Miracle-dz/UniFAR-FTUs huggingface dataset:
A Switchable Biomimetic Antenna Array

Biomimetic antenna arrays inspired by the fly Ormia ochracea showed a promising improvement of the angle estimation capabilities of radar sensors. So far, there is usually a loss of output power associated with this improvement decreasing the detectability of weak radar targets. In this letter, an electronically switchable two-element biomimetic antenna array is presented for the first time. This array provides the possibility to switch between a biomimetic antenna mode with enhanced phase sensitivity and a conventional antenna mode with a better signal-to-noise ratio. Circuit requirements are discussed, the design process is described, and a realization in the 77 GHz range using p-i-n diodes as switching elements is presented. Radar measurements verify the concept. By switching, the phase sensitivity of the realized array can be enhanced by a factor of three with a relative power loss of maximum 14 dB.
Business Travel GHG Emissions Analysis: Factors, Tools, and Cases for Calculating GHG Emissions and Setting Science-Based Targets for Organizations

Air travel is an increasing source of global GHG emissions, yet still many organizations have struggled to reduce business travel activity. Originating out of the COVID-19 pandemic and global travel restrictions, this working paper is part of a series addressing business travel reduction opportunities. This paper discusses how various organizations can and have conducted GHG emissions measurement on air travel, set science-based reduction targets, and improved accuracy and efficiency of data over time.
Is It Possible to Read Other People’s Confidence While Testing Their Implicit Learning?

Recent studies have shown that observers can accurately read a partner’s confidence in their decision without verbal information exchange. The main question of the present study concerns the possibility of an accurate reading of the metacognitive experiences of others if they make decisions based on implicit knowledge. The second question addressed the predictors of such mindreadings if they are possible. The experiment was conducted using an artificial grammar learning paradigm. Participants worked in dyads: the Learner implicitly learned to classify stimuli as grammatical and non-grammatical, and the Observer observed the classification process while having no access to the stimuli, and not communicating with the partner. The Observer’s judgment of the Learner’s confidence, as well as the Learner's judgment of his/her confidence, were recorded. The results demonstrate that the Learner's confidence judgments correlate with the Observer's judgments of his/her confidence. Moreover, only the confidence judgments of the Learner correlate with the classification accuracy. It is concluded that intrapersonal confidence judgments and interpersonal confidence judgments are partially guided by the same criteria (in particular, response time), however, the Learners’ judgments of their own confidence in the decisions are more sensitive to the implicit knowledge
UNCERTAINTY QUANTIFICATION FOR DEEP LEARNING REGRESSION MODELS IN THE LOW DATA LIMIT

Deep learning models have contributed to a broad range of applications, but require large amounts of data to learn the desired input-output mapping. Despite the success in developing prediction engines that have high accuracy, much less attention has been given to assessing the error associated with individual predictions. In this work, we study machine-learning models of uncertainty quantiﬁcation for regression, i.e., methods that are almost purely data driven and use deep learning itself to quantify the conﬁdence in its predictions. We use two approaches, namely the heteroscedastic and quantile formulations, and their extensions to problems with multidimensional output. We focus on the low data limit, where the data sets available are on the order of hundred, not thousands, samples. Through numerical experiments we demonstrate that both heteroscedastic and quantile formulations are robust and good at uncertainty estimation even in this low data limit. We note that the quantile formulation seems to have better performance and is more stable than the heteroscedastic case. Overall, our studies pave the way towards practical design of deep learning models that provide actionable predictions with quantiﬁed uncertainty using accessible volumes of data.
O36 Investigating clonal expansions in the normal stomach and the 3D architecture of oxyntic gastric glands

mm, 10–20 mm and >20 mm respectively. Three patients had EUS reported as normal/submucosal thickening only. 78 of the 81 biopsy results of SELs at initial endoscopy provided no diagnostic value. 47 (35.6%) patients underwent FNA of lesion, (0%,12% and 72% of patients for size <10 mm 10–20 mm and >20 mm respectively). 5 (3.8%) SELs were not sampled due to patient factors. 27 (57%) of EUSFNAs were diagnostic: 20 (42.5%) were proven GIST, 3 (6.3%) leiomyoma, 3 (6.3%) other malignancy and one lipoma. Only 2 (28%) FNAs of lesions <20 mm were diagnostic. All patients with SELs <20 mm all survived, except four who died of unrelated causes. Two had a resection and 36 underwent surveillance. Of the 53 patients with SELs >20 mm, 17 had resection/Imatinib, 25 underwent surveillance and two died of other causes. Conclusion EUS is a useful tool in the assessment, diagnosis and follow-up of small SELs. Management of lesions <20 mm remains controversial, however our patients with SEL <20 mm had no clinical sequelae and no EUS findings of concern during follow-up of at least one-year. Diagnostic yield of FNA for lesions <20 mm was low which suggests a more conservative, surveillance approach may be appropriate.
A diffused material interface based homogenization method for periodic composites

