAs with self-decoupled orthogonal orientations eliminating the need for extra decoupling structures while achieving an impressive maximum isolation of 43 dB. The corner-edge feeding mechanism of the extended feedline generates two orthogonal E-fields, facilitating circular polarization. Additionally, a printed hook-shaped stub integrated with the ground plane enhances CP performance across the two operating bands without altering the DR structure. Fabrication and testing exhibit an impressive 133 % impedance bandwidth (2.5-14 GHz) with high port isolation. For a 3 dB axial ratio reference, the single-element design exhibits axial ratio bandwidths (ARBW) of 1.2 GHz (3.6-4.8 GHz) and 0.8 GHz (9.3-10.1 GHz). Remarkably, the MIMO configuration achieves a single ARBW of 0.5 GHz (3.9-4.4 GHz). Detailed investigations of MIMO performance parameters, including diversity gain, envelope correlation coefficient, channel capacity loss, and total active reflection coefficient, underscore the design's efficacy, making it a good choice for UWB wireless applications., (© 2024. The Author(s).)

Achieving compact size has emerged as a key consideration in modern microwave design. While structural miniaturization can be accomplished through judicious circuit architecture selection, precise parameter tuning is equally vital to minimize physical dimensions while meeting stringent performance requirements for electrical characteristics. Due to the intricate nature of compact structures, global optimization is recommended, yet hindered by the excessive expenses associated with system evaluation, typically conducted through electromagnetic (EM) simulation. This challenge is further compounded by the fact that size reduction is a constrained problem entailing expensive constraints. This paper introduces an innovative method for cost-effective explicit miniaturization of microwave components on a global scale. Our approach leverages response feature technology, formulating the optimization problem based on a set of characteristic points derived from EM-analyzed responses, combined with an implicit constraint handling approach. Both elements facilitate handling size reduction by transforming it into an unconstrained problem and regularizing the objective function. The core search engine employs a machine-learning framework with kriging-based surrogates refined using the predicted improvement in the objective function as the infill criterion. Our algorithm is demonstrated using two miniaturized couplers and is shown superior over several benchmark routines, encompassing both conventional (gradient-based) and population-based procedures, alongside a machine learning technique. The primary strengths of the proposed framework lie in its reliability, computational efficiency (with a typical optimization cost ranging from 100 to 150 EM circuit analyses), and straightforward setup., (© 2024. The Author(s).)

Background: Falls are prevalent among people living with Multiple Sclerosis (PwMS) and Spinal Cord Injury (PwSCI) who use wheelchairs or scooters (WC/S) full time, however, there is a scarcity of evidence-based fall prevention and management programs., Objective: To describe the systematic translation of an in-person fall prevention and management program (Individualized Reduction Of FaLLs - iROLL) for PwMS to an online platform, extending its scope to include PwSCI, and to evaluate the preliminary useability, usefulness, and safety of the intervention., Methods: iROLL was systematically translated to an online platform (iROLL-O). PwMS and PwSCI who use a WC/S full time, experienced at least one fall within the past 36 months, and could transfer independently or with minimal to moderate assistance, enrolled in iROLL-O. Usability, usefulness, and safety were evaluated through 1:1 semi-structured interviews, gathering feedback on: perceived impact of the intervention on falls and functional mobility, program experiences, adverse events, and recommendations for improvement., Results: Five participants successfully completed the iROLL-O program. No safety concerns were raised by participants. Themes emerging from the semi-structured interviews included: (1) barriers and facilitators to program access, (2) motivation for participation, (3) program outcomes, and (4) program content and structure. Participants reported reduced concerns about falling, enhanced functional mobility skills, and highlighted the supportive nature of synchronous group meetings for learning., Conclusion: No adverse events occurred during the implementation of iROLL-O and participants found the program to be useable and useful. Further testing is needed to examine efficacy among a large and diverse population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer TZ declared a past co-authorship with the authors LR, EP to the handling editor., (© 2024 Rice, Yehya, Yi, Koziel and Peterson.)

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques., (© 2024. The Author(s).)

