Concrete structures, such as bridges or viaducts, play an important role in global road infrastructure. These types of structures are relatively expensive to build and they are susceptible to outer external influences, which in time deteriorate and lead to the reduction of their structural resistance. To reduce this effect, regular inspection is needed, which is often done manually by using specialized equipment to reach certain parts of bridges and viaducts. This process is both expensive and dangerous for the inspectors to conduct. Within the research project ASAP (Autonomous System for Assessment and Prediction of Infrastructure Integrity) in order to overcome these challenges, we have developed a prototype of a wall-climbing robot (WCR) for nondestructive testing (NDT). In this paper, different iterations of the developed WCR prototypes are presented. In four consecutive prototype designs, we have evaluated and upgraded the adhesion and locomotion system. Finally, a fifth prototype that carries the NDT equipment is presented. The final version of the WCR is equipped with robust and flexible adhesion that enables the robot to adhere to different types of surfaces. We have also addressed the challenges of integrating NDT equipment into the robot. To successfully conduct an inspection, besides the WCR, a safety system, control, and power systems are needed, which are further presented and discussed.

The aim of this study was to explore diplopia as a symptom of undetected COVID-19 infection or as a possible side effect of COVID-19 vaccination. We examined 380 patients with diplopia admitted to the Department of Ophthalmology of the University Hospital Centre Sestre milosrdnice in Zagreb, Croatia, from July 2020 to June 2022. After excluding patients with confirmed organic underlying diplopia causes or monocular diplopia, we linked the patient information with the national COVID-19 and vaccination registries. Among the 91 patients included in this study, previously undetected COVID-19 infection as the possible cause of diplopia was confirmed in five of them (5.5%). An additional nine patients (9.9%) were vaccinated within one month from the onset of their symptoms, while the remaining 77 had neither and were therefore considered as controls. The breakdown according to the mechanism of diplopia showed no substantial difference between the vaccinated patients and the controls. We detected marginally insignificant excess abducens nerve affection in the COVID-positive group compared with that in the controls (p = 0.051). Post-vaccination diplopia was equally common in patients who received vector-based or RNA-based vaccines (21.4 vs. 16.7%; p = 0.694). COVID-19 testing should be performed for all cases of otherwise unexplained diplopia. The risk of post-vaccination diplopia was similar in both types of vaccines administered, suggesting a lack of evidence linking specific vaccine types to diplopia.

Studies assessing the dynamics and duration of antibody responses following SARS-CoV-2 infection or vaccination are an invaluable tool for vaccination schedule planning, assessment of risk groups and management of pandemics. In this study, we developed and employed ELISA assays to analyze the humoral responses to Nucleocapsid and Spike proteins in vaccinated health-care workers (HCW) and critically ill COVID-19 patients. Sera of more than 1000 HCWs and critically ill patients from the Clinical Hospital Center Rijeka were tested across a one-year period, encompassing the spread of major SARS-CoV-2 variants of concern (VOCs). We observed 97% of seroconversion in HCW cohort as well as sustained anti-Spike antibody response in vaccinees for more than 6 months. In contrast, the infection-induced anti-Nucleocapsid response was waning significantly in a six-month period. Furthermore, a substantial decrease in vaccinees’ anti-Spike antibodies binding to Spike protein of Omicron VOC was also observed. Critically ill COVID-19 patients had higher levels of anti-Spike and anti-Nucleocapsid antibodies compared to HCWs. No significant differences in anti-Spike and anti-Nucleocapsid antibody levels between the critically ill COVID-19 patients that were on non-invasive oxygen supplementation and those on invasive ventilation support were observed. However, stronger anti-Spike, but not anti-Nucleocapsid, antibody response correlated with a better disease outcome in the cohort of patients on invasive ventilation support. Altogether, our results contribute to the growing pool of data on humoral responses to SARS-CoV-2 infection and vaccination.

In this work we introduce the concept and method of so-called cooperative solar generation forecasting, where geographically close data sources are utilized in order to improve forecasting accuracy. We devised and examined various largescale one-hour-ahead artificial neural networks based solar generation forecasting scenarios to prove the benefits of cooperation. The introduced cooperative solar generation forecasting method showed significant improvement in forecasting accuracy, especially when combined with previous generation data, where a root mean square error reduction of at least 50% could be achieved in the majority of cases. We believe these results point to a scientific and economical benefit of international cooperation in solar generation forecasting.

