The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

Knowledge is not merely the characteristic of certain pieces of information being deemed true and justifiable, but rather a process. Within an axiomatic system, the set of information that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the process of justification (exchange of information), if it consistently justifies a particular piece of information, it elevates it to knowledge. In this paper, we provide a framework for knowledge within an axiomatic model where we define the fundamental elements of communication and grammar. As a result, the structure dictates that knowledge, to be considered as such, must be investigateable by anyone who utilizes it in its entirety, both in form and content. Ultimately, the development of knowledge processing, referred to as \emph{knowmatics}, represents a clear evolution of the computer science that processes information (subset of knowledge).

The expansion of university enrollment and the implementation of tuition fees have become focal points of discussion in both current educational practices and theories. While increased university enrollment offers more opportunities for higher education, the high cost of tuition fees has pushed economically disadvantaged students to the brink of discontinuing their studies, prompting widespread concerns about educational equality.

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

This short technical note proves some observed novel corollaries of the Collatz Conjecture. For this, a unique ‘Collatz Representation’ of Hailstone numbers has been employed. After that, an extension of this corollary is explained using the fact that only certain differences in the last digit of the ‘Collatz Representation’ gives a whole number difference between numbers. A more general result is obtained. The final proof is for a corollary that some numbers the same number of steps to reach 1 if the Collatz Conjecture is true and all variables are whole numbers.

This research paper examines a cosmological model in flat space-time via $f(R,G)$ gravity where $R$ and $G$ are respectively the Ricci scalar and Gauss-Bonnet invariant. Our model assumes that $f(R,G)$ is an exponential function of $G$ combined with a linear combination of $R$. We scrutinize the observational limitations under a power law cosmology that relies on two parameters - $H_0$, the Hubble constant, and $q$, the deceleration parameter, utilizing the 57-point $H(z)$ data, 8-point BAO data, 1048-point Pantheon data, joint data of $H(z)$ + Pantheon, and joint data of $H(z)$ + BAO + Pantheon. The outcomes for $H_0$ and $q$ are realistic within observational ranges. We have also addressed Energy Conditions, $Om(z)$ analysis and cosmographical parameters like Jerk, Lerk and Snap. Our estimate of $H_0$ is remarkably consistent with various recent Planck Collaboration studies that utilize the $\Lambda$CDM model. According to our study, the power law cosmology within the context of $f(R,G)$ gravity provides the most comprehensive explanation for the important aspects of cosmic evolution.

This study aimed to investigate the current status of the Sustainable Workplace Equality Policy (SWEP) in an emerging country—Kuwait—and how it impacts firms’ financial and market performance. The study included companies listed in the Kuwait Boursa (Boursa Kuwait is the operator of the Kuwait Stock Exchange) in the period between 2012 and 2020. A disclosure index was prepared for SWEP based on guidelines provided by a combination of various sources and standards such as the Global Reporting Initiatives (GRI) standard, S&P global corporate sustainability assessment, Dow Jones sustainability index, United Nations global compact, and KPMG sustainability reporting standards. Time series regression analysis was used to examine the study hypotheses. The analysis revealed a strong positive relationship between the SWEP disclosure and firm measures of financial performance. The results indicate that SWEP is value-relevant and affects firms’ market value, suggesting that investors should consider firms’ disclosure of the SWEP when making investment decisions. The results of the current study are of interest to several stakeholders, especially investors and policymakers. Specifically, the study is relevant to the Kuwaiti Government, which has defined a clear path for sustainable growth with the Vision 2035/New Kuwait that is aimed at transforming the country into a financial and commercial hub for the region by 2035.

Human resources are the major input of health systems therefore the equitable distribution of human resources in health care remain critical in making progress towards the goal of universal health coverage and thus to sustainable development. The purpose of this study is to evaluate the health care human resources distribution across regions of Poland between 2010 and 2017 and estimate the level of equity. This research by applying to Polish conditions will allow to fill in the gap in existing literature. Data was derived from the Knowledge Database Health and Health Care of Statistic Poland and Polish Statistical Yearbook. In purpose to examine the distribution of health resources against population size and geographic size in Poland, the Gini coefficient calculated based on the Lorenz Curve was engaged. This study has several major findings. The amount of most types of human resources – as apart from internist, obstetrics and gynecology, occupational medicine - when expressed as number of them per 10,000 population or 1 square km increased in 2017 compared to 2010. Moreover, for most types of health care human resources the level of access slightly changed during analysied period as Gini coefficients decreased. However, the geographical distribution of all types of human resources is less equitable then in case of population distribution. The most troublesome is relatively lower equity in case of oncologist, family medicine and occupational medicine and cardiologist in both population and geographical distribution. Thus this research provides some implications for policy and practice. As the main reason to establish National Health Fund (which meant the centralization of the system) in Poland was to to eliminate regional differences in access to health care, thus this research confirms that still some more corrective actions, in this field, should be undertaken.

