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.

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.

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.

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 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.

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.

Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward’s algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking–Wallis and Anderson–Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering.

Abstract The outbreak of the coronavirus disease 2019, caused by the SARS-CoV-2 virus, has prompted global health concerns. In response, researchers have been conducting investigations on active compounds in plants that may hold the potential to inhibit the proliferation of the virus. The aim of this study was to simulate and predict structural interactions of selected compounds isolated from 28 endemic plants of Bosnia and Herzegovina against the main protease (Mpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), spike glycoprotein and uridylate-specific endoribonuclease (NendoU) of SARS-CoV-2. The majority of compounds, especially hesperidin, showed great binding affinity to the target proteins. The highest affinity for Mpro was observed for genistein and hesperidin, while in terms of structural interactions, both compounds achieved interactions of interest. Hesperidin and luteolin were the compounds with the highest binding affinity for PLpro, but no significant interactions were observed. For RdRp, hesperidin and quercetin showed the highest binding affinity, where both compounds formed interactions of interest. Hesperidin and fisetin were the compounds with the highest binding affinity for spike glycoprotein, and both compounds achieved significant interactions. The highest affinity for NendoU was obtained for hesperidin and isorhamnetin, where both compounds formed interactions of interest. Although these findings appear encouraging, further research is needed, which includes in vitro and in vivo assessments, along with clinical trials, to provide evidence for the potential therapeutic uses of these plants.

In a ternary mixture with the Soret effect, the interplay between cross-diffusion, thermodiffusion, and convection can lead to rich and complex dynamics including spatial patterns and oscillations. We present an experimental and three-dimensional numerical study of dynamic regimes in the toluene-methanol-cyclohexane ternary mixture with the Soret effect in the geometry of a thermogravitational column. An important feature of the system is that for the first component, toluene, the Soret and thermodiffusion coefficients have opposite signs, which triggers the oscillatory instability. Our experiments and numerical analysis show that the primary long-wave instability manifests itself in the form of a standing wave, and the secondary one emerges in the form of a swinging pattern. The computational model provides insight into the role of cross-diffusion coefficient D12 in the emergence and development of oscillatory instability. This study demonstrates that the long-wave oscillatory instability in transverse direction occurs only within a limited range of the D12 values and outside of this range it decays to a stationary pattern of either Turing-like or monotonic instability.