In this study, a high-isolation dual-band (28/38 GHz) multiple-input-multiple-output (MIMO) antenna for 5G millimeter-wave indoor applications is presented. The antenna consists of two interconnected patches. The primary patch is connected to the inset feed, while the secondary patch is arc-shaped and positioned over the main patch, opposite to the feed. Both patches function in the lower 28 GHz band, while the primary patch is accountable for inducing the upper 38 GHz band. An expedited trust-region (TR) algorithm is employed to optimize the dimensions of the antenna components, ensuring the antenna operates efficiently with high reflection at both bands. The antenna demonstrates a gain exceeding 7 dBi at both frequencies. An array of four antennas is configured orthogonally to create a MIMO system with isolation surpassing 19 dB. The isolation is further enhanced through the addition of a circular parasitic patch at the front and modifications made to the ground. The TR method is employed again to optimize their parameters and achieve the desired isolation, exceeding 32 dB at both bands. The MIMO system demonstrates outstanding diversity performance at both frequencies, characterized by low values of the envelope correlation coefficient (ECC) (< 10 - 4), channel capacity loss (CCL) (< 0.03 bit/s/Hz), and total active reflection coefficient (TARC) (< - 10 dB). Additionally, it secures a diversity gain (DG) exceeding 9.99 dB. The MIMO system is manufactured and tested, showing good alignment between simulation and measurement data for all performance metrics., (© 2024. The Author(s).)

Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design. However, constructing reliable surrogate models faces several challenges, including the nonlinearity of circuit characteristics and the vast size of the parameter space, encompassing both dimensionality and design variable ranges. Additionally, ensuring the validity of the model across broad geometry/material parameter and frequency ranges is crucial for its utility in design. The purpose of this paper is to introduce an innovative approach to cost-effective and dependable behavioral modeling of microwave passives. Central to our method is a fast global sensitivity analysis (FGSA) procedure, which is devised to identify correlations between design parameters and quantify their impacts on circuit characteristics. The most significant directions identified through FGSA are utilized to establish a reduced-dimensionality domain. Within this domain, the model may be constructed using a limited amount of data samples while capturing a significant portion of the circuit response variability, rendering it suitable for design purposes. The outstanding predictive capability of the proposed model, its superiority over traditional techniques, and its readiness for design applications are demonstrated through the analysis of three microstrip circuits of diverse characteristics., (© 2024. The Author(s).)

In order to increase the radiation performance of aperture-type antennas, this paper demonstrates a low-profile, planar, single-layer, three-dimensional (3-D) printable metastructure. The proposed hybridized metastructure is highly transparent as it is made out of novel hybrid meta-atoms having transmission coefficient magnitudes greater than - 0.72 dB and fully complies with the near-field phase transformation principle. The hybridized design approach makes the metastructure planar, low-profile, light in weight, and compatible with additive printing technology. For the proof-of-concept, such metastructure is developed and numerically verified to enhance the radiation performance of a resonant cavity antenna (RCA). With the proposed metastructure, the peak directivity of the RCA is improved by 8.6 dBi (from 11.4 dBi to 20 dBi) at the operating frequency of 12.4 GHz. The aperture efficiency and 3-dB directivity bandwidth of the RCA with the metastructure are 41.46% and 16.5%, respectively. Using readily accessible thermoplastics or polymers and copper with cost-effective fused deposition modeling (FDM) 3-D printing technology, the proposed planar hybridized metastructure can be prototyped commercially., (© 2024. The Author(s).)

The incorporation of higher-order modes (HOMs) can substantially augment the antenna gain and bandwidth, but this improvement is typically accompanied by compromised radiation performance including radiation nulls and higher side lobe levels. In this study, an inventive strategy is introduced to reduce the radiation nulls and the side lobe levels of a single antenna element by positioning multiple slots of the radiating element at unequal spacing. Dual hybrid HOMs are analyzed inside a substrate integrated waveguide-based cavity to design a wide band, enhanced gain dual-polarized antenna. The radiating element of the antenna is designed with multiple slots positioned at unequal spacing but symmetrical along the origin. This methodology provides three-fold advantages: a reduction of side lobes, an adjustment of phase center, and a significant reduction of radiation nulls. The antenna has been fabricated, and experimentally validated. The antenna exhibits a reduction in radiation null to - 0.5 dB, a phase adjustment of the main lobe to 0°, and a reduction in side lobe level from - 14.4 dB (N = 2, equal spacing) and - 15.5 dB (N = 4, equal spacing) a maximum of - 19.7 dB (N = 4, unequal spacing) at 12.35 GHz in the phi-0 plane. Excellent agreement between measured and simulated results corroborates the efficacy of the proposed approach. The significant improvement in the radiation performance of the single-element antenna design sets the antenna design apart from the state-of-the-art solutions., (© 2024. The Author(s).)

