Both inhibitory control and typological similarity between two languages feature frequently in current research on multilingual cognitive processing mechanisms. Yet, the modulatory effect of speaking two typologically highly similar languages on inhibitory control performance remains largely unexplored. However, this is a critical issue because it speaks directly to the organisation of the multilingual's cognitive architecture. In this study, we examined the influence of typological similarity on inhibitory control performance via a spatial Stroop paradigm in native Italian and native Dutch late learners of Spanish. Contrary to our hypothesis, we did not find evidence for a differential Stroop effect size for the typologically similar group (Italian–Spanish) compared to the typologically dissimilar group (Dutch–Spanish). Our results therefore suggest a limited influence of typological similarity on inhibitory control performance. The study has critical implications for characterising inhibitory control processes in multilinguals.

Building Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety, to name just a few. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore’s CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information about building objects, a separate, ad-hoc model pre-processing is required to determine, for example, evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.

Multiple input multiple output (MIMO) systems, which use multiple antennas to deliver faster data rates, are one of the promising methods in 5G services. 5G is a popular issue among the world's main telecom firms currently. The sub-6 GHz band for 5G applications in various countries lies between 3 and 5 GHz. The sub-6 GHz 5G bands are 3.4–3.8 GHz in Europe, 3.1–3.55 GHz in the USA, and 3.3–3.6 GHz and 4.8–4.99 GHz in China. This paper presents a two-element slotted octagon-shaped antenna operating in the sub-6 GHz band (3.1–4.5 GHz) for 5G applications. A T-formed isolation structure is placed at a ground plane to minimize mutual coupling between MIMO antennas. The proposed MIMO antenna has physical dimensions of 55 × 38 mm2 and an envelope correlation coefficient or correlation of 0.0004 over the entire operating band. The antenna operates at 3.6 GHz, with a return loss of 40.8 dB at the resonance. An antenna prototype has been investigated and proven to be of excellent quality in terms of performance like isolation >20 dB, efficiency >80%, and mean effective gain <−3 dB over the full operating band.

Mung bean is highly susceptible to insect attack during storage. Hermetic storage is an effective technique to control insect damage. This study investigated the potential of the hermetic SuperGrain bag (SGB) for controlling bruchids during storage. The dry samples were packed in SGB infested with adult bruchids (SGB-I), SGB natural field infested (SGB-N), woven polypropylene bags (WPP-I and WPP-N) and kept at room temperature for 180 days. Oxygen (O2) and carbon dioxide (CO2) concentrations were measured at 15 days intervals. Moisture content, infestation level, seed damage and weight loss were determined at 60 days intervals. Seed colour, hardness, crude protein and fat contents were analysed before and after storage. The O2 level decreased to 10.09%, whereas the CO2 level increased to 8.87% in both SGB-I and SGB-N treatments. The moisture content of mung bean was maintained as onset storage in both SGB-N and SGB-I treatments, whereas reduced in WPP-N (9.26% db) and WPP-I (9.21% db). In SGB treatments, no significant bruchids were detected, but they increased drastically in WPP-N (52 ± 9) and WPP-I (377 ± 14). Seed damage (2–3%) and weight loss (0.8–1.0%) were recorded in both SGB-N and SGB-I. Conversely, seed damage reached 26.67 and 54.17%, corresponding to weight losses of 12.33 and 20.82% in WPP-N and WPP-I, respectively. Seed colour, hardness, crude protein and fat contents in SGBs showed no significant changes than in the WPP bags. The study illustrated that the SGB is an efficient hermetic device in protecting mung beans against bruchids attacks compared to the WPP bags.

COVID-19 pandemic continues to evolve and new variants like Delta and Omicron have been discovered. REGEN-COV is a recombinant human monoclonal antibody to the spike protein of SARS-CoV-2 which received emergency use authorisation for treatment and post-exposure prophylaxis in patients with high risk of progression to severe disease. We review our experience with use of REGEN-COV in paediatric heart transplant patients.

FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.

In the Singhbhum Craton of the Indian shield, the Remal granite-gneiss preserves felsic magmatic fabrics onto which a low-temperature segregation layering has been superposed. Planar, sub-horizontal to gently dipping layers (Sign1) comprise K-feldspar megacrysts, plagioclase and quartz, with the base of each layer defined by segregations of biotite. Sign2 consists of trough cross-bedded layers composed of K-feldspar phenocrysts, plagioclase and quartz with biotite schlieren defining the base of each layer. Microstructural features such as concentrically arranged mineral inclusions in K-feldspar phenocrysts and graphic intergrowth textures testify to the magmatic origin of these fabrics, with insignificant subsequent metamorphic reconstitution. The tectonic fabric S1 has developed sub-parallel to localized greenschist-facies mylonite bands, and is defined by weakly aligned flakes of biotite. Crystallographic preferred orientations away from the mylonitized domains show a strong alignment of K-feldspar, quartz and biotite parallel to the magmatic fabric due to efficient segregation during magmatic flow. Quartz crystallographic preferred orientations within the mylonitized domains show a strong preferred orientation and dextral asymmetry. Temperature constraints from synkinematic chlorites along with estimates of deformation temperature from quartz crystallographic preferred orientations indicate that mylonitization occurred at the lower limits of quartz crystal plasticity. The results of combined thermodynamic and multiphysics modelling studies show that felsic magmas can undergo significant convective motion for a wide range of crystallinities and water contents before solidification. Additionally, segregation layering resembling a gneissosity can develop at low temperatures owing to localized mylonitization and concomitant dissolution–precipitation of biotite.

Clinical observations and subjective judgements have traditionally been used to evaluate patients with muscular and neurological disorders. As a result, identifying and analyzing functional improvements are difficult, especially in the absence of expertise. Quantitative assessment, which serves as the motivation for this study, is an essential prerequisite to forecast the task of the rehabilitation device in order to develop rehabilitation training. This work provides a quantitative assessment tool for muscle weakness in the human upper limbs for robotic-assisted rehabilitation. The goal is to map the assessment metrics to the recommended rehabilitation exercises. Measurable interaction forces and muscle correlation factors are the selected parameters to design a framework for muscular nerve cell condition detection and appropriate limb trajectory selection. In this work, a data collection setup is intended for extracting muscle intervention and assessment using MyoMeter, Goniometer and surface electromyography data for upper limbs. Force signals and human physiological response data are evaluated and categorized to infer the relevant progress. Based upon the most influencing muscles, curve fitting is performed. Trajectory-based data points are collected through a scaled geometric Open-Sim musculoskeletal model that fits the subject’s anthropometric data. These data are found to be most suitable to prescribe relevant exercise and to design customized robotic assistance. Case studies demonstrate the approach’s efficacy, including optimally synthesized automated configuration for the desired trajectory.

Prostate cancer (PC) presents great challenges in early diagnosis and often leads to unnecessary invasive procedures as well as over diagnosis and treatment, thus highlighting the need for promising early diagnostic biomarkers. The aim of this review is to provide an up-to-date summary of chronologically existing metabolomics PC biomarkers, their potential to improve clinical PC diagnosis and to reduce the proliferation and monitoring of PC. The systematic research was conducted on PubMed in accordance with PRISMA guidelines to report PC biomarkers. The majority of the studies distinguished malignant from benign prostate and few explored the biomarkers associated with the progression of PC. The present review summarises the primary outcomes of most significant studies to extend our knowledge of PC metabolomics biomarkers. We observed divergent inter-laboratory technical procedures employing different statistical approaches produced abundant information regarding PC metabolites perturbation. Since PC metabolomics is still in its early phase, it is vital that we dig out the most specific, sensitive and accurate metabolic signatures and conduct more studies with milestone findings with comparable sample sizes to validate and corroborate the findings.