Abstract In this article, we propose a novel semi-analytical method to model complicated material interfaces by using a Gaussian kernel function and the resulting error function (erf) available in the symbolic toolbox of MATLAB. The proposed method provides an analytical expression for the smoothened material properties via a diffused representation of material interfaces that eliminates the requirement to track material interfaces explicitly while implementing the finite element method. In this article, we have demonstrated the proposed diffused representation approach for the homogenization of composites with periodic microstructures. We have studied the influence of varying shapes and sizes of inclusion on the overall effective material properties and the elastic response of the homogenized composites. However, the method is very generic and can be applied to obtain an analytical expression for smoothened material properties for any complicated material interface that may be useful to design high-performance topologically interlocked composites.
Multi-performance Target Collaborative Optimization Methods for Battery Electric Vehicle

The present studies on battery electric vehicles (BEVs) has mainly focused on the single-objective or weighted multi-objective optimization based on energy management, which can not manifest the coupling relationship among the vehicle performance objectives essentially. To optimize the handling stability, ride comfort and economy of BEV, this paper built the stability dynamics analysis model, ride comfort simulation half-car model and power consumption calculation model of BEV, as well as two-point virtual random excitation model on Level B road and proposed related evaluation indexes, including vehicle handling stability factor, weighted acceleration root-mean-square (RMS) value of vertical vibration at the driver’s seat and power consumption per 100 m at a constant speed. The Pareto optimum principle–based multi-objective evolutionary algorithm (MOEA) of BEV was also designed, which was encoded with real numbers and obtained the target values of all optional schemes via MATLAB/Simulink simulation software. The merits and demerits of alternative schemes could be judged according to the Pareto dominance principle, so that alternative schemes obtained after optimization were realizable. The results of simulation experiment suggest that the proposed algorithm can perform the multi-objective optimization on BEV, and obtain a group of Pareto optimum solutions featured by high handling stability, favorable ride comfort and low energy consumption for the decision-makers.
Study Protocol Feasibility and Pilot Trial of Brief Behaviour Change Counseling Using Five as and a Guiding Style From Motivational Interviewing on Lifestyle Behaviour in Patients With Hypertension or Diabetes in Mangochi, Malawi: BBCC+5 A's+GS Quasi-experimental Study Protocol

BackgroundDiabetes and hypertension and their lifestyle risk factors are on the increase in sub-Saharan Africa. Despite positive effects of behavioural intervention such as brief behaviour change in some African countries, there has not been significant implementation of the same in Malawi. We therefore decided to conduct this study aimed at implementing brief behaviour change counseling using 5 As and a guiding style from motivational interviewing (BBCC + 5 As + GS) in Mangochi with specific objectives of determining its feasibility and its efficacy.MethodsThis study is a mixed, quasi-experimental, feasibility study which comprises an exploratory 2 arms quantitative part with a qualitative component at the end of the trial. Fifty consecutive eligible participants will be recruited among patients attending two Non-Communicable Diseases (NCDs) clinics (Mangochi District Hospital and Monkey Bay Rural Hospital) and allocated equally to intervention group (receiving brief behaviour change counseling) and non-intervention (routine care only). In addition to a qualitative process evaluation at the end of the third time points, the groups will be assaessed at baseline - 12 weeks and 24 weeks’ post-implementation on the following factors: feasibility outcomes, sociodemographic factors, lifestyle factors, body mass index, waist circumference, hip to waist ratio, lipids (cholesterols, triglycerides), glycated hemoglobin, quality of life domains and theory of planned behaviour constructs’ changes and process evaluation. DiscussionIt is anticipated that the results of this pilot trial will help to determine and assess the feasibility and the provisional efficacy of this intervention, as well as key useful elements of this trial in order to develop it for further exploration in a larger trial.Registration and status of the trialTrial registration: ClinicalTrials.gov, NCT04625452, registered 12 November 2020, https://clinicaltrials.gov/ct2/show/NCT04625452, and recruitment: June 1, 2021.
Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicenter Dementia Registry (digiDEM Bayern): Mixed Method Study