In the context of the Education-2030 Framework for Action, an important goal for initial STEM teacher education is to provide professional development on equality and gender awareness. This study explored whether STEM prospective secondary teachers are prepared to implement a sustainable gender-sensitive practice upon graduation. To this end, we cross-culturally validated the TEGEP (Teacher Self-Efficacy for Gender Equality practice) scale and compared STEM student teachers’ perceptions of self-efficacy by country and sex. Participants were 205 STEM (science, technology, engineering, and mathematics) secondary school student teachers (136 Greek and 69 Spanish) drawn from seven public universities (six Greek, one Spanish). Statistical analysis confirmed the structure and factor invariance of the TEGEP across country and between sexes showing evidence that gender equality self-efficacy level is only moderate and that perceived competence in gender knowledge was significantly higher in Greek than in Spanish STEM student teachers, while the latter felt more competent than the Greek in developing values and attitudes in regards to gender. The study provides a cross-validated instrument to measure gender equality self-efficacy in STEM teacher education and evaluate sustainable changes after planned interventions.

One of matrix equalities composed of multiple products of matrices and their generalized inverses is given by $A_1B_1^{-}A_2B_2^{-} \cdots A_kB_k^{-}A_{k+1}= A$ where $A_1$, $B_1$, $A_2$, $B_2$, $\ldots$, $A_k$, $B_k$, $A_{k+1}$, and $A$ are given matrices of appropriate sizes, and $B_1^{-}$, $B_2^{-}$, $\cdots$, $B_k^{-}$ are generalized inverses of matrices. The cases for $k = 1, 2$ and their special forms were properly approached in the theory of generalized inverses of matrices. In this note, the author presents an algebraic procedure to derive explicit necessary and sufficient conditions for the equality with $k = 3$ to always hold using certain rank equalities for the block matrices constructed by the given matrices, and then mention a key step of extending the previous work to a general situation.

We continue to analyze basic constraints on human's decision making from the viewpoint of quantum measurement theory (QMT). As has been found, the conventional QMT based on the projection postulate cannot account for combination of the question order effect (QOE) and the response replicability effect (RRE). This was an alarm signal for quantum-like modeling of decision making. Recently, it was shown that this objection to quantum-like modeling can be removed on the basis of the general QMT based on quantum instruments. In the present paper we analyse the problem of combination of QOE, RRE, and the famous QQ-equality (QQE). This equality was derived by Busemeyer and Wang and it was shown (in the joint paper with Solloway and Shiffrin) that statistical data from many social opinion polls satisfies it. Now, we construct quantum instruments satisfying QOE, RRE, and QQE. The general features of our approach are formalized with postulates which generalize {\it Wang-Busemeyer} postulates for quantum-like modeling of decision making. Moreover, we show that our model closely reproduces the statistics of the famous Clinton-Gore Poll data with a prior belief state independent of the question order. This model successfully removes the order effect from the data to determine the genuine distribution of the opinions in the Poll. The paper also provides a psychologist-friendly introduction to the theory of quantum instruments - the most general mathematical framework for quantum measurements. We hope that this theory will attract attention of psychologists and will stimulate further applications.

This article presents a combined route and path planning strategy to guide Skid-Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity, and ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrated the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

A description is provided of a method -- the Set Equality Method (SEM) -- to determine the validity, or lack of validity, of each categorical syllogism. A justification is given for the presentation of a new method to solve a problem which has already been solved using different approaches. First, the SEM assigns an equality of certain sets (or two of those equalities in specific cases as will be indicated) to each of the categorical propositions composing each syllogism considered -- that is, to each of the two premises and to the conclusion. Each syllogism considered is valid if and only if a) it is possible to select one of those equalities corresponding to one of the premises such that one of its members is a certain set and the other of those equalities corresponding to the other premise such that one of its members is a subset of the set mentioned, and b) it is possible to deduce an equality corresponding to the conclusion of the two equalities corresponding to the premises. In some cases, as will be specified, it is possible to provide a second test for the validity of a syllogism whose validity was already proven, thus providing information about the logical form of categorical syllogisms.