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI., (© 2024. The Author(s).)

In this paper, a novel negative refractive index metamaterial (NIM) is developed and characterized. The proposed metamaterial exhibits negative effective permittivity (ε effe ) and negative effective permeability (µ effe ) at millimeter wave frequency of 28 GHz. This attractive feature is utilized to enhance the gain of a microstrip patch antenna (MPA). Two thin layers of 5 × 5 subwavelength unit cell array of NIM are placed above a single MPA to enhance the gain of the antenna. Each unit cell has an area of 3.4 × 3.4 mm 2 . A gain increase of 7.9 dBi has been observed when using the proposed NIM as a superstrate. Furthermore, the NIM array is placed over a 2 × 2 array of MPAs with four ports to demonstrate versatility of the metamaterial. The total size of the 2 × 2 antenna array system with N-MTM is about 61.1 × 34 × 16mm 3 (5.71λ × 3.18λ × 1.5λ, where λ is the free-space wavelength at 28 GHz). The measurement result indicate that the maximum gain of the antenna array is 13.5dBi. A gain enhancement of 7.55 dB in E-Plane and 7.25 dB in H-Plane at the resonant frequency of 28 GHz is obtained. The proposed antenna structure is suitable for 5G millimeter wave communications, in particular, for possible implementation in future millimeter wave access points and cellular base stations., (© 2024. The Author(s).)

A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60 ∘ at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3  ×  3.3 mm 2 ) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32  ×  x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20-25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages., (© 2024. The Author(s).)

Modern microwave devices are designed to fulfill stringent requirements pertaining to electrical performance, which requires, among others, a meticulous tuning of their geometry parameters. When moving up in frequency, physical dimensions of passive microwave circuits become smaller, making the system performance increasingly susceptible to manufacturing tolerances. In particular, inherent inaccuracy of fabrication processes affect the fundamental operating parameters, such as center frequency or bandwidth, which is especially troublesome for narrow-band structures, including notch filters. The ability to quantify the effects of tolerances, and-even more-to account for these in the design process, are instrumental in making the designs more reliable, and to increase the likelihood that adequate operation is ensured despite manufacturing errors. This paper proposes a simple yet computationally efficient and reliable procedure for statistical analysis and yield optimization of resonator-based notch filters. Our methodology involves feature-based surrogate models that can be established using a handful of training data points, and employed for rapid evaluation of the circuit fabrication yield. Furthermore, a yield optimization procedure is developed, which iteratively sets up a sequence of feature-based models, constructed within local domains relocated along the optimization path, and uses them as predictors to find a robust (maximum yield) design at a low computational cost. The presented approach has been demonstrated using two complementary split ring resonator (CSRR)-based notch filters. The cost of statistical design is about a hundred of EM simulations of the respective filter, with yield evaluation reliability corroborated through EM-based Monte Carlo analysis., (© 2023. Springer Nature Limited.)

Fetal life and infancy are extremely sensitive to adverse environmental conditions. This study aimed to assess the effect of exposure to a natural disaster (cyclone Aila) in utero or during infancy on fine and gross motor functions in preadolescent Indian children. The study was conducted in West Bengal, India, and included approximately 700 children (7-10 years old) who were prenatally or postnatally exposed to cyclone Aila and a nonaffected group. Anthropometric measures included height, weight, and birthweight. Socioeconomic status was based on parental education, family size, and income. Motor functions were assessed using the short form of Bruininks-Oseretsky Test of Motor Proficiency (BOT-2). Statistical analyses included, for example, generalized linear models. There were no differences in motor functions relative to the timing of the exposure (trimester) during pregnancy. Compared to the controls, prenatal Aila exposure resulted in poorer performance in all BOT-2 subtests, except for fine motor precision, strength, and balance (the last in boys), while postnatal Aila exposure, compared to the controls, resulted in poorer performance in manual dexterity, bilateral coordination, balance (girls only), and speed and agility. Early life exposure to a natural disaster has long-term adverse effect on motor proficiency in children. By inference, the welfare of pregnant women and infants should be of particular concern for emergency and health services during an environmental cataclysm., (© 2023 Wiley Periodicals LLC.)

Numerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability. The numerical challenges are even more pronounced when global search is required, which is most often carried out using nature-inspired algorithms. Population-based procedures are known for their ability to escape from local optima, yet their computational efficiency is poor, which makes them impractical when applied directly to EM models. A common workaround is the utilization of surrogate modeling techniques, typically in the form of iterative prediction-correction schemes, where the accumulated EM simulation data is used to identify the promising regions of the parameter space and to refine the surrogate model predictive power at the same time. Notwithstanding, implementation of surrogate-assisted procedures is often intricate, whereas their efficacy may be hampered by the dimensionality issues and considerable nonlinearity of antenna characteristics. This work investigates the benefits of incorporating variable-resolution EM simulation models into nature-inspired algorithms for optimization of antenna structures, where the model resolution pertains to the level of discretization density of an antenna structure in the full-wave simulation model. The considered framework utilizes EM simulation models which share the same physical background and are selected from a continuous spectrum of allowable resolutions. The early stages of the search process are carried out with the use of the lowest fidelity model, which is subsequently automatically increased to finally reach the high-fidelity antenna representation (i.e., considered as sufficiently accurate for design purposes). Numerical validation is executed using several antenna structures of distinct types of characteristics, and a particle swarm optimizer as the optimization engine. The results demonstrate that appropriate resolution adjustment profiles permit considerable computational savings (reaching up to eighty percent in comparison to high-fidelity-based optimization) without noticeable degradation of the search process reliability. The most appealing features of the presented approach-apart from its computational efficiency-are straightforward implementation and versatility., (© 2023. The Author(s).)

In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources., (© 2024. The Author(s).)

Utilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response features, smoothing the objective function landscape processed by the algorithm. We begin with initial parameter space screening and surrogate model construction using coarse-discretization EM analysis. Subsequently, the surrogate evolves iteratively into a co-kriging model, refining itself using accumulated high-fidelity EM simulation results, with the infill criterion focusing on minimizing the predicted objective function. Employing a particle swarm optimizer (PSO) as the underlying search routine, extensive verification case studies showcase the efficiency and superiority of our procedure over benchmarks. The average optimization cost translates to just around ninety high-fidelity EM antenna analyses, showcasing excellent solution repeatability. Leveraging variable-resolution simulations achieves up to a seventy percent speedup compared to the single-fidelity algorithm., (© 2024. The Author(s).)

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch., (© 2024. The Author(s).)

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type., (© 2024. The Author(s).)

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO 2 ), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO 2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO 2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO 2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO 2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m 3 ). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches., (© 2024. The Author(s).)

Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the design process of transmitarrays is more intricate due to the necessity of manipulating both the transmission phase and magnitude of its unit elements. For reliability, the design process has to be conducted at the level of full-wave electromagnetic models, which makes direct optimization prohibitive. The most widely used workaround is to employ surrogate modeling techniques to construct fast representations of the unit elements, yet the initial model setup cost is typically high and includes acquisition of thousands of training data points. In this paper, we propose a novel approach to cost-efficient design of transmitarrays. It is based on artificial-intelligence-enabled data-driven surrogates, which can be constructed using only a few hundreds of training data samples, while exhibiting the predictive power sufficient for reliable design. Our methodology is demonstrated by re-using the presented surrogate for the design of high-performance transmitarrays operating at various frequency ranges of 8-14 GHz, 22-28 GHz, and 28-36 GHz., (© 2023. Springer Nature Limited.)

