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Background: Language analytic ability is well researched in the context of foreign language acquisition but its role in the acquisition of grammar in the native language is under investigation. Objectives: Our study explored the influence of language analytic ability and print exposure on receptive grammar and reading comprehension in childhood. Additionally, we investigated whether exposure to specific constructions through the written modality held an advantage over exposure in the audio modality. Method: We assessed the language analytic ability, reading comprehension, reading fluency, print exposure, and receptive grammar of 12-year-olds. Subsequently, we exposed them to written or spoken target constructions, followed by an assessment of receptive grammar. Linear regression models were used to analyse the contributions of reading fluency, print exposure, and language analytic ability to reading comprehension and receptive grammar. We also examined the influence of the intervention on receptive grammar. Results: Language analytic ability and print exposure significantly predicted receptive grammar. Print exposure significantly predicted reading comprehension and improvement in receptive grammar. Conclusion: Language analytic ability is important for grammar and reading comprehension development. Print exposure enhances reading comprehension by supporting vocabulary development and providing exposure to intricate structures. Both language analytic ability and experience are key factors in construction acquisition. Contribution: This study adds to the growing body of evidence emphasising the role of language analytic skills in native language grammar acquisition and advocates for explicit grammar teaching. Furthermore, it underscores the importance of adequate print exposure in grammar acquisition and the development of reading comprehension skills.

This paper investigates publication strategies of the most distinguished local government researchers in eight European countries. Drawing upon dependency theory, accumulative advantage theory and 'utility maximizing' theory, we compare publication strategies of scholars from countries that vary in terms of the distance from the core of academic knowledge production, also taking into consideration their career length (academic age). The two publication strategies compared are international visibility and domestic visibility. The analysis confirmed the hypothesis that younger scholars are more prone to adopt internationalization strategy which has been institutionally incentivized by NPM reforms. However, the difference among core, catching-up and peripheral countries is not as sizeable as expected.

Abstract 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.

Abstract 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 (NO2), 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 NO2 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 NO2 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 NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 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 NO2 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/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Abstract Although global vaccination campaigns alleviated the SARS-CoV-2 pandemic in terms of morbidity and mortality, the ability of the virus to originate mutants may reduce the efficacy of vaccines, posing a serious risk of a renewed pandemic. There is therefore a need to develop small molecules capable of targeting conserved viral targets, such as the main protease (Mpro). Here, a series of benzisoselenazolones and diselenides were tested for their ability to inhibit Mpro; then the most potent compounds were measured for antiviral activity in vitro, and the mechanism of action was investigated. Density functional theory calculations, molecular docking and molecular dynamics simulations were also used to elucidate the protein/drug interaction. Finally, a bio-organic model was established to study the reaction between selenorganic compounds and biologically relevant thiols to unveil possible metabolic pathways of such compounds. The overall results contribute to the identification of a series of novel Se-containing molecules active against SARS-CoV-2 and to the clarification of some important aspects in the mechanisms of action of such inhibitors targeting SARS-CoV-2 Mpro.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract Increasing access to water quality tests in low-income communities is a crucial strategy toward achieving global water equality. Recent studies in the Water Sanitation and Hygiene (WASH) sector underscore the importance of addressing practical concerns in water testing, such as robustness and results communication. In response, we present the WaterScope testing kit; an open-source, validated platform for drinking water quality assessment. It modernises the testing process with the inclusion of a unique cartridge/slider mechanism, machine-learning-enhanced classification and full digitalisation of results. WaterScope’s equivalency to conventional methods for quantifying E. coli is established through extensive validation experiments in both laboratory and field environments. This versatile platform provides potential to expand its applications to test other bacteria, perform colorimetric assays, and analyse clinical samples such as blood/urine samples. We anticipate that the system’s ease-of-use, portability, affordability, robustness, and digital nature will accelerate progress toward global water equality.

Abstract 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.

Abstract 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 mm2. 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 × 16mm3 (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.

Kinga Dabrowska-Kloda,1 Eydis Olafsdottir,1 Anastasia Stefanou,1 Sven Crafoord1,2 1Department of Ophthalmology, Örebro University Hospital, Örebro, Sweden; 2Department of Ophthalmology, Faculty of Medicine and Health, Örebro University, Örebro, SwedenCorrespondence: Kinga Dabrowska-Kloda, Department of Ophthalmology, Örebro University Hospital, PO Box 1613, Örebro, SE 70116, Sweden, Tel +46 19 6021000, Fax +46 19 6021052, Email kinga-magdalena.dabrowska-kloda@regionorebrolan.sePurpose: This study reports clinical outcomes up to 6 years after Descemet’s membrane endothelial keratoplasty (DMEK) performed at the Department of Ophthalmology, Örebro University Hospital, Örebro, Sweden.Methods: The study has a cross-sectional and case series design. Inclusion criteria were all DMEK-operated eyes during 2013– 2018 until repeat keratoplasty.Results: Altogether 162 eyes from 120 patients were enrolled. Among eyes without preoperative comorbidities, examined 1– 6 years after DMEK, 85.8% achieved visual acuity of 0.1 logMAR or better. The median endothelial cell density (ECD) loss was 27% in a cohort of eyes examined 1– 2 years post-DMEK, 31% at 2– 3 years, 42% at 3– 4 years, and > 60% at 4– 6 years post–DMEK. ECD at the examination timepoint was correlated with donor ECD (as expected) and time since surgery.Conclusion: The results from DMEK surgeries in Örebro, Sweden, are promising. Further studies with even longer follow–up are needed to evaluate graft sustainability.Keywords: DMEK, Descemet’s membrane endothelial keratoplasty, endothelial cell density, endothelial cell loss, corneal surgery in Sweden, corneal surgery in Scandinavia

