Aim of study: A two-year experiment (2021-2022) was conducted to assess the response of a local maize hybrid BL-43 to different water regimes (full irrigation, deficit irrigation and rainfed) at two distinguished pedo-climatic locations (Aleksandrovac and Butmir) in Bosnia and Herzegovina (BiH). Area of study: The field experiment was located in Aleksandrovac (near Banja Luka) and Butmir (near Sarajevo) in BiH. Material and methods: A randomized block design was adopted at both experimental locations with three replicates. An Excel-based irrigation tool was used to manage crop water requirements and irrigation scheduling. Main results: Crop response to water was affected by site-specific agronomic management, the duration of phenological stages and their interconnection with precipitation events. At both locations, the effect of the water inputs on grain yield was statistically significant confirming the beneficial impact of irrigation. The effect of water stress on yield was particularly pronounced at Aleksandrovac, which was under water and temperature stresses during flowering time. During both seasons and for all water regimes, the total average grain yield was greater at Butmir than at Aleksandrovac for 38% and 27%, respectively. Research highlights: This is the first experimental study conducted in BiH on the effect of irrigation on maize grain production under different pedoclimatic conditions. The study emphasizes the need for knowledge regarding the impacts that climate change is having on the productivity of one of the region's most important crops.

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein–Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Abstract The ODYSSEY OUTCOMES trial, comprising over 47 000 patient-years of placebo-controlled observation, demonstrated important reductions in the risk of recurrent ischaemic cardiovascular events with the monoclonal antibody to proprotein convertase subtilisin/kexin type 9 alirocumab, as well as lower all-cause death. These benefits were observed in the context of substantial and persistent lowering of low-density lipoprotein cholesterol with alirocumab compared with that achieved with placebo. The safety profile of alirocumab was indistinguishable from matching placebo except for a ∼1.7% absolute increase in local injection site reactions. Further, the safety of alirocumab compared with placebo was evident in vulnerable groups identified before randomization, such as the elderly and those with diabetes mellitus, previous ischaemic stroke, or chronic kidney disease. The frequency of adverse events and laboratory-based abnormalities was generally similar to that in placebo-treated patients. Thus, alirocumab appears to be a safe and effective lipid-modifying treatment over a duration of at least 5 years.

The maritime industry is witnessing a revolutionary transformation with the development of autonomous ships. Unmanned vessels employ advanced technologies such as artificial intelligence, especially machine learning, and intelligent agents for autonomous navigation. The successful integration of autonomous ships into existing maritime ecosystems requires a robust infrastructure capable of addressing various challenges, including interoperability of different ship systems. To ensure interoperability, it is crucial to establish a suitable information infrastructure. The implementation of ontologies within the information infrastructure of autonomous ships facilitates the use of intelligent agents. This paper discusses the concept of ontological information infrastructure tailored for autonomous ships and gives an example of an ontology designed specifically for autonomous vessel firefighting systems.

The aim of the research was to determine the possibility of predicting vertical jump height based on absolute and relative lower extremity strength. Thirty healthy and physically active male students (age: 20.84 ± 0.99 years; height: 179.46 ± 5.91 cm; body weight: 73.88 ± 6.43 kg) from the Faculty of Sports and Physical Education participated in this study. Absolute lower extremity strength was assessed using the one-repetition maximum (1RM) back squat, expressed in kilograms. Relative lower extremity strength was calculated by dividing the estimated 1RM back squat by the participants’ body weight, yielding the 1RM back squat relative to body weight (BW) (1RM/BW). Vertical jump height was measured using an Optojump system with two types of jumps: countermovement jumps (CMJ) and squat jumps (SJ). Statistically significant and moderately high correlations were observed between absolute and relative lower extremity strength variables and vertical jump height variables. Regression analysis results indicated statistically significant multiple correlations of 1RM absolute strength and 1RM/BW relative lower extremity strength as predictors of CMJ and SJ vertical jump height criteria variables. There is limited research on this topic conducted specifically on student populations, making this study a valuable foundation for future research. The research findings can serve as guidelines for the development of absolute and relative lower extremity strength, thereby contributing to improved vertical jump performance.

Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the ‘international first’ offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.

Local markets have been a special setting throughout human history. Apart from their important social role, they had immeasurable economic importance as primary forms of exchange of goods (trade). Nonetheless, they experienced numerous transformational changes that affected their functioning. Like other countries, Serbia has a long tradition of market activity. However, several novelties have been introduced in recent years. Among many, the process of e-fiscalization is the main issue. Therefore, the focus of our research is based on a qualitative analysis of online media content (news and comments) related to the fiscalization of market activity. The attitudes of different categories of participants (state authorities, vendors, and customers) were analyzed. LIGRE open-access software was used for this purpose. The results of the analysis showed conflicting parties. Legislators emphasize the exclusive positive effects, while vendors point to the negative side of fiscalization. As a third party, customers (service users) have an undefined attitude in relation to fiscalization (pros/cons/neutral). There is an agreement to introduce market activity into legal flows. However, the key prerequisite is the prior resolution of a number of problems (working conditions, business costs, market monopoly, etc.).