The rapid pace of change in technology, business models, and work practices is causing ever-increasing strain on the global workforce. Companies in every industry need to train professionals with updated skill-sets in a rapid and continuous manner. However, traditional educational models — university classes and in-person degrees— are increasingly incompatible with the needs of professionals, the market, and society as a whole. New models of education require more flexible, granular and affordable alternatives. MIT is currently developing a new educational framework called Agile Continuous Education (ACE). ACE describes workforce level education offered in a flexible, cost-effective and time-efficient manner by combining individual, group, and real-life mentored learning through multiple traditional and emerging learning modalities. This paper introduces the ACE framework along with its different learning approaches and modalities (e.g. asynchronous and synchronous online courses, virtual synchronous bootcamps, and real-life mentored apprenticeships and internships) and presents the MIT Refugee Action Hub (ReACT) as an illustrative example. MIT ReACT is an institute-wide effort to develop global education programs for underserved communities, including refugees, displaced persons, migrants and economically disadvantaged populations, with the goal of promoting the learner’s social integration and formal inclusion into the job market. MIT ReACT’s core programs are the Certificate in Computer and Data Science (CDS) and the MicroMasters in Data, Economics and Development Policy, which consist of a combination of online courses, bootcamps, and global apprenticeships. Currently, MIT ReACT has regional presence in the Middle East and North Africa, East Africa, South America, Asia, Europe and North America.

HEDY - Life in the AI era is a 2-year Erasmus+ project started in November 2021 targeting higher education audience. Its goal is to offer a comprehensive and shared view of how Artificial Intelligence (AI) is affecting our lives and reshaping our socioeconomic, cultural, and human environments and to define which topics related to AI are of interest to different university studies and how they should be addressed. Four specific free and accessible sources of information will be produced to reach these goals, the first of which is the Booklet, the subject of this paper. The Booklet is an essay defining the HEDY position on life in the AI era and its aim is to identify the challenges, opportunities and expected impact of AI on four different areas: business, governance, skills & competencies, and people & lifestyle. In this paper, we summarise the content of the Booklet. In particular, we describe our methodology to build our rationales based on collecting information from two sources: i) Literature survey, and ii) Focus groups. These two sources provide a unique contribution on AI panorama by combining state of the art research with first-hand opinions and debated questions, concerns, and ideas of interacting individuals. The main finding is that there is the necessity to train citizens in AI by providing teachings, courses and trainings in schools and higher education institutes to facilitate the use and adoption of AI for young people and future generations.

pH represents the concentration of free H+ in pine needles extracts (PNE) and is therefore an important initial parameter in quality control. Electrical conductivity and pH of samples of fresh and stored for 20 days of PNE with black cumin oil and olive oil had values of 0.00 due to the encapsulation of water molecules, pH and electrical conduction was not possible. The pH of the other samples was in a weakly acidic environment because the pH of natural pine needles is 3.8. Electrical conductivity values in all samples except pine needle extract and honey increased during storage. By monitoring the parameters of pH and electrical conductivity in the quality control of PNE, it gives us a significant insight into the physical state of the phases and the way of storage.

In order to reduce CO2 emissions, hydrogen combustion has become increasingly relevant for technical applications. In this context, lean H2-air flames show promising features but, among other characteristics, they tend to exhibit thermo-diffusive instabilities. The formation of cellular structures associated with these instabilities leads to an increased flame surface area which further promotes the flame propagation speed, an important reference quantity for design, control, and safe operation of technical combustors. While many studies have addressed the physical phenomena of intrinsic flame instabilities in the past, there is also a demand to predict such flame characteristics with reduced-order models to allow computationally efficient simulations. In this work, a H2-air spherical expanding flame, which exhibits thermo-diffusive instabilities, is studied with flamelet-based modeling approaches both in a-priori and a-posteriori manner. A recently proposed Flamelet/Progress Variable (FPV) model, with a manifold based on unstretched planar flames, and a novel FPV approach, which takes into account a large curvature variation in the tabulated manifold, are compared to detailed chemistry (DC) calculations. First, both FPV approaches are assessed in terms of an a-priori test with the DC reference dataset. Thereafter, the a-posteriori assessment contains two parts: a linear stability analysis of perturbed planar flames and the simulation of the spherical expanding flame. Both FPV models are systematically analyzed considering global and local flame properties in comparison to the DC reference data. It is shown that the new FPV model, incorporating large curvature variations in the manifold, leads to improved predictions for the microstructure of the corrugated flame front and the formation of cellular structures, while global flame properties are reasonably well reproduced by both models.

