Abstract In this note, we correct an oversight regarding the modules from Definition 4.2 and proof of Lemma 5.12 in Baur et al. (Nayoga Math. J., 2020, 240, 322–354). In particular, we give a correct construction of an indecomposable rank $2$ module $\operatorname {\mathbb {L}}\nolimits (I,J)$ , with the rank 1 layers I and J tightly $3$ -interlacing, and we give a correct proof of Lemma 5.12.

The ability to integrate two disparate materials-binding domains into a single ligand to achieve regiospecific binding would be powerful to direct material assembly; however, this has proven challenging to achieve due to cross-materials binding. Accomplishing this goal might be achieved by harnessing the precision of biology to exploit the recognition between peptides and specific nanomaterials. Here, a designed bifunctional molecule termed Biomolecular Exfoliant and Assembly Motifs (BEAM) is introduced, featuring two different materials-binding peptide domains, one for graphene and one for hexagonal boron nitride (h-BN), at each end of the molecule, separated by a fatty acid spacer. The BEAM is demonstrated to bind strongly to both graphene and h-BN surfaces, and in each case the materials-binding peptide domain is shown to preferentially bind its target material. Critically, the two materials-binding domains exhibited limited cross-domain interaction. The BEAM design concept shows substantial potential to eventually guide self-organization of a range of materials in aqueous media.

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene.

Background: Morphological dimensions represent the functioning of the growth and development of the musculoskeletal system and also play a role in specific volleyball activities. In the sphere of volleyball, it is imperative to identify the anthropometric constitution, generated by exogenous and endogenous factors. Aim study : The main goal of the research was to determine the factor structure of isolated latent dimensions of the population of female volleyball players of VC "Jahorina" Pale, a member of the volleyball Premier League of Bosnia and Herzegovina. Methods: The study involved 18 active players of the women's volleyball club Jahorina (BH=173±8.77cm; BW=66.04±9.09kg; BMI=22.03±2.19kg/m2, age=19.11±2.63 years old) members of the volleyball Premier League of Bosnia and Herzegovina. A set of 15 anthropometric variables was measured in the morphological space with the aim of determining correlations between anthropometric characteristics and identifying the latent morphological structure of volleyball players. Result: Statistical processing of the data was performed by Pearson correlation coefficients and Hoteling principal components analysis (PCA). The results of the correlation analysis showed statistically significant linear correlations between most anthropometric variables (p<0.05; p<0.01; p<0.001). The three-component model, which defined 81.45% of the proportion of the total common variance of the manifest anthropometric variables, was extracted by the method of analysis of the main components, with the Gutman-Kaiser criterion and Varimax rotation. Conclusion : It was structured by hypothetical factors with characteristic roots (Eig.>1), which were interpreted as Factor of volume and longitudinal dimensionality of the skeleton (F1=42.45%; Eig. 6.37>1), Factor of skin folds of the trunk (F2=28. 96; Eig. 4.08>1) and Subcutaneous adipose tissue factor of the lower extremities (F3=10.04; Eig.2.12>1). The correlation of the factors confirmed the inverse relationship of the factors (F1-F2=-0.85; F1-F3=-0.81). It can be concluded that the increased volume of volleyball players is not accompanied by higher values of skin folds, but is a consequence of increased muscle mass.

Intelligent edge orchestration has become a vital component within next generation communication networks, such as 5G. They offer optimal resource allocation and service distribution, hence allow for full utilization of the opportunities provided by those networks. Orchestrators make use of Machine Learning (ML) techniques to determine the most optimal operational decisions, such as deployment and scaling of services, ensuring the quality of the service performance. The training and validation of these models require significant amount of data. However, in such environments we deal with heterogeneous and distributed data sources, in which the data needs to be collected and pre-processed efficiently, and as such, made ready-to-use for these ML models. Hence, in this paper several state-of-the-art data management technologies suitable for edge computing and orchestration are investigated and compared. After investigating the theoretical features of these technologies, they are deployed and tested on the Smart Highway testbed. The strengths and shortcomings of these systems are presented and compared based on the Quality of Service (QoS) requirements for various vehicular services dealing with highly mobile users, i.e., vehicles, which are considered as quite stringent. This hybrid platform, combining several data management technologies, will be used to assist and enrich the research on smart edge orchestration at IDLab and serve as a reference for other interested parties.