The existing data indicates a steady decrease in the grey wolf (Canis lupus) population of Bosnia and Herzegovina (BiH), but despite this there remains no official protective legislation in place for the species. In an attempt to address the issue of protective legislation, we initiated monitoring projects on the grey wolf in BiH with the support of the Rufford Foundation. The aim of these projects was to obtain data on the presence, activity, abundance, and behavior of wolves, while continuously expanding the area of monitoring. Monitoring has been conducted via camera trapping since 2015. Cameras were set up at several localities, at one of which a confrontation between a wolf and European brown bear (Ursus arctos) was recorded. Since these two apex predators have recolonized common regions and habitats across Europe, resource competition and the possibility of inter-specific conflict is more likely. These conflicts may jeopardize the continued existence and future expansion of populations of both bears and wolves in these recolonized habitats. Accordingly, it is very important to study the nature of their coexistence, and the resulting data is ultimately essential for helping to create or resume conservation management plans for both species. Moreover, these data can help highlight areas for data collection and monitoring, thus providing important baseline information for survey planning.

Shortly after the first publication on the new disease called Coronavirus Disease 2019 (Covid-19), studies on the causal consequences of this disease began to emerge, initially focusing only on transmission methods, and later on its consequences analyzed in terms of gender, age, and the presence of comorbidities. The aim of our research is to determine which comorbidities have the greatest negative impact on the worsening of the disease, namely which comorbidities indicate a predisposition to severe Covid-19, and to understand the gender and age representation of participants and comorbidities. The results of our study show that the dominant gender is male at 54.4% and the age of 65 and older. The most common comorbidities are arterial hypertension, diabetes mellitus, and cardiovascular diseases. The dominant group is recovered participants aged 65 and older, with comorbidities most frequently present in this group. The highest correlation between patients with different severity of the disease was found with cardiovascular diseases, while the coefficient is slightly lower for the relationship between patients with different disease severity and urinary system diseases and hypertension. According to the regression analysis results, we showed that urinary system diseases have the greatest negative impact on the worsening of Covid-19, with the tested coefficient b being statistically significant as it is 0.030 < 0.05. An increase in cardiovascular diseases affects the worsening of Covid-19, with the tested coefficient b being statistically significant as it is 0.030 < 0.05. When it comes to arterial hypertension, it has a small impact on the worsening of Covid-19, but its tested coefficient b is not statistically significant as it is 0.169 > 0.05. The same applies to diabetes mellitus, which also has a small impact on the worsening of Covid-19, but its tested coefficient b is not statistically significant as it is 0.336 > 0.05. Our study has shown that comorbidities such as urinary system diseases and cardiovascular diseases tend to have a negative impact on Covid-19, leading to a poor outcome resulting in death, while diabetes mellitus and hypertension have an impact but without statistical significance.

Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi) media researchers. They call this area of study “Quality of Experience” (QoE). In this paper, we present a theoretical model based on review of previous QoE’s models. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them; therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. This novel top-down model serves as a practical guide for structuring QoE experiments, ensuring the incorporation of influential factors in a confirmatory manner.

Requirements elicitation has since long been recognized as critical to the success of requirements engineering, hence also to the success of systems engineering. Achieving sufficient scope and quality in the requirements elicitation process poses a great challenge, given the limited slices of budget and time available for this relatively sizeable activity. Among all predominant requirements elicitation techniques and approaches, operational scenarios development has a special standing and character. The set of operational scenarios is acknowledged as a constituent deliverable in the requirements engineering process, serving many purposes. Hence, ensuring success in the development of operational scenarios constitutes a consequential area of research. In this paper we present the results from an industrial survey on experienced and presumptive success factors in the development of operational scenarios. The survey was done using a strength-based approach, involving engineers and managers in two organizations developing cyber-physical systems in the transportation and construction equipment businesses. Our results suggest that operational scenarios reusability and a collaborative operational scenarios development environment are two prime areas for success. Our study provides two contributions. First, we provide an account of success factors in the view of practitioners. This is fundamental knowledge, since a successful deployment of any state-of-the-art approach and technology in a systems engineering organization needs to take the views of the practitioners into consideration. Second, the study adds input to the body of knowledge on requirements elicitation, and can thereby help generate suggestions on direction for future work by researchers and developers.

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.