This paper represents a solution to the problem of automatization of a web page robustness score grading. Robustness of a web page is best defined as a property of a specific web page to keep its layout and style of elements after applying different modifications. The rapid development of web pages has enabled a quick creation of numerous web pages, but the question is what is the quality of those web pages in terms of robustness. Automatic grading enables a relatively fast way of creating a metric in terms of the score that specific web pages get after being tested for the level of robustness. The research framework consists of different technologies and concepts that have been used during the implementation of a practical solution. The paper describes data structures that have been used to represent web pages as well as the machine learning methods such as neural networks, used to calculate the robustness score.

With the arrival of COVID-19 pandemic in March of 2020 the first lock-down and closing of schools and faculties occurred. Educational institutions had to find a solution overnight. To ensure the continuity of schooling was a great challenge, the strategy of the faculties has only indicated digital transformation, and a small number of faculty staff was ready to face this new situation (according to the research had already been carried out). Teachers that had already used digital technologies and e-learning found it easier to form their classes in online environment that they were already familiar with. But those teachers that have never done so, found themselves facing a great challenge. Faced with the inability to hold classes in a traditional classroom way, they were challenged to quickly transition to online environment and ensure the completion of the academic year. With the arrival of the new academic year, the return to the classic way of classes was expected, but this did not completely occur. The pandemic did not cease and again higher education institutions face closing and classes are more and more held remotely. Levels of preparation and experience of teachers and students at the University of Mostar differ greatly. This paper has a goal of presenting experiences and needs of the teachers of the Faculty of Humanities and Social Sciences and ascertaining how these experiences can be, and how much they have been used relating to the situation we find ourselves in again.

This paper focuses on reviewing relevant work on autonomous ground systems for concrete bridge inspection. The current inspection of bridges is still based on visual inspection by inspectors or by using semi-destructive techniques. Current inspection practices require a large amount of time for inspection. In addition, complex scaffolding or expensive equipment is required for inaccessible areas, which also poses a risk to the safety of inspectors. These drawbacks could be overcome by using robotic systems equipped with non-destructive techniques (NDT). This paper presents the ground robotic systems that have been used in the inspection of concrete bridges, mainly for the localization of reinforcement, corrosion assessment and crack detection.

The vehicle routing problem is one of the most complex problems in the field of combinatorial optimization. Creating optimal routes leads to timely delivery of orders to end customers, which increases the efficiency of the company and enables maximum earnings. The problem of vehicle routing with a series of real-world constraints is called the rich vehicle routing problem (RVRP). The paper presents an approach to solving RVRP, where the asymmetric routing problem with a heterogeneous vehicle fleet, time windows, customer-vehicle constraints and a number of others is observed. The approach solves the problem in two phases, by dividing customers into clusters using a discrete metaheuristic Bat algorithm, and by solving the routing problem for each obtained cluster. The proposed approach has been tested for 26 days of delivery from large warehouses in Bosnia and Herzegovina. Significant savings were achieved compared to previously implemented approaches. All created routes were feasible. The approach automatically creates routes, and gives results in a shorter time than previously used approaches. Time does not increase significantly with the increase in the number of customers, which is a great advantage of the proposed approach.

In this paper, we present a novel algorithm – DRGBT (Dynamic Rapidly-exploring Generalized Bur Tree), intended for motion planning in dynamic environments. The main idea behind DRGBT lies in a so-called adaptive horizon, consisting of a set of prospective target nodes that belong to a predefined $\mathcal{C}$-space path, which originates from the current node. Each node is assigned a weight that depends on relative distances and captured changes in the environment. The algorithm continuously uses a suitable horizon assessment to decide when to trigger the replanning procedure. A comprehensive simulation study is performed, covering a variety of manipulators, where DRGBT is compared to a state-of-the-art algorithm. Results indicate some promising features of the proposed method.

