The fact that automated vehicles will be part of road traffic raises the question of how human road users, like bicyclists or pedestrians, would safely interact with them. Research has proposed external human-machine interfaces (eHMIs) for automated vehicles as a potential solution. Concept prototypes and evaluations so far have mainly focused on young, healthy adults and people without disabilities, such as visual impairments. For a “one-for-all” holistic, inclusive solution, however, further target groups like children, seniors, or people with (other) special needs will have to be considered. In this workshop, we bring together researchers, experts, and practitioners working on eHMIs to broaden our perspective on inclusiveness. We aim to identify aspects of inclusive eHMI design that can be universal and tailored to any culture and will focus on discussing methods, tools, and scenarios for inclusive communication.

External human-machine interfaces (eHMIs) support automated vehicles (AVs) in interacting with vulnerable road users such as pedestrians. eHMI research has mostly dealt with investigating the communication an AV’s yielding intent, but there is little insight into how (or if) an eHMI should communicate an AV’s non-yielding intent. We conducted a video-based study (N = 25) with two eHMI concepts that offer pedestrians information about the vehicle’s non-yielding intent either explicitly or implicitly, and compared it with a baseline of an AV without an eHMI. Results show that while both kinds of eHMIs are effective and perform better than the baseline, there is no evidence of significant difference in road-crossing decision performance between explicit and implicit eHMIs in ambiguous situations. However, subjective feedback shows a trend of preference for eHMIs that communicate an AV’s intent explicitly at all times, although with a need for a significant distinction between the yielding and non-yielding messages.

A search for pair production of doubly charged Higgs bosons ( 𝐻 ±± ), each decaying into a pair of prompt, isolated, highly energetic leptons with the same electric charge, is presented. The search uses a proton–proton collision data sample at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 139 fb − 1 recorded by the ATLAS detector during Run 2 of the Large Hadron Collider. This analysis focuses on same-charge leptonic decays, 𝐻 ±± → ℓ ± ℓ (cid:48)± where ℓ, ℓ (cid:48) = 𝑒, 𝜇, 𝜏 , in two-, three-, and four-lepton channels, but only considers final states which include electrons or muons. No evidence of a signal is observed. Corresponding limits on the production cross-section and consequently a lower limit on 𝑚 ( 𝐻 ±± ) are derived at 95% confidence level. Assuming that the branching ratios to each of the possible leptonic final states are equal, B( 𝐻 ±± → 𝑒 ± 𝑒 ± ) = B( 𝐻 ±± → 𝑒 ± 𝜇 ± ) = B( 𝐻 ±± → 𝜇 ± 𝜇 ± ) = B( 𝐻 ±± → 𝑒 ± 𝜏 ± ) = B( 𝐻 ±± → 𝜇 ± 𝜏 ± ) = B( 𝐻 ±± → 𝜏 ± 𝜏 ± ) = 1 / 6, the observed lower limit on the mass of a doubly charged Higgs boson is 1080 GeV within the left-right symmetric type-II seesaw model, which is an improvement over previous limits. Additionally, a lower limit of 𝑚 ( 𝐻 ±± ) = 900 GeV is obtained in the context of the Zee–Babu neutrino mass model.

In this work, we adopt the analysis of a heterogeneous cellular network by means of stochastic geometry, to estimate energy and spectral network efficiency. More specifically, it has been the widely spread experience that practical field assessment of the Signal-to-Noise and Interference Ratio (SINR), being the key physical-layer performance indicator, involves quite sophisticated test instrumentation that is not always available outside the lab environment. So, in this regard, we present here a simpler test model coming out of the much easier-to-measure Bit Error Rate (BER), as the latter can deteriorate due to various impairments regarded here as equivalent with additive white Gaussian noise (AWGN) abstracting (in terms of equal BER degradation) any actual non-AWGN impairment. We validated the derived analytical model for heterogeneous two-tier networks by means of an ns3 simulator, as it provided the test results that fit well to the analytically estimated corresponding ones, both indicating that small cells enable better energy and spectral efficiencies than the larger-cell networks.

Background Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest. Objective This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster. Methods This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking “How to faster end the COVID-19 pandemic?” and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users' comments were collected and categorized into two major themes – personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users. Results The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results. Conclusions Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.