The research problem is the influence of global crisis on identity and daily life of Montenegrin people. The subject is the political construction of (Montenegrin) identity, as a result of social control on everyday life, where identities are born. For the purpose of researching this relation, the starting point is the explanatory potential of J. Habermas’s contemporary socio-political theory about the colonization of the lifeworld by an expansionist logic of the political-economic constellation (system). Scientific goals are: 1) description of the colonization of the lifeworld and the construction of (Montenegrin) identity, as an indicator of social crisis (in Montenegro); 2) understanding the causal factors (causes, motives) of (Montenegrin) identity construction; 3) generic explanation of the political construction of (Montenegrin) identity, through strategies, policies, educational and media influences. Social goals refer to: 1) expansion of the fund of knowledge about contemporary (Montenegrin) society and its problematic aspects; 2) pragmatic explanations of the problem of social control of everyday life through the re-actualization of socio-political concepts; 3) indication of guidelines for possible changes for the better in the sphere of everyday life in the foreseeable future. The methodology concerns the main hypothesis that by constructing the identity of people in the post-industrial society, social control of everyday life is realized. Auxiliary hypotheses are: 1) Identity construction takes place through strategies, policies, educational and media influences as segments of social control aimed at managing social resources from the local to the global level. 2) With instruments of social control, a desirable system of values of individuals and social groups is established with the aim of preventing, delaying, compensating, and transforming their potential subversive action (undermining the system from within). The independent or causal variable is the social control of everyday life, while the dependent or consequential variable is the constructed identity. Making final findings will be facilitated by the synthesis, inductive and deductive methods of reasoning, as well as the method of comparison with a case study. The results refer to the achieved goals and the general conclusion that the system’s power logic reduces the sphere of free and open communication of individuals, thus their identity becomes a place of permanent crisis due to the threat of the meaning of everyday life within which identity is formulated. This crisis is the reason that identities will remain endangered until the question of alienation of life is raised, in accordance with the capitalist imperatives of unlimited economic growth and consumption, and instrumental-rational interaction. As a reaction to the system dominance, the forces of resistance are strengthening, in the form of new alternative grassroots social movements, such as Civil Movement United Reform Action (URA), in Montenegro, which is pro-European green political party of the left center and social-liberal ideological provenance.

The aim of this study was to determine the predispositions of the studied groups of students to work in the IT sector. The basis for predisposition assessment was their voluntary self-assessment of certain preferences, which are related to the theory of multiple intelligences of Professor Gardner. The study was conducted on a reference group of IT sector employees, assuming that they will be the model, to which the results of the study will be related. The method used to obtain data about the students' predispositions was a test carried out in an auditorium mode or online. More than 500 students from several countries were surveyed and interesting statistical material was obtained allowing for comparison between groups. The most important result was to find a way to sort the students into groups in order from most similar in their aptitude to the market pattern to least. This made it possible to determine the boundary beyond which students could be considered selected for a job in the IT sector. Statistical hypotheses about the similarity of the student groups to the reference group were verified. The results were both positive, confirming that a large percentage of students have predispositions to work in the IT market, and less promising. The authors are convinced that the method can be applied all over the world, as they examined groups in very diverse countries, taking into account, for example, location, education system, culture or religion.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2, SARS2) is responsible for the COVID-19 pandemic and infections that continue to affect the lives of millions of people worldwide, especially those who are older and/or immunocompromised. The SARS2 main protease enzyme, Mpro (also called 3C-like protease, 3CLpro), is a bona fide drug target as evidenced by potent inhibition with nirmatrelvir and ensitrelvir, the active components of the drugs Paxlovid and Xocova, respectively. However, the existence of nirmatrelvir and ensitrelvir-resistant isolates underscores the need to develop next-generation drugs with different resistance profiles and/or distinct mechanisms of action. Here, we report the results of a high-throughput screen of 649,568 compounds using a cellular gain-of-signal assay. In this assay, Mpro inhibits expression of a luciferase reporter, and 8,777 small molecules were considered hits by causing a gain in luciferase activity 3x SD above the sample field activity (6.8% gain-of-signal relative to 100 µM GC376). Single concentration and dose-response gain-of-signal experiments confirmed 3,522/8,762 compounds as candidate inhibitors. In parallel, all initial high-throughput screening hits were tested in a peptide cleavage assay with purified Mpro and only 39/8,762 showed inhibition. Importantly, 19/39 compounds (49%) re-tested positive in both SARS2 assays, including two previously reported Mpro inhibitors, demonstrating the efficacy of the overall screening strategy. This approach led to the rediscovery of known Mpro inhibitors such as calpain inhibitor II, as well as to the discovery of novel compounds that provide chemical information for future drug development efforts.

This paper presents a planar multiple-input-multiple-output (MIMO) antenna with dual circular polarization (CP) and a simple geometry for millimetre-wave band. The proposed design is featuring a fully grounded coplanar waveguide (CPW) and a systematically perturbed feedline radiator. The symmetry of the electric field is disrupted using varying stub sizes and offsets between the stubs, resulting in wideband operation and circularly polarized waves. Following full optimization of the physical parameters, the numerical design is validate experimentally. The results indicate that the proposed antenna design achieves a −10 dB impedance bandwidth from 24.6 GHz to 32.1 GHz, with an axial ratio (AR) of 3 dB or less from 26 GHz to 31.8 GHz. The peak realized gain is 10.3 dBic, with steady broadside radiation. The envelope correlation coefficient (ECC) is 0.01, and the diversity gain is 10 dB across the operating band. The planar structure and the wideband performance of the proposed design makes it suitable for application necessitating fix beam applications in the mm-wave band.

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