An analog time of flight correlator designed in a 150 nm LFoundry CMOS process, capable of correlating photon pulses with an input clock for the use with single-photon avalanche diodes (SPADs) is presented. This correlator will allow highly sensitive and high precision indirect time of flight (iTOF) distance measurement with modulation frequencies up to 1 GHz and a distance resolution of 3 mm and 1.3 mm for a total measurement time of only $4\ \mu\mathrm{s}$ and $40\ \mu\mathrm{s}$, respectively. Additionally long integration times are possible, which guarantee operation with high background-to-signal-ratios (BSR). The small size and low power consumption of less than 1 mW allow the use of many correlators on a single chip. Two correlators and a quenching circuit are integrated on a chip with a size of $1.5 \times 1.3\ \text{mm}^{2}$. The size of a single correlator is $225 \times 143\ \mu\mathrm{m}^{2}$.

In this work a single photon avalanche diode (SPAD) based phase measurement circuit for distance measurements using continuously modulated light in a 150 nm CMOS technology is presented. An on-chip quadruple-voltage quenching circuit, allowing up to 7.2 V excess bias for external SPADs, generates pulses synchronous to the detection times of single photons. Circuit simulations show, that a precision of 0.54 mm can be achieved for distance measurements in low background light environments, in a measurement time of 200 μs. The efficiency of background light suppression can be improved by increasing the measurement time. Even a factor of 100:1 of background to measurement light should allow sub-cm precision given a sufficient measurement time. Correlation frequencies up to 1 GHz are possible. One correlator block has a size of 230×210 µm2 and the power consumption for each correlator is 391 µW.

Existing literature compares neuromarketing and traditional methods, making the questionable assumption that these are monolithic measurement alternatives all serving the same, predictive purpose. This study examines and empirically challenges this notion by relying on a neuroscientific perspective and a robust empirical study to examine the correspondence of expanded sets of diverse electroencephalogram (EEG) and survey advertising indicators. The key findings are that EEG and survey indicators measure different kinds of emotions (and attention) and that the newly developed, momentary EEG indicators are superior to the conventional, aggregated ones. The findings suggest that moment-to-moment EEG advertising indicators, such as peak emotions during branding moments, distinctively enhance advertising effectiveness evaluation and enhancement.

The National Association for the Education of Young Children recently revised its Developmentally Appropriate Practice (DAP), the standard for early childhood care and education. Josh Thompson and Zlata Stanković-Ramirez explore how DAP has evolved over time and what guidance it provides early childhood educators regarding the interaction between typical waves of child development, children’s individual characteristics, and social and cultural context.

BACKGROUND The Prostate Biopsy Collaborative Group risk calculator (PBCG RC) has a moderate discriminatory capability. This study aimed to create automated machine learning (AutoML) PBCG RC for predicting the probability of any-grade and high-grade prostate cancer (PCa). METHODS This retrospective, single-center study was carried out using the database with 832 patients who were subject to transrectal ultrasound-guided prostate biopsy with prostate-specific antigen (PSA) values from 2 to 50 ng/ml. Information about PBCG RC predictors was gathered for all patients. We used H2O, as an open-source platform for AutoML, where the set of 20 base learning algorithms were trained. The AutoML PBCG RC was compared in terms of discrimination, calibration, and clinical utility with the original PBCG RC. RESULTS PCa was detected in 341 (41%) men, and 159 (19.1%) of them had high-grade PCa. Our AutoML models demonstrated better discriminative ability than the original PBCG RC for detection of PCa (area under the curve [AUC]: 0.703 vs 0.628; P = 0.023) and high-grade PCa (AUC: 0.990 vs 0.717; P < 0.001). The decision curve analyses showed that AutoML models performed better. For high-grade PCa the PSA was the most important feature. CONCLUSIONS We applied ensemble techniques to create a freely available online PCa risk tool based on PBCG RC predictors and AutoML algorithms. The AutoML models drastically improved original model performance and the predictions of high-grade PCa were nearly perfect. However, new models should be used with a reserve, because external validation has not been performed yet.

Description Young generations are severely threatened by climate change Under continued global warming, extreme events such as heat waves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades (1–4). Younger generations are therefore expected to face more such events across their lifetimes compared with older generations. This raises important issues of solidarity and fairness across generations (5, 6) that have fueled a surge of climate protests led by young people in recent years and that underpin issues of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming (7), a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared with another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations (see the first figure), we estimate that children born in 2020 will experience a two- to sevenfold increase in extreme events, particularly heat waves, compared with people born in 1960, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future.