A project-based module on Sustainable Development Goal number 5, Gender Equality, was im-plemented on 5 different groups of Business English students consisting of a total number of 62 students in higher education. The main purpose of this project was to raise awareness of this goal by means of a flipped method in which students were required to carry out some research on specific areas of the aforementioned goal and work in teams to elaborate oral presentations. Once their findings were shared in class, students were expected to answer a written questionnaire of open-ended questions which were part of a qualitative analysis. Results of this survey showed that not only 90% of the students gained in depth knowledge of this goal, but also 85% had built a positive attitude to take initiative and 80% were optimistic about future gender equality. Finally, 70% of students suggested further social action to curb the problem of gender discrimination. On the whole, the flipped classroom method of learning combined with project-based group work have proven to be an effective way to raise awareness of this goal, create a more positive attitude, in-crease their willingness to take action as well as widening their English lexical resources.

This note reconsiders a matrix equality $A_1A_2^{-}A_3A_4^{-}A_5 = A$ composed by six matrices of appropriate sizes, where $A_2^{-}$ and $A_4^{-}$ are generalized inverses of $A_2$ and $A_4$, respectively, and solves a selection of matrix set inclusion problems associated with various mixed reverse order laws for generalized inverses of products of two, three, and four matrices by means of this equality and its variations.

Standard Model is well-performing under the three-generation, but in the wake of more physics, fourth-generation leptons and quarks are introduced. In this paper, We will be reviewing various constraints surrounding quarks and leptons of 4th Generation and tried to show the importance of quarks to understand the baryogenesis. We talked about, reviewed existing parameters and set the updated lower limits of t’ and b’ by analysis. And predicted an updated version of lower mass bounds quarks and leptons. It is also reviewed that fourth generation leptons are primarily not possible because of disintegration of Higgs Boson and an interpretation has been setup. And in final, we talked about the Unified Standard Model.

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

In the present work we consider two modified Chevallier-Polarski-Linder (CPL) models in the background of homogeneous and isotropic FLRW space-time, namely (i) the generalized CPL model (Model I) and (ii) the logarithmic form of the the equation of state for the dark energy. From the observational data sets ((Pantheon+)+BAO+HST), we find that at the present epoch (redshift $z=0$), the equation of state for the dark energy converges almost at the same value $\omega_{fld}\approx -0.86$ and the variation of $\omega_{fld}(z)$ with respect to the redshift parameter is very small for both models. we also find that the present value of the Hubble parameter $H_0$ is almost same for both the models. Finally, we compare the models in the light of Akaike, Bayesian Information Criterion (BIC) and Bayesian Evidence. However, we find that Model II is better compared to the Model I from the estimated value of the deceleration parameter.

The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties. The main additions to the classic function computation problem include 1) privacy leakage to an eavesdropper is measured with respect to the remote source rather than the transmitting terminals’ observed sequences; 2) the information leakage to a fusion center with respect to the remote source is considered as a new privacy leakage metric; 3) the function computed is allowed to be a distorted version of the target function, which allows to reduce the storage rate as compared to a reliable function computation scenario in addition to reducing secrecy and privacy leakages; 4) two transmitting node observations are used to compute a function. Inner and outer bounds on the rate regions are derived for lossless and lossy single-function computation with two transmitting nodes, which recover previous results in the literature. For special cases that include invertible and partially-invertible functions, and degraded measurement channels, exact lossless and lossy rate regions are characterized, and one exact region is evaluated for an example scenario.