Background: Systemic sclerosis (SSc) is a connective tissue disease that can serve as a model to study vascular changes in response to inflammation, autoimmunity, and fibrotic remodeling. Although microvascular changes are the earliest histopathologic manifestation of SSc, the vascular pathophysiology remains poorly understood., Methods: We applied spatial proteomic approaches to deconvolute the heterogeneity of vascular cells at the single-cell level in situ and characterize cellular alterations of the vascular niches of patients with SSc. Skin biopsies of patients with SSc and control individuals were analyzed by imaging mass cytometry, yielding a total of 90 755 cells including 2987 endothelial cells and 4096 immune cells., Results: We identified 7 different subpopulations of blood vascular endothelial cells (VECs), 2 subpopulations of lymphatic endothelial cells, and 3 subpopulations of pericytes. A novel population of CD34 + ;αSMA + (α-smooth muscle actin);CD31 + VECs was more common in SSc, whereas endothelial precursor cells were decreased. Co-detection by indexing and tyramide signal amplification confirmed these findings. The microenvironment of CD34 + ;αSMA + ;CD31 + VECs was enriched for immune cells and myofibroblasts, and CD34 + ;αSMA + ;CD31 + VECs expressed markers of endothelial-to-mesenchymal transition. The density of CD34 + ;αSMA + ;CD31 + VECs was associated with clinical progression of fibrosis in SSc., Conclusions: Using spatial proteomics, we unraveled the heterogeneity of vascular cells in control individuals and patients with SSc. We identified CD34 + ;αSMA + ;CD31 + VECs as a novel endothelial cell population that is increased in patients with SSc, expresses markers for endothelial-to-mesenchymal transition, and is located in close proximity to immune cells and myofibroblasts. CD34 + ;αSMA + ;CD31 + VEC counts were associated with clinical outcomes of progressive fibrotic remodeling, thus providing a novel cellular correlate for the crosstalk of vasculopathy and fibrosis., Competing Interests: Disclosures Although no disclosures directly relate to the present study, J.H.W. Distler has consultancy relationships with AbbVie, Active Biotech, AnaMar, ARXX, AstraZeneca, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, Genentech, GSK, Inventiva, Janssen, Novartis, Pfizer, Roche, and UCB. JHWD has received research funding from AnaMar, Argenx, ARXX, BMS, Bayer Pharma, Boehringer Ingelheim, Cantargia, Celgene, CSL Behring, Galapagos, GSK, Inventiva, Kiniksa, Lassen, Sanofi-Aventis, RedX, and UCB. Moreover, J.H.W. Distler is a stock owner of 4D Science and a scientific leader of FibroCure.

Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability, but the expense of conducting rudimentary EM-driven global optimization by means of popular bio-inspired algorithms is impractical. Similarly, nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidelity EM simulations and response feature technology within a kriging-based machine-learning framework for cost-effective global parameter tuning of microwave passives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landscape through the response feature method. The primary prediction tool is a co-kriging surrogate, while a particle swarm optimizer, guided by predicted objective function improvements, handles the search process. Rigorous validation demonstrates the proposed framework's competitive efficacy in design quality and computational cost, typically requiring only sixty high-fidelity EM analyses, juxtaposed with various state-of-the-art benchmark methods. These benchmarks encompass nature-inspired algorithms, gradient search, and machine learning techniques directly interacting with the circuit's frequency characteristics., (© 2024. The Author(s).)

This paper presents high-sensitivity sensors based on an open complementary square split-ring resonator and a modified open complementary split-ring resonator operating at 4.5 GHz and 3.4 GHz, respectively. The sensors are designed for the detection of multiple liquid materials, including distilled water, methanol, and ethanol. The liquid under test is filled in a glass container loaded using a pipette. Compared to the conventional OCSSRR, the modified OCSSRR with multiple rings exhibits a higher frequency shift of 1200 MHz, 1270 MHz, and 1520 MHz for ethanol, methanol, and distilled water, respectively. The modified sensor also demonstrates a high sensitivity of 308 MHz/RIU for ethanol concentration which is the highest among the existing microwave sensors. The sensors in this manuscript are suitable for multiple liquid-material-sensing applications.

In this article, the phenomena of beam deviation in reflectarray is discussed. The radiation pattern of the unit cell, which plays a vital role in shaping the beam of the reflectarray, is analyzed by considering undesired specular and scattered reflections. These unwanted reflections adversely affect the pattern of the single unit cell, thereby reducing the overall performance of the reflectarray. To conduct our investigations, three cases of reflectarray-i.e., (i) a center-fed with broadside beam (Case-I), (ii) a center-fed with the beam at 30° (Case-II), and (iii) off-center-fed with the beam at 30° reciprocal to feed position with reference to the broadside direction (Case-III)-are simulated. Different degrees of beam deviation are analyzed in each reflectarray by assessing the radiation pattern of a single element. The simulation results shows that maximum of 0°, 3.4°, and 0.54° beam squint across the bandwidth found in Case-I, Case-II, and Case-III, respectively; this leads to aperture efficiencies of 31.2%, 11.9%, and 31.2%, respectively. The significance of specular reflections is further confirmed by half (left half and right half) aperture analysis of Case-II. This involves simulating the half-plane aperture illuminated by horn antenna, resulting in a distinct beam angle at the same frequency. However, deviations of -4.71 to +4.1 for the left half aperture and -1.82 to +1.1 for the right half aperture are noticed. Although the analysis specifically focuses on the three cases of the reflectarray, the proposed methodology is applicable to any type of reflectarray. The study presented in this work provides an important insight into the practical aspects of reflectarray operation, particularly in terms of quantifying undesirable effects that are normally overlooked in the design of this class of arrays. To achieve a good performance, a new design of the dielectric loaded horn feed is proposed. This design approach is both simple and applicable to any reflectarray, with the added benefit of maintaining a low profile for the RA. Moreover, this work holds significant potential for remote sensing satellite systems as beam deviation can adversely impact data collection accuracy and compromise observation precision, resulting in distorted images, reduced data quality, and overall hindrance to the system's performance in capturing reliable information.