Background: Multiple sclerosis is a progressive inflammatory disease of the the central nervous system. Problems with sexual functions are the common features of multiple sclerosis and important factor that contribute to the quality of life among affected persons. Objective: The aim of the study was to evaluate the influence of sociodemographic and clinical characteristics on sexual functions domains of health related quality of life (HRQOL) in multiple sclerosis patients. Methods: This study included 100 MS patients treated at the Department of Neurology, Clinical Center University of Sarajevo. Inclusion criteria were an Expanded Disability Status Scale score between 1.0 and 6.5, age between 18 and 65 years, stable disease on enrollment. HRQOL was evaluated by the Multiple Sclerosis Quality of Life-54 questionnaire. Mann-Whitney and Kruskal-Wallis test were used for comparisons between sociodemographic and clinical characteristics and HRQOL scores. Results: Out of 60% of patients reported to have sexual dysfunction, and 55 % were female patients. Younger patients had statistical significant higher median value of sexual function score (91.68 vs. 58,28, p=0.001) and satisfaction with sexual life scores (62.5 vs 37.5 , p =0.019) comparing to older patients. Employed patients also showed statistical significant higher median value of sexual function score (82 vs. 66.7, p=0.003) comparing to unemployed patients and also statisticaly significant higher median scores considering satisfaction with sexual life among employed patients (p=0,001). There were no differences in sexual functions scores considering gender, marital status and education. Patients with higher level of disabilty, progressive type of disease, more relapses and longer diseas duration had statistical significant lower median value of sexual function score and also satifaction with sexual life scores, except for disease duration Conclusion: Aging, dysability and progression are major factors that contribute to lower sexual function scores and satisfaction with sexual life among multiple sclerosis patients. Althoug women reported sexual problems more often then men, impact of these problems on quality of life are similar in men and women with MS.

Specific emitter identification (SEI) is developed as a potential technology against attackers in cognitive radio networks and authenticate devices in Internet of Things (IoT). It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Due to the strong capability of deep learning (DL) in extracting the hidden features of data and making classification decision, deep neural networks (DNNs) have been widely used in the SEI. Considering the insufficiently labeled training dataset and large unlabeled training dataset, we propose a novel SEI method using semi-supervised (SS) learning framework, i.e., metric-adversarial training (MAT). Specifically, two object functions (i.e., cross-entropy (CE) loss combined with deep metric learning (DML) and CE loss combined with virtual adversarial training (VAT)) and an alternating optimization way are designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset. The simulation results show that the proposed method achieves a better identification performance than four latest SS-SEI methods.

Malware traffic classification (MTC) is a very important component of cyber security, and a number of the MTC techniques are based on deep learning (DL) with a strong capability of feature mining and classification. However, these DL-based MTC methods are heavily dependent on a large amount of network traffic samples. In the few-shot scenarios, these methods usually overfit and have poor classification performance. Considering that the update cycle of malware is faster and faster, and there are more and more types of malware, collecting enough training samples for all malware is very challenging, if not impossible. In this paper, a novel few-shot MTC(FS-MTC) method is proposed based on convolutional neural network (CNN) and model-agnostic meta-learning (MAML) algorithm. Specifically, the CNN is trained on samples from normal softwares by MAML rather than the conventional optimization methods, then the CNN is finetuned by a few samples from malware for MTC. Simulation results show that our proposed MAML-based FS-MTC can outperform the traditional MTC methods. The performance of our proposed method can reach up to 95.69%.