Tetracainium salicylate and tetracainium ibuprofenate were synthesized as active pharmaceutical ingredient ionic liquids (API-ILs). These ILs represent a combination of a drug for local anaesthesia (tetracaine) and nonsteroidal anti-inflammatory drugs (salicylic acid and ibuprofen). After IL synthesis, spectroscopic investigations were performed using infrared and nuclear magnetic resonance spectroscopy to confirm their structures. Differential scanning calorimetry and thermogravimetric analysis determined the obtained thermal behaviour of the ionic liquids. Experimental density, viscosity, and electrical conductivity measurements were performed in a wide temperature range to understand the interactions occurring in the obtained pharmaceutically active ionic liquids. All experimental values were well-fitted by the empirical equations. According to the theoretical calculations, weaker interactions of tetracaine with ibuprofenate than with salicylate are found, ascribed to the decreasing molecular symmetry, weakened hydrogen bonding, and increasing steric hindrance of ibuprofenate's alkyl chain.

The estimated percentage of individuals with COVID-19 due to infection with SARS-CoV-2 in need of hospitalization mostly increases proportionally with age, reaching almost 10% for those older than 60 years. Among hospitalized patients, one-fifth require treatment in the intensive care unit (ICU) due to acute respiratory distress syndrome, multiorgan failure, or hypoxemic respiratory insufficiency. Patients with moderate and severe COVID-19 who were hospitalized during the early stages of the pandemic and who continue to be hospitalized report fatigue, muscle weakness, joint stiffness, reduced mobility, increased risk of falls, and impaired quality of life. Physiotherapy is recognized to be important in the rehabilitation of COVID-19 patients requiring hospitalization. The current physiotherapy guidelines and recommendations for individuals with COVID-19, which include treatment methods and outcome measures for evaluation of the effects on respiratory and physical function and quality of life, are those established from the pre-COVID-19 era. The available extant scientific literature mainly reported the effect of physiotherapy in patients with COVID-19 in the acute, hospitalization courses of the disease, while there is a lack of quality primary, experimental studies on the effects of physiotherapy in rehabilitation of post-COVID-19 patients after hospitalization. This review aims to present an update on the effects of physiotherapy on rehabilitation and quality of life in patients hospitalized for COVID-19 and the findings from key studies published between 2020 and 2022.

disciplines and research areas that rely on participants’ self-reports to accrue data on participants’ true preferences are faced with the

This paper presents the results of a two-year study of six selected soybean genotypes with the aim of examining which of the genotypes in the given production conditions give the best results in regards with the amount and quality of seed yield. All genotypes belong to a zero-maturity group. The correlation between the grain yield per plant and other studied traits was tested through linear (simple) correlations. The testing showed that the following traits had a positive highly significant impact on seed yield: the number of seeds per plant (0.917**), seed germination energy (0.897**), seed moisture content (0.803**), plant height (0.802**), seed germination (0.789**), the number of seeds in pods (0.696**), the number of harvested plants per m-2 (0.590**), the number of plants (phenophase 1-3 in the three-leaf stage) per m2 (0.550**), 1000 seed mass (0.471**), and the height to the first node (0.412**).

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep-learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this article proposes three methods based on semisupervised learning (SSL), transfer learning (TL), and domain adaptive (DA), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the classification accuracy with few labeled samples. Then, we use the DA method to solve the mismatch problem between the source domain and the target domain in the TL process. The proposed method is not only applicable to the shallow network but also to the deep neural network structure, and can achieve better classification results. Experimental results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples in IIoT. The source code for all the experiments is available at GitHub.The code of this article can be downloaded from GitHub link: https://github.com/yzjh/Keras-MTC-DA-Ladder.

Abstract Lesser mole-rats (Nannospalax leucodon) are members of the Rodentia order’s Spalacidae family, and they are found in Northeastern Africa, the Balkans, Southeastern Europe, Central Asia, the Middle East, and Caucasia. The shape of the skull has a significant impact on the phenotypic appearance of animal heads, and although many domestic species have been studied, there is a lack of evidence on the macro-anatomical characteristics of the skeletal system in mole-rats. The current research was focused on the morphological, morphometric, and radiographic properties of lesser mole-rats skull in Bosnia and Herzegovina. The research was conducted on five lesser mole-rats from Bjelasnica Mountain, Bosnia and Herzegovina. We compared the results of the previously published studies, and we found a lot of similarities between Nannospalax leucodon in Bosnia and Herzegovina and Nannospalax ehrenbergi in North Iraq, as well as the Nannospalax nehringi from Eastern Anatolia.

Key Points • Rondaptivon pegol is a first-in-class prohemostatic molecule that prolongs the half-life of both endogenous FVIII and substituted FVIII.• Rondaptivon pegol could be used to enable once-weekly substitution therapy in severe hemophilia A or as prophylaxis in nonsevere hemophilia A.

We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.