In March 2019, the World Health Organization (WHO) declared that humanity was entering a global pandemic phase. This unforeseen situation caught everyone unprepared and had a major impact on several professional categories that found themselves facing important ethical dilemmas. The article revolves around the category of biomedical and clinical engineers, which were among those most involved in dealing with and finding solutions to the pandemic. In hindsight, the major issues brought to the attention of biomedical engineers have raised important ethical implications, such as the allocation of resources, the responsibilities of science and the inadequacy and non-universality of the norms and regulations on biomedical devices and personal protective equipment. These issues, analyzed one year after the first wave of the pandemic, come together in the appeal for responsibility for thought, action and, sometimes, even silence. This highlights the importance of interdisciplinarity and the definitive collapse of the Cartesian fragmentation of knowledge, calling for the creation of more fora, where this kind of discussions can be promoted.

We studied clinical and immunological outcome of Covid-19 in consecutive CLL patients from a well-defined area during month 1–13 of the pandemic. Sixty patients (median age 71 y, range 43–97) were identified. Median CIRS was eight (4–20). Patients had indolent CLL (n = 38), had completed (n = 12) or ongoing therapy (n = 10). Forty-six patients (77%) were hospitalized due to severe Covid-19 and 11 were admitted to ICU. Severe Covid-19 was equally distributed across subgroups irrespective of age, gender, BMI, CLL status except CIRS (p < 0.05). Fourteen patients (23%) died; age ≥75 y was the only significant risk factor (p < 0.05, multivariate analysis with limited power). Comparing month 1–6 vs 7–13 of the pandemic, deaths were numerically reduced from 32% to 18%, ICU admission from 37% to 15% whereas hospitalizations remained frequent (86% vs 71%). Seroconversion occurred in 33/40 patients (82%) and anti-SARS-CoV-2 antibodies were detectable at six and 12 months in 17/22 and 8/11 patients, respectively. Most (13/17) had neutralizing antibodies and 19/28 had antibodies in saliva. SARS-CoV-2-specific T-cells (ELISpot) were detected in 14/17 patients. Covid-19 continued to result in high admission even among consecutive and young early- stage CLL patients. A robust and durable B and/or T cell immunity was observed in most convalescents.

This paper presents two schemes to jointly estimate parameters and states of discrete-time nonlinear systems in the presence of bounded disturbances and noise. The parameters are assumed to belong to a known compact set. Both schemes are based on sampling the parameter space and designing a state observer for each sample. A supervisor selects one of these observers at each time instant to produce the parameter and state estimates. In the first scheme, the parameter and state estimates are guaranteed to converge within a certain margin of their true values in finite time, assuming that a sufficiently large number of observers is used and a persistence of excitation condition is satisfied in addition to other observer design conditions. This convergence margin is constituted by a part that can be chosen arbitrarily small by the user and a part that is determined by the noise levels. The second scheme exploits the convergence properties of the parameter estimate to perform subsequent zoom-ins on the parameter subspace to achieve stricter margins for a given number of observers. The strengths of both schemes are demonstrated using a numerical example.

Purpose This study aims to apply the stimulus-organism-response framework to uncover the underlying mechanism by which the perceived helpfulness of online customer reviews (OCRs) drives behavioural intentions in mobile travel app commerce. Also, the current study explores how vendor-driven perceived usefulness of a product and its attributes influence the mediated relationship between perceived helpfulness of OCRs (OCRs helpfulness) and behavioural intentions. Design/methodology/approach The online survey (n = 151) was used to collect the data. The authors used structural equation modelling and the bias-corrected bootstrap method to test the proposed conceptual model for mediation and moderated-mediation effect. Findings Findings indicate that the perceived OCRs helpfulness has an indirect positive effect, via trust and attitude, on travel app downloading intention. Moreover, results suggest that the presence of vendor cues (vendor-generated informational content about a travel app) does not significantly moderate the mediating effect of perceived OCRs helpfulness on travel app downloading intention. Originality/value The present study reinforces the applicability of the warranting principle in the context of travel app commerce by exploring the relative effectiveness of customer-generated and vendor-generated informational content in influencing travel app downloading intention.