We use Big Bang Nucleosynthesis (BBN) to probe Beyond Standard Model physics in the neutrino sector. Recently the abundances of primordially produced light elements D and He-4 were determined from observations with better accuracy. The good agreement between the theoretically predicted abundances of primordially produced and derived from observations light elements allows to update the BBN constraints on Beyond Standard Models (BSM) physics. We provide numerical analysis of several BSM models of BBN and obtain precise cosmological constraints and indications for new neutrino physics. Namely, we derive more stringent BBN constraints on electron neutrino-sterile neutrino oscillations corresponding to 1% uncertainty of the observational determination of the primordial He-4. The cosmological constraints are obtained both for the case of zero and non-zero initial population of the sterle neutrino state. Then in a degenerate BBN model with neutrino νe↔νs oscillations we analyze the change of the cosmological constraints in case lepton asymmetry L is big enough to suppress oscillations. We obtain constraints on the lepton asymmetry L . We discuss a possible solution to the dark radiation problem in degenerate BBN models with νe↔νs oscillations in case L is large enough to suppress neutrino oscillations during BBN epoch. Interestingly, the required value of L for solving DR problem is close to L indicated by EMPRESS experiment and close to the value of lepton asymmetry necessary to relax Hubble tension.

In this paper, we investigate an exact Universe which is observational viable and filled with binary mixture of perfect fluid and cosmological constant $\Lambda$. Owning the non-uniform expansion of cosmos, we have considered redshift drift $\dot{z} = -(1+z)H_{0}+H(z)$ and performed statistical test to obtain the best fit value of model parameters of derived Universe with its observed values. Here $H_{0}$ and $z$ denote the present value of Hubble constant and redshift respectively. We estimate the best fit values of Hubble constant and density parameters are $H_{0} = 68.58 \pm 0.84$ km/s/Mpc, $(\Omega_{m})_{0} = 0.26 \pm 0.010$ and $(\Omega_{\Lambda})_{0} = 0.71 \pm 0.025$ by bounding the derived model with latest observational Hubble data (OHD) while with joint Pantheon data and OHD, its values are $H_{0} = 71.93 \pm 0.58$ km/s/Mpc, $(\Omega_{m})_{0} = 0.272 \pm 0.06$ and $(\Omega_{\Lambda})_{0} = 0.74 \pm 0.09$. The analysis of deceleration parameter and jerk parameter show that the Universe in derived model is compatible with $\Lambda$CDM model.

Healthcare crises occur occasionally are very disruptive and resource consuming. The Covid-19 pandemic provided an example of inefficient wasteful resource management that would be unsustainable in the long term. This paper presents a framework for sustainable crisis management that will be applicable for future crises. The methodology is also applicable to other pandemics, wars, multi-casualty natural disasters, industrial disasters, etc. Attention focuses on rapid crisis resolution such as lockdowns. Despite unprecedented investments in healthcare, lack of capacity and timeliness are still problems affecting every country, even the wealthier ones. During the Covid19 pandemic, some healthcare systems around the world have collapsed. For example, Brazil, Spain, Italy, and more. This is due to the lack of a clear methodology for handling such crises. This paper presents a resource-based methodology to cope with healthcare crises considering three scenarios: “Peace”, “War” and “Tsunami”. The paper outlines a methodology for identifying the system bottlenecks for coping with the crisis in each scenario by using established simple tools. These tools assure significant resource frugality that will enhance sustainability. The paper also analyses where the system constraint should be and prescribes how to prepare for such scenarios. A large medical center case study demonstrates our approach. During “peace” times, evolutionary strategies should be used exclusively. During “war” times, a hybrid – evolutionary/disruptive (revolutionary) strategy. During the “Tsunami” scenario, one should rely only on the disruptive (revolutionary) strategy. This methodology can be extended to other scenarios and other lines of research.

$\phi$CDM models provide an alternative to the standard $\Lambda$CDM paradigm, while being physically better motivated. These models lead to a time-dependent speed of sound for dark energy that is difficult to replicate by $w$CDM parametrizations. We review the most up-to-date status of observational evidence for the $\phi$CDM models in this paper. We start with an overview of the motivation behind these classes of models, the basic mathematical formalism, and the different classes of models. We then present a compilation of recent results of applying different observational probes to constraining $\phi$CDM model parameters. Over the last twenty years, the precision of observational data has increased immensely, leading to ever tighter constraints. A combination of the recent measurements favors the spatially flat $\Lambda$CDM model, but a large class of $\phi$CDM models is still not ruled out.