Network slicing plays a key role in supporting the influx of smart devices and ensuring their connectivity, because it divides the network into different logical slices over the same shared infrastructure with the aim to guaranty Quality of Service (QoS), security and isolation, which are some of the challenges that IoT systems face. The aim of this paper is to research the possibilities using Network Slicing (NS) towards enabling healthcare services with secured data flows from smart devices to cloud and then back to end user, assuring security, isolation and the required levels of QoS. In this paper, we present the study of: i) isolation and security of network slices, which is important due to interference that occur between slices and can effect the data privacy, ii) service requirements, such as e.g., latency requirement for healthcare services, which is important to have an efficient diagnosis and rapid decisions in case of any risk, and iii) building a hybrid network slicing system, combining different technologies and applying slicing, which could bring the optimal solution for end-to-end for smart healthcare and smart living.

The next generation of vehicular communications has the potential to interconnect Unmanned Automated Vehicles (UAVs), which are the level 5 autonomous vehicles, with its nearby surroundings including Vulnerable Road Users (VRUs), other UAVs, and roadside infrastructure. Given the increased interest in the demand of autonomous driving and Cooperative Connected and Automated Mobility (CCAM), the future UAVs will be safer through enabling communication between UAVs themselves and with their surroundings e.g., VRUs. Communication between UAVs and VRUs will be needed since VRUs will not be able to make a physical eye-contact with the UAVs since their is no physical driver anymore behind the steering wheel. In this way the VRUs can make themselves digitally aware in the UAV traffic and vice versa. Such that UAVs will not be isolated black boxes, i.e., operating and relying fully on their embedded sensors, to its surrounding UAVs and VRUs. This paper shows the current and future needs for autonomous vehicular communication between UAVs and VRUs. Therefore, we present the current trends in the vehicular communication domain and its research challenges. We propose a solution to enable communication between semi-autonomous vehicles, UAVs and their surrounding e.g., VRUs. In our paper, we provide also an early initial proposal to fuse the Vehicle-to-everything (V2X) communication with the embedded autonomous software in the autonomous vehicle.

Intelligent edge orchestration has become a vital component within next generation communication networks, such as 5G. They offer optimal resource allocation and service distribution, hence allow for full utilization of the opportunities provided by those networks. Orchestrators make use of Machine Learning (ML) techniques to determine the most optimal operational decisions, such as deployment and scaling of services, ensuring the quality of the service performance. The training and validation of these models require significant amount of data. However, in such environments we deal with heterogeneous and distributed data sources, in which the data needs to be collected and pre-processed efficiently, and as such, made ready-to-use for these ML models. Hence, in this paper several state-of-the-art data management technologies suitable for edge computing and orchestration are investigated and compared. After investigating the theoretical features of these technologies, they are deployed and tested on the Smart Highway testbed. The strengths and shortcomings of these systems are presented and compared based on the Quality of Service (QoS) requirements for various vehicular services dealing with highly mobile users, i.e., vehicles, which are considered as quite stringent. This hybrid platform, combining several data management technologies, will be used to assist and enrich the research on smart edge orchestration at IDLab and serve as a reference for other interested parties.

Background Double-layer stents show promising results in preventing periinterventional and postinterventional embolic events in elective settings of carotid artery stenting (CAS). We report a single-center experience with the CGuard stent in the treatment of acute ischemic stroke (AIS) due to symptomatic internal carotid artery (ICA) stenosis or occlusion with or without intracranial occlusion. Methods We retrospectively analyzed all patients who received a CGuard stent in the setting of AIS at our institution. Neuroimaging and clinical data were analyzed with the following primary endpoints: technical feasibility, acute and delayed stent occlusion or thrombosis, distal embolism, symptomatic intracranial hemorrhage (sICH) and functional outcome at 3 months. Results In 33 patients, stenting with the CGuard was performed. Stent deployment was successful in all patients (28 with tandem occlusions, 5 with isolated ICA occlusion). Transient acute in-stent thrombus formation occurred in three patients (9%) without early stent occlusion. Delayed, asymptomatic stent occlusion was seen in 1 patient (3%) after 49 days. Asymptomatic periinterventional distal emboli occurred in 2 patients (6%), 1 patient experienced a transient ischemic attack 79 days after the procedure and 1 patient (3%) developed sICH. Favorable clinical outcome (mRS 0–2) at 3 months was achieved in 12 patients (36%) and the mortality rate was 24%. Conclusion The CGuard use in emergencies was technically feasible, the safety has to be confirmed by further multicentric studies.