Background Digital registries have been shown to provide an efficient way of gaining a better understanding of the clinical complexity and long-term progression of diseases. The paperless method of electronic data capture (EDC) during a patient interview saves both time and resources. In the prospective multicenter project “Digital Dementia Registry Bavaria (digiDEM Bayern),” interviews are also performed on site in rural areas with unreliable internet connectivity. It must be ensured that EDC can still be performed in such a context and that there is no need to fall back on paper-based questionnaires. In addition to a web-based data collection solution, the EDC system REDCap (Research Electronic Data Capture) offers the option to collect data offline via an app and to synchronize it afterward. Objective The aim of this study was to evaluate the usability of the REDCap app as an offline EDC option for a lay user group and to examine the necessary technology acceptance of using mobile devices for data collection. The feasibility of the app-based offline data collection in the digiDEM Bayern dementia registry project was then evaluated before going live. Methods An exploratory mixed method design was employed in the form of an on-site usability test with the “Thinking Aloud” method combined with an online questionnaire including the System Usability Scale (SUS). The acceptance of mobile devices for data collection was surveyed based on five categories of the technology acceptance model. Results Using the “Thinking Aloud” method, usability issues were identified and solutions were accordingly derived. Evaluation of the REDCap app resulted in a SUS score of 74, which represents “good” usability. After evaluating the technology acceptance questionnaire, it can be concluded that the lay user group is open to mobile devices as interview tools. Conclusions The usability evaluation results show that a lay user group generally agree that data collecting partners in the digiDEM project can handle the REDCap app well. The usability evaluation provided statements about positive aspects and could also identify usability issues relating to the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with diverse experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on these results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution for collecting data in a decentralized registry–based research project.
Arterial stiffness in children with primary hypertension is related to subclinical inflammation

Introduction The immune system can trigger an inflammatory process leading to blood pressure elevation and arterial damage. The aim of the study was to assess the relation between subclinical inflammation and arterial damage in pediatric patients with primary hypertension (PH) and to establish the usefulness of neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte (PLR) ratios, and mean platelet volume (MPV) as markers of arterial damage in these subjects. Material and methods In 119 children with PH (14.94 ±2.76 years) and 45 healthy children (14.91 ±2.69 years) we analyzed markers of subclinical inflammation (NLR, PLR, MPV), clinical and biochemical parameters, office blood pressure, ambulatory blood pressure monitoring (ABPM), central blood pressure, aortic pulse wave velocity (aPWV), augmentation index corrected for heart rates 75 (AIx75HR), carotid intima media thickness (cIMT), and common carotid artery stiffness (E-tracking). Results Children with PH were characterized by significantly higher neutrophil (3.9 ±1.7 vs. 3.0 ±1.0 [1000/µl], p < 0.001) and platelet counts (271.9 ±62.3 vs. 250.3 ±60.3 [1000/µl], p = 0.047), NLR (1.9 ±1.5 vs. 1.3 ±0.4, p = 0.010), PLR (131.4 ±41.9 vs. 114.7 ±37.6, p = 0.020), aPWV (5.36 ±0.88 vs. 4.88 ±0.92 m/s, p = 0.004), and cIMT (0.46 ±0.07 vs. 0.43 ±0.07 mm, p = 0.002) compared to healthy children. In PH children NLR correlated positively (p < 0.05) with: systolic, diastolic and mean blood pressure in ABPM (r = 0.243, r = 0.216, r = 0.251), aPWV [m/s] (r = 0.241), aPWV Z-score (r = 0.204), and common carotid artery PWVbeta [m/s] (r = 0.202). Conclusions There is a link between arterial stiffness and subclinical inflammation in pediatric patients with primary hypertension. Neutrophil-to-lymphocyte ratio may serve as a promising marker of arterial stiffness in pediatric patients affected by primary hypertension.
Making it Work for Everyone: Developing Flexible Digital Clinical Communication Modules for Health Disciplines in an Australian Context

Purpose: This paper discusses an innovative multi-disciplinary approach to the development and evaluation of flexible, digital clinical communication learning resources to support students in twelve health disciplines to develop clinical communication skills in preparation for their clinical placements. Methods: A modified Delphi survey was sent to all stakeholders in the Division of Health Sciences to identify the most important communication skills from the list of 61 communication skills identified by Bachmann et al in 2013. Lecturers, clinical educators, clinicians, and students were invited to rate the importance of each skill. Results: A set of digital resources was created for teaching clinical communication to students across all years and disciplines and for use by clinical educators in the Division to prepare students for clinical placement. In addition, an effective methodology for course development, involving broad consultation and multi-disciplinary development teams comprised of clinicians and other educational experts, was developed. The resources were implemented in two disciplines, podiatry and midwifery, and evaluated pre- and post-placement using a qualitative and quantitative method developed with the course coordinators. Conclusions: There are identifiable clinical communication skills which are applicable across numerous health disciplines and as for other learnable skills, teaching can be enhanced by breaking them down into specific, demonstrable elements which students can observe and practice. Using the pedagogy and methods outlined in the paper, students demonstrated increased awareness of the importance of communication in the clinical context, and the capacity to evaluate their performance of those skills.
Study on the Optical Properties of High Refractive Index TeO2-PbO-ZnO-BaF2 Glass System

