Abstract: The degradation of the environment is one of the most urgent challenges today. Since the industrial revolution, we have only known the model of linear economy that deals with the relationship between growth and consumption with the creation of large amounts of waste. As an alternative, a new the concept of the mod-ern economy, the circular economy. The underlying assumptions of such a system are characterised by a ten-dency towards efficient use, and recycling and re-use of resources asit would limit the negative environmental impacts of the economy, while reducing costs in economic activities with the aim of economic growth. Our goal in this paper is to highlight the role and significance of the Circular Economy and natural resources in the process of creation of competitive advantages in a globaly connected world as well as in Bosnia and Herzegovina. Our companies have preferred the mass production method of material wealth based on the mass consumption of natural resources as the main economic development method while pursuing high economic growth and maximum economic profit. These days, this economic development method faces various limitations. Many problems, such as mass generation of wastes exceeding the natural purification capacity, enormous damage environment, deepening of natural disasters and global warming, various disputes surrounding natural resources. This analysis highlights that the use of circular economy tools can help economic policy makers and researchers to take into account the impact on the environment during strategic planning activities and projections of economic growth in BiH.

A search is presented for a heavy resonance $Y$ decaying into a Standard Model Higgs boson $H$ and a new particle $X$ in a fully hadronic final state. The full Large Hadron Collider Run 2 dataset of proton-proton collisions at $\sqrt{s}= 13$ TeV collected by the ATLAS detector from 2015 to 2018 is used, and corresponds to an integrated luminosity of 139 fb$^{-1}$. The search targets the high $Y$-mass region, where the $H$ and $X$ have a significant Lorentz boost in the laboratory frame. A novel signal region is implemented using anomaly detection, where events are selected solely because of their incompatibility with a learned background-only model. It is defined using a jet-level tagger for signal-model-independent selection of the boosted $X$ particle, representing the first application of fully unsupervised machine learning to an ATLAS analysis. Two additional signal regions are implemented to target a benchmark $X$ decay into two quarks, covering topologies where the $X$ is reconstructed as either a single large-radius jet or two small-radius jets. The analysis selects Higgs boson decays into $b\bar{b}$, and a dedicated neural-network-based tagger provides sensitivity to the boosted heavy-flavor topology. No significant excess of data over the expected background is observed, and the results are presented as upper limits on the production cross section $\sigma(pp \rightarrow Y \rightarrow XH \rightarrow q\bar{q}b\bar{b}$) for signals with $m_Y$ between 1.5 and 6 TeV and $m_X$ between 65 and 3000 GeV.

By creating a dependable, transparent, and cost-effective system for forecasting and ongoing environmental impact monitoring of exploration and exploitation activities in the deep sea, TRIDENT seeks to contribute to the sustainable exploitation of seabed mineral resources. In order to operate autonomously in remote locations under harsh conditions and send real-time data to authorities in charge of granting licenses and providing oversight, this system will create and integrate new technology and innovative solutions. The efficient monitoring and inspection system that will be created will abide by national and international legal frameworks. At the sea surface, mid-water, and the bottom, TRIDENT will identify all pertinent physical, chemical, geological, and biological characteristics that must be monitored. It will also look for data gaps and suggest procedures for addressing them. These are crucial actions to take in order to produce accurate indicators of excellent environmental status, statistically robust environmental baselines, and thresholds for significant impact, allowing for the standardization of methods and tools. In order to monitor environmental parameters on mining and reference areas at representative spatial and temporal scales, the project consortium will thereafter develop and test an integrated system of stationary and mobile observatory platforms outfitted with the most recent automatic sensors and samplers. The system will incorporate high-capacity data processing pipelines able to gather, transmit, process, and display monitoring data in close to real-time to facilitate prompt actions for preventing major harm to the environment. Last but not least, it will offer systemic and technological solutions for predicting probable impacts of applying the developed monitoring and mitigation techniques.

In the interest of both enabling long-term autonomous monitoring of at-risk marine environments and raising awareness and capabilities among citizens, a heterogeneous system of marine robots was developed, integrated, and deployed on a mission in the Adriatic Sea. This paper details a use-case scenario for a team of marine robotic agents for the purpose of cooperative marine litter detection and mapping, while also including interested citizens in the loop and allowing them to serve as operators. Two Autonomous Surface Vehicles (ASVs), a Remotely Operated Vehicle (ROV), and a Smart Buoy were deployed in a real marine environment to demonstrate the cooperative abilities of this system.

Advances in applied mechanics have facilitated a better understanding of the recycling of heat and work in the troposphere. This goal is important to meet practical needs for better management of climate science. Achieving this objective may require the application of quantum principles in action mechanics, recently employed to analyze the reversible thermodynamics of Carnot’s heat engine cycle. The testable proposals suggested here seek to solve several problems including (i) the phenomena of decreasing temperature and molecular entropy but increasing Gibbs energy with altitude in the troposphere; (ii) a reversible system storing thermal energy to drive vortical wind flow in anticyclones while frictionally warming the Earth’s surface by heat release from turbulence; (iii) vortical generation of electrical power from translational momentum in airflow in wind farms; and (iv) vortical energy in the destructive power of tropical cyclones. The scalar property of molecular action (@t ≡ ∫mvds, J-sec) is used to show how equilibrium temperatures are achieved from statistical equality of mechanical torques (mv2 or mr2ω2); these are exerted by Gibbs field quanta for each kind of gas phase molecule as rates of translational action (d@t/dt ≡ ∫mr2ωdϕ/dt ≡ mv2). These torques result from the impulsive density of resonant quantum or Gibbs fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure against the density of field energy (J/m3). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this chemical potential diminishing in magnitude as the translational action of vapor molecules and quantum field energy content increases with temperature. These cases distinguish symmetrically between causal fields of impulsive quanta (Σhν) that energize the action of matter and the resultant kinetic torques of molecular mechanics (mv2). The quanta of these different fields display mean wavelengths from 10−4 m to 1012 m, with radial mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). Widespread neglect of the Gibbs field energy component of natural systems may be preventing advances in tropospheric mechanics. A better understanding of these vortical Gibbs energy fields as thermodynamically reversible reservoirs for heat can help optimize work processes on Earth, delaying the achievement of maximum entropy production from short-wave solar radiation being converted to outgoing long-wave radiation to space. This understanding may improve strategies for management of global changes in climate.