Human-generated, natural occurrences and other actions connected with the petroleum industry have contributed expansively to the contamination of numerous areas of human habitant, thus effecting overwhelming challenges and constraints to sustainable human health, development and the biosphere. The sites commonly associated include derelict pits, hydrocarbon onshore release sites, and places to where oil slicks exuding from offshore releases are blown onshore. In diverse cases, remediation is pertinent to restore the affected ambient. This is required despite the complexity and intricate pore structure and fluid trafficking trajectories of soil which are not easily amenable to remediation. Due to the complex assemblage of contaminated soil, it is necessary to conduct an encompassing site assessment by considering the potential impact of the environment and human health prior to proper selecting and implementing of a desirable remediation process. There are extant remediation methodologies which are effective and efficient for the clean up of contaminated shorelines and other petroleum-contaminated sites involving inter alia agricultural activities. There are salient variations in the techniques to expunge contaminants regarding spatiotemporal and pecuniary costs or considerations due to the gross environmental hazards entailed. This study provides for the opportunity to harness and sustain the capacity for the mitigation of untoward impacts, and induce the latitude for an enabling condition in sustainable human health, environment and development in the petroleum industry and other anthropogenic activities.

This paper is dedicated to examining dynamic dark energy models in the context of observational constraints and their implications for the universe's future. By integrating theoretical models with various observational data, we aim to provide a comprehensive view of how dynamic dark energy influences cosmic evolution and the universe's fate. This study tests these models against the latest observational evidence, including the Cosmic Microwave Background, supernovae, and large-scale structure surveys, to refine our understanding of the universe's accelerating expansion and the mysterious nature of dark energy.

Hox genes represent an important gene family that is involved in the segmentation pattern and identity of the segments during the formation of the body plan in metazoans. For many years, several studies have sought to establish a correlation between the evolution of these genes and the evolution of large groups of metazoans. Here, we use publicly available sequences of Hox gene clusters to reconstruct the evolutionary history of anterior Hox genes. We show that information harbored by these genes, in part, reflects the evolution and diversification of most animal archetypes, but in many cases, there were conflicts between the evolutionary history of some genes and the history of large groups, these cases may have occurred due to specific and similar selective pressures in relatively distant groups, which may have led to evolutionary convergences. Our findings also reveal that the evolution of Hox genes (and clusters) as a multigene family is consistent to a birth-and-death model constrained by development, where there is a trade-off between a relatively fast gene turnover and their central developmental roles

This paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed multi-objective optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF) model for the optimal operation. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant savings in electricity cost with network operational constraints satisfied.

It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, multicellularity and development, the threshold nature of computations in cognitive systems, and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.

Recent inference results of the sound velocity in the cores of neutron stars are summarized. Implications for the equation of state and the phase structure of highly compressed baryonic matter are discussed. In view of the strong constraints imposed by the heaviest known pulsars, the equation of state must be very stiff in order to ensure the stability of these extreme objects. This required stiffness limits the possible appearance of phase transitions in neutron star cores. In view of these empirical findings, much discussed issues such as the quest for a phase transition towards restored chiral symmetry and the active degrees of freedom in cold and dense baryonic matter, are explored.

Export participation and restricted access to external formal credit are two factors attracting meticulous attention from researchers and policymakers, especially in developing countries. Exploring the interactive relationship of these factors in both the static and dynamic models is the purpose of this study. The study uses data sets from small and medium-sized manufacturing enterprises (SMEs) in Vietnam for the period 2009 - 2015. The instrumental variable approach is implemented to deal with the endogenous variable problem in the model. The results show an effect of credit constraint on the firms’ exporting status, and continuous exports are likely to reduce the limit of credit constraint.

The dynamic nature of international business necessitates a strategic approach to achieve sustainable value creation. This paper supports the hypothesis that understanding institutional constraints and leveraging organizational resources provides the foundation for sustainable value creation during international expansion. These elements provide a solid foundation for firms seeking to expand internationally by enabling them to effectively navigate the complexities of foreign markets. Using the context supplied by Peng (2023), the subsequent sections and subsections delve into critical facets of international expansion, including market integration, the role of entrepreneurial firms, entry strategies, supply chain establishment, multinational strategies, and the impact of global trade and foreign direct investment (FDI).