This paper presents a wideband multiple-input multiple-output (MIMO) antenna with high gain and isolation, as well as beam tilting capability, for 5G millimeter wave (MMW) applications. A single bow-tie antenna fed by a substrate-integrated waveguide (SIW) is proposed to cover the 28 GHz band (26.5-29.5 GHz) with a maximum gain of 6.35 dB. To enhance the gain, H-shaped metamaterial (MM)-based components are incorporated into the antenna substrate. The trust-region (TR) gradient-based search algorithm is employed to optimize the H-shape dimensions and to achieve a maximum gain of 11.2 dB at 29.2 GHz. The MM structure offers zero index refraction at the desired range. Subsequently, the MIMO system is constructed with two vertically arranged radiators. Another MM, a modified square resonator (MSR), is embedded between the two radiators to reduce the mutual coupling and to tilt the antenna main beam. Herein, the TR algorithm is again used to optimize the MSR dimensions, and to enhance the isolation to a maximum of 75 dB at 28.6 GHz. Further, the MSR can tilt the E-plane radiation by ± 20° with respect to the end-fire direction when alternating between the two ports' excitation. The developed system is validated experimentally with a good matching between the simulated and measured data., (© 2024. The Author(s).)

This communication presents a novel concept of microfluidically frequency-reconfigurable self-quadruplexing tunable antenna for quad-band applications. At the initial design stage, a substrate-integrated square cavity is divided into four unequal quarter-mode cavity resonators by inserting an X-shaped slot on the top surface of the cavity. Applying four 50-Ω microstrip feed-lines to these four quarter-mode cavity resonators enables quad-band operation with self-quadruplexing capabilities. The feed lines are organized orthogonally and off-center, which leads to port isolation greater than 32.3 dB. An equivalent network model is developed to validate the proposed antenna. To realize frequency reconfigurability, two microfluidic channels corresponding to each port are created by engraving the bottom surface of the cavity. To create a reconfigurable self-quadruplexing antenna, the channels are either filled with air or dielectric liquids of higher permittivity, so that the design offers independent tunability of the operating frequencies. As a proof of concept, the prototype of a self-quadruplexing tunable antenna is fabricated and validated through measurements. The antenna prototype occupies a footprint area of 0.37λ g 2 . The design exhibits frequency tuning ranges of 350 MHz (8.3%), 500 MHz (10.3%), 610 MHz (11.2%), and 845 MHz (14.1%) for the first, second, third, and fourth operating bands, respectively. In all bands and across the entire tuning range, the realized gains of the designed antenna exceed 4.05 dBi. The electromagnetic modeling responses agree extremely well with the measured characteristics., (© 2024. The Author(s).)

This paper presents a series-fed four-dipole antenna with a broad bandwidth, high gain, and compact size for 5G millimeter wave (mm-wave) applications. The single dipole antenna provides a maximum gain of 6.2 dBi within its operational bandwidth, which ranges from 25.2 to 32.8 GHz. The proposed approach to enhance both gain and bandwidth involves a series-fed antenna design. It comprises four dipoles with varying lengths, and a truncated ground plane. These dipoles are connected in series on both sides, running in parallel through a microstrip line. The proposed design significantly enhances the bandwidth, which extends from 26.5 to 40 GHz. This frequency range effectively covers the 5G bands of 28 and 38 GHz. The expedited trust-region (TR) gradient-based search algorithm is utilized to optimize the dimensions of the antenna components, resulting in a maximum gain of 11.2 dBi at 38 GHz. To further enhance the gain, modified H-shaped metamaterial (MTM)-based unit cells are integrated into the antenna substrate. The TR algorithm is employed once more to optimize the MTM dimensions, yielding a maximum gain of 15.1 dBi at 38 GHz. The developed system is experimentally validated, showing excellent agreement between the simulated and measured data., (© 2024. The Author(s).)