The tellurite 60TeO2-20PbO-(20-x)ZnO-xBaF2 (x = 1, 3, 5, 6, 7, 9, and 11) series glass samples were prepared. The samples’ SEM, XRD, DSC, density, refractive index, and absorption spectra were tested. A self-made optical platform was used to measure and calibrate the refractive index. The relationship between glass composition and crystallinity was discussed. The effects of density, molar refractive index, and metal standard value on the refractive index of glass samples were studied. The results showed that TPZBF glass has a high refractive index, and the refractive index increases with the increase in BaF2 content.
Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment

The pandemic of COVID-19 has altered the way people learn. Learning has moved from offline to online throughout this pandemic. Predicting student performance based on relevant data has opened up a new field for educational institutions to improve teaching and learning processes, as well as course curriculum adjustments. Machine learning technology can assist universities in forecasting student performance so that necessary changes in lecture delivery and curriculum can be made. The performance of the pupils was predicted using machine learning techniques in this research. Open University (OU) educational data is examined. Demographic, engagement, and performance metrics are used. The results of the experiment. The k-NN strategy outperformed all other algorithms on the OU dataset in some circumstances, but the ANN approach outperformed them all in others.
Intravenous lidocaine in cancer-related neuropathic pain: case series

Introduction: Administering systemic lidocaine has been shown to deliver effective analgesia for both cancer-related and non-cancer pain. Adverse effects and toxicity are rare with controlled administration. Objective: To report the results obtained after the indication to manage with IV lidocaine infusion to control neuropathic pain flares in 9 cancer patients. Methodology: Observational, descriptive, case series-type study. A search was conducted in the files of the Pain and Palliative Care Service of the National Cancer Institute - Instituto Nacional de Cancerología - in Bogotá. Patients over 18 years old diagnosed with cancer, who experienced high intensity neuropathic pain and with the cognitive ability to rate their pain in a numerical analogue scale (NAS), without any absolute contraindications for the use of IV lidocaine were included; patients were assessed between September 27 and November 21, 2019. Results: 9 patients experiencing a pain flare-up which was characterized as neuropathic were registered, of which 89 % had some improvement following the administration of an initial lidocaine bolus. After one hour, 60 % reported over 40% improvement in the initial NAS. After 24 hours all patients had experienced some improvement, with a reduction of 46% in the pain scale as compared to the baseline. Conclusions: In this series of cases, the intravenous infusion of lidocaine as an option for the management of neuropathic pain flares seems to reduce pain intensity following the initial bolus administration.
Exact SER and BER Analysis on Jointly Optimal Maximum Likelihood Detection of Two QPSK Signals With Phase Offset

Analysis on the exact error rate of jointly optimal (JO) maximum likelihood (ML) multiuser detection is limited in the existing literature owing to its high computational complexity. In this paper, the JO ML detection of two quadrature phase-shift keying (QPSK) signals with an arbitrary relative phase offset is investigated. Exact, analytical symbol error rate (SER) and bit error rate (BER) formulas are derived for two users with no less than 3 dB power difference between them. Closed-form union bounds on the user SER and BER with an arbitrary phase offset and an arbitrary user power ratio are also given as a simpler approach to acquire the asymptotic error rate performance. Furthermore, insights on the potentials and properties of the JO ML detector regarding its error rate behaviors for each user are revealed through comparisons with the successive interference cancellation receiver. The accuracies of the obtained formulas are demonstrated through simulation. The results show the error rates for the two-user QPSK case no longer follow the classic knowledge on the error rate behaviors in the single signal detection. In addition, the performance and dB penalty employing different modulation types are also compared and discussed.
The effect of the use of indigenous knowledge-based Physics comics of Android-based marbles games on verbal representation and critical thinking abilities in Physics teaching

This research aims to reveal the effectiveness of the use of an indigenous knowledge-based physics comic of Android-based marbles games on verbal representation and critical thinking abilities. It is a quasi-experiment applying the pretest-posttest control group design. The research sample consists of two classes: the control and experimental classes, each of which consists of 35 students established using the cluster random sampling technique. The effectiveness of the indigenous knowledge-based physics comic of marbles games was analyzed using the quantitative method applying the effect size analysis. The result of the effect size analysis obtained from Cohen’s f in verbal representation ability is 0.11 interpreted as medium effect size and critical thinking ability is 0.43 interpreted as large effect size. This shows that the developed indigenous knowledge-based physics comic of Android-based marbles games in physics teaching gives effects to verbal representation and critical thinking abilities of the students. In other words, the developed comic is effective in improving verbal representation and critical thinking abilities.