The concept of brand personality plays a crucial role in brand literature as consumers tend to anthropomorphize brands by attributing human characteristics to them. The creation of a brand personality that resonates with consumers leads to greater customer satisfaction and loyalty over the long term. This study investigates the mediating potential of brand personality dimensions, speci cally Competence and Sophistication, in the relationship between brand communication (both controlled and uncontrolled) as an antecedent and brand loyalty as an outcome. Using a sample of 340 users of a cosmetic brand, we employed structural equation modeling to analyze the data. Our results indicate that controlled communication signi cantly in uences both the Competence and Sophistication dimensions of brand personality, and that there are signi cant indirect effects of both controlled and uncontrolled communication through reference groups on loyalty mediated by personality dimensions. These ndings provide valuable insights for brand managers and marketers seeking to enhance brand loyalty by developing effective communication strategies that align with the desired brand personality dimensions.

This paper studies the dynamics of a class of host-parasitoid models with host refuge and the strong Allee effect upon the host population. Without the parasitoid population, the Beverton–Holt equation governs the host population. The general probability function describes the portion of the hosts that are safe from parasitism. The existence and local behavior of solutions around the equilibrium points are discussed. We conclude that the extinction equilibrium will always have its basin of attraction which implies that the addition of the host refuge will not save populations from extinction. By taking the host intrinsic growth rate as the bifurcation parameter, the existence of the Neimark–Sacker bifurcation can be shown. Finally, we present numerical simulations to support our theoretical findings.

Social networks have become an integral part of modern society, allowing users to express their thoughts, opinions, and feelings, and engage in discussions on various topics. The vast amount of user-generated content on these platforms provides a valuable source of data for sentiment analysis (SA), which is the computational analysis of opinions and sentiments expressed in text. However, most existing deep learning models for SA rely on minimizing the cross-entropy loss, which does not incorporate any knowledge of the sentiment of labels themselves. To address this limitation, a novel approach that utilizes an optimal transport-based loss function to improve sentiment analysis performance was proposed. Optimal transport (OT) metrics are fundamental theoretical properties for histogram comparison, and the proposed loss function uses the cost of the OT plan between ground truth and outputs of the classifier. The experimental results demonstrate that this approach can significantly reduce miss detections between positive and negative classes and suggest that using an OT-based loss function can effectively overcome the deficiency of existing SA models and improve their performance in real-world applications.

In research aimed at determining ways to protect the data of primary and secondary school students, as well as students and innovators who have submitted their ideas and innovations to innovation fairs in the territory of Republika Srpska, there is a lack of thoroughly analyzed methods and systems for protecting their ideas/innovations. This paper analyzes the most effective security algorithms for the protection of innovations and innovators from different categories. The objective of this work is to define the best prototype for protecting the identity database of innovators and innovations from the civil sector until their patent protection is granted in the territory of Bosnia and Herzegovina. By using the deductive method, we analyze various algorithms that function in a distributed environment. By comparing the advantages and disadvantages of existing algorithms, we suggest the application of the most appropriate one to meet the strategic decision-making needs of civil organizations.

In the present paper, we study the high-order above-threshold ionization of noble-gas atoms using a bi-elliptic orthogonal two-color (BEOTC) field. We give an overview of the SFA theory and calculate the differential ionization rate for various values of the laser field parameters. We show that the ionization rate strongly depends on the ellipticity and the relative phase between two field components. Using numerical optimization, we find the values of ellipticity and relative phase that maximize the ionization rate at energies close to the cutoff energy. To explain the obtained results, we present, to the best of our knowledge, for the first time the quantum-orbit analysis in the BEOTC field. We find and classify the saddle-point (SP) solutions and study their contributions to the total ionization rate. We analyze quantum orbits and corresponding velocities to explain the contribution of relevant SP solutions.

Recent studies of selective auditory attention have demonstrated that neural responses recorded with electroencephalogram (EEG) can be decoded to classify the attended talker in everyday multitalker cocktail-party environments. This is generally referred to as the auditory attention decoding (AAD) and could lead to a breakthrough for the next-generation of hearing aids (HAs) to have the ability to be cognitively controlled. The aim of this paper is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary analysis revealed an average AAD accuracy of 96%. Moreover, we observed a significant increase in auditory attention classification accuracies with our approach over the use of traditional AAD methods (7% absolute increase). Overall, our exploratory study could open a new avenue for developing new AAD methods to further advance hearing technology. We recognize that additional research is needed to elucidate the full potential of cepstral analysis for AAD.

Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model ’imagines’ the latent context and ’predicts the past’ while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.

Unlike pan-FGFR inhibitors, RLY-4008 was designed to be selective for FGFR2 and induces clinical responses in FGFR2-altered solid tumors without clinically significant FGFR1-mediated hyperphosphatemia and FGFR4-mediated diarrhea.

Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–“DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model’s sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.