Small size has become a crucial prerequisite in the design of modern microwave components. Miniaturized devices are essential for a number of application areas, including wireless communications, 5G/6G technology, wearable devices, or the internet of things. Notwithstanding, size reduction generally degrades the electrical performance of microwave systems. Therefore, trade-off solutions have to be sought that represent acceptable compromises between the ability to meet the design targets and physical compactness. From an optimization perspective, this poses a constrained task, which is computationally expensive because a reliable evaluation of microwave components has to rely on full-wave electromagnetic analysis. Furthermore, due to its constrained nature, size reduction is a multimodal problem, i.e., the results are highly dependent on the initial design. Thus, utilization of global search algorithms is advisable in principle, yet, often undoable in practice because of the associated computational expenses, especially when using nature-inspired procedures. This paper introduces a novel technique for globalized miniaturization of microwave components. Our technique starts by identifying the feasible region boundary, and by constructing a dimensionality-reduced surrogate model therein. Global optimization of the metamodel is followed by EM-driven local tuning. Application of the domain-confined surrogate ensures low cost of the entire procedure, further reduced by the incorporation of variable-fidelity EM simulations. Our framework is validated using two microstrip couplers, and compared to nature-inspired optimization, as well as gradient-based size reduction. The results indicate superior miniaturization rates and low running cost, which make the presented algorithm a potential candidate for efficient simulation-based design of compact structures., (© 2022. The Author(s).)

Quantifying the effects of fabrication tolerances and uncertainties of other types is fundamental to improve antenna design immunity to limited accuracy of manufacturing procedures and technological spread of material parameters. This is of paramount importance especially for antenna design in the industrial context. Degradation of electrical and field properties due to geometry parameter deviations often manifests itself as, e.g., center frequency shifts or compromised impedance matching. Improving antenna performance at the presence of uncertainties is typically realized through maximization of the fabrication yield. This is normally carried out at the accuracy level of full-wave electromagnetic (EM) analysis, which incurs considerable computational expenses. The involvement of surrogate modeling techniques is the most common approach to alleviating these difficulties, yet conventional modeling methods suffer to a great extent form the curse of dimensionality. This work proposes a technique for low-cost yield optimization of antenna structures. It capitalizes on meticulous definition of the domain of the metamodel constructed for statistical analysis purposes. The domain is spanned by a limited number of essential directions being the most influential in terms of affecting antenna responses in the frequency bands of interest. These directions are determined through an automated decision-making process based on the assessment of the circuit response variability. Our approach permits maintaining small domain volume, which translates into low cost of surrogate model setup, while providing sufficient room for yield improvement. The presented method is validated using three antenna structures and favorably compared to several surrogate-assisted benchmark methods. EM-driven Monte Carlo simulation is also conducted to verify reliability of the yield optimization process., (© 2022. The Author(s).)

BACKGROUND/OBJECTIVES: Biological indicators of children's growth, such as the mean values for both height and weight, are often used as markers of change in living conditions. However, little is known about how the quality of life affects the variation in height and weight. Therefore, the aim of this study was to assess the variation in height and body mass index (BMI) in Polish children between 1966 and 2012. During this period, Poland underwent vast political and socioeconomic changes. SUBJECTS/METHODS: The study included 5597 boys and 5479 girls aged 7-8 years of age. Socioeconomic status (SES) was defined in three categories: high, medium and low. RESULTS: Between 1966 and 2012, the mean values for height and BMI significantly increased in both sexes (P < 0.001). The variation of these two parameters, however, showed a different pattern. Whereas the variation in Z-values for height remained unchanged in both sexes, the variation in BMI increased in boys (P < 0.01) but not in girls. SES affected the variation in Z-BMI in 1978 in both sexes (P < 0.001), whereas variation in Z-height between SES categories remained unchanged across all years of surveys in boys. Before the political transformation, significant regional differences were observed in the variances of Z-BMI (P < 0.05) but not of Z-height. This pattern changed after the political transformation, when regional differences in variances of Z-BMI disappeared. CONCLUSIONS: It is concluded that the mean values and the variation of BMI are affected by a changing quality of life, whereas the variation in height is usually independent of living conditions. European Journal of Clinical Nutrition (2016) 70, 662-666;doi:10.1038/ejcn.2016.30; published online 16 March 2016, INTRODUCTION Many studies have reported the influence of living conditions on mean values of both height and weight, which are considered biological indicators of children's growth. (1,2) These two anthropometric [...]