Stroke is a medical condition in which the blood vessels in the brain rupture, causing brain damage. Symptoms may appear if the brain's flow of blood and other nutrients is disrupted. Stroke is the leading cause of death and disability worldwide, according to the World Health Organization (WHO). Early awareness of the numerous stroke warning symptoms can assist to lessen the severity of the stroke. To forecast the likelihood of a stroke happening in the brain, many machine learning (ML) models have been developed. This research uses a range of physiological parameters and machine learning algorithms, such as Support Vector Machine with extensive Exploratory Data Analysis, Random Forest Regression and PySpark. By using this methodologies and algorithms we got very high accuracy score results which are described down below.

Introduction: The education system of Federation of Bosnia and Herzegovina includes 60% of people with cerebral palsy (CP), hearing impairment and other associated difficulties, of which 37.50% in regular schools and 22.50% in special schools. From the total number of students, 44.44% attending schools according to the regular curriculum, and 55.56% according to the adjusted curriculum (1). Methods: The study is retrospective and analytical-descriptive. The study was conducted on a sample of 120 respondents from four cantons of the Federation of Bosnia and Herzegovina. Each Canton was used as one subsample of respondents. The data are presented in tabular form using classical descriptive statistics methods. The parametric statistics, analysis of variance (ANOVA) at the level of statistical significance of 0.05 was used to examine the statistical significance of differences between the sub-samples of the respondents. Results: The results of the study showed that persons with cerebral palsy, hearing impairment and other associated difficulties do not have adequate support in educational inclusion. Defectological support at school is provided for 17.50% of persons with cerebral palsy, hearing impairment and other associated difficulties, and only 6.66% of persons have the support of a teaching assistant. Conclusion: Persons with cerebral palsy, hearing impairment and other associated difficulties do not have adequate support in educational inclusion. There is no statistically significant difference between the subsamples of respondents when it comes to special education support and assistant support in the educational inclusion.

Next-generation sequencing (NGS) has been increasingly used in a wide range of research communities and in routine clinical practice and leads to an ever increasing amount of sequencing data. Sequencing data comes with, several challenges such as sharing, storing, integrating, analyzing, and interpretion. The management of the expanding amount of data is challenging and, especially for human omics data, privacy protection is crucial. Unraveling the causes of rare diseases is critically dependent on data sharing, but progress is hampered by regulations and privacy concerns. To overcome the concerns associated with centralized human genomic data storage, we developed a federated analysis platform, referred to as Widely Integrated NGS (WiNGS). The presented approach enables datasharing and combined data-analysis of omics data across a consortium without a centralized data store. Moreover, the platform incorporates extensive variant interpretation tools from genotype to phenotype for the diagnosis of rare developmental disorders.

Condition monitoring software is crucial for companies from all industrial branches that take care of the high availability of their automation systems. However, as the automation systems increase in complexity to support numerous business needs, the complexity behind the condition monitoring software development increases as well. It demands a deep understanding of various domain-specific requirements, state-of-the-art architectural concepts, and implementation technologies by software engineers. This paper copes with this complexity by proposing a model-driven approach to support software engineers in designing and implementing condition monitoring software. In that context, the paper contributes with a domain-specific language for condition monitoring software development (DSL4CMSD) along with a code generator that produces a set of python-based microservices. Furthermore, the paper discusses condition monitoring domain-specific requirements and presents a design process for their implementation using the DSL4CMSD. Finally, we evaluate the applicability of our modeling approach on the industrial heat exchanger monitoring case study.

Recent progress in calculating lepton density functions inside the proton and simulating lepton showers laid the foundations for precision studies of resonant leptoquark production at hadron colliders. Direct quark-lepton fusion into a leptoquark is a novel production channel at the LHC that has the potential to probe a unique parameter space for large masses and couplings. In this work, we build the first Monte Carlo event generator for a full-fledged simulation of this process at NLO for production, followed by a subsequent decay using the POWHEG method and matching to the parton showers utilizing HERWIG. The code can handle all scalar leptoquark models with renormalisable quark-lepton interactions. We then comprehensively study the differential distributions, including higher-order effects, and assess the corresponding theoretical uncertainties. We also quantify the impact of the improved predictions on the projected (HL-)LHC sensitivities and initiate the first exploration of the potential at the FCC-hh. Our work paves the way toward performing LHC searches using this channel.

This paper presents a combination of two methods that can be effectively combined for control of electrical machines. The first method enables real-time identification of electrical and mechanical parameters based on differential geometry and geometric algebra. The second method enables robust control of electrical machines, even when the knowledge about parameters is incomplete. In combination, the two methods open a path for successful control of electrical machines with unknown and/or time varying parameters.