The intensive variables, geochemical, mineralogical and petrogenetic constraints of the Iberian rare metal granites, many of them unknown, are presented. It allows to distinguish two main granitic types with contrasting characteristics: Nb-Ta-rich granites and Nb-Ta-poor granites. The former have lower emplacement temperatures, higher water contents and probably lower emplacement pressures than Nb-Ta-poor granites. Nb-Ta-rich granites also have higher fluoride contents, strong fractionation into twin pairs such as Zr-Hf, Y-Ho and Nb-Ta, and an abundant, probably exsolved, fluid phase, which is not or not as evident in Nb-Ta-poor granites, probably in part because the Nb-Ta-rich granites reach their solidus later (about 550 ºC). The geochemical signature of the Iberian rare metal granites follows the trends of mainly two-mica granites and P-rich cordierite granites, but also of granodiorites and even type I granites, suggesting that the rare metal granites may have different parental magmas. Thermodynamic modeling shows that many of the geochemical characteristics of the RMGs can be explained by a process of extreme differentiation, while others cannot, e.g. Na evolution. In terms of ore deposition, white mica plays a very important role in explaining the variation of the Ta/Nb ratio, although the latter could also be controlled by late Nb-Ta oxides crystallization and even by acidic fluids that remove Nb relative to Ta from earlier Nb-Ta oxides.

Starting from the analysis of the lack of positivity of the Cattaneo heat equation, the article addresses the thermodynamic relevance of the positivity constraint in irreversible thermodynamics, than is at least as important as the entropic constraints. The fulfillment of this condition in hyperbolic models leads to the parametrization of the concentration fields with respect to internal variables associated with the microscopic dynamics. Using Brownian motion theory as a landmarking example for deriving macroscopic transport equations from the equations of motion at the particle/molecular level, we discuss two typical problems involving hydrodynamic interactions at microscale: surface chemical reactions at a solid interface of a diffusing reactant, and mass-balance equations in a complex viscoelastic fluid, in which the physics of the interaction leads either to the overcome the parabolic diffusion model or to consider the parametrization of the concentration with respect to the degrees of freedom associated with the relaxation dynamics of the solvent fluid.

In response to the issue of vehicle escape guidance, this manuscript proposes a unified intelligent control strategy synthesizing optimal guidance and Meta Deep Reinforcement Learning (DRL). Optimal control with minor energy consumption is introduced to meet terminal latitude, longitude and altitude. Maneuvering escape is realized by adding longitudinal and lateral direction maneuver overloads. Maneuver command decision model is calculated based on Soft-Actor-Critic (SAC) networks. Meta learning is introduced to enhance autonomous escape capability, which improves generalization performance to time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this manuscript uses the prediction method to solve reward values, which avoiding a large number of numerical integration. The simulation results manifest that the proposed intelligent strategy can achieve high precise guidance and effective escape.

We investigate the evolution of gravitational waves through discontinuous evolution (transition) of the Hubble expansion rate H(z) at a sudden cosmological singularity which may be due to a transition of the value of the gravitational constant. We find the evolution of the scale factor and the gravitational wave waveform through the singularity by imposing the proper boundary conditions. We also use existing cosmological data and mock data of future gravitational wave experiments (the ET) to impose current and anticipated constraints on the magnitude of such a transition. We show that mock data of the Einstein Telescope can reduce the uncertainties by up to a factor of 3 depending on the cosmological parameter considered.

In the context of reinforcement learning (RL), ensuring both safety and performance is crucial, especially in real-world scenarios where mistakes can lead to severe consequences. This study aims to address this challenge by integrating temporal logic constraints into RL algorithms, thereby providing a formal mechanism for safety verification. We employ a combination of theoretical and empirical methods, including the use of temporal logic for formal verification and extensive simulations to validate our approach. Our results demonstrate that the proposed method not only maintains high levels of safety but also achieves comparable performance to traditional RL algorithms. Importantly, our approach fills a critical gap in existing literature by offering a solution that is both mathematically rigorous and empirically validated. The study concludes that the integration of temporal logic into RL offers a promising avenue for developing algorithms that are both safe and efficient. This work lays the foundation for future research aimed at generalizing this approach to various complex systems and applications.

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, a dispatch optimization method with an iterative grid constraint generation, decoupled from the linear unit commitment problem, is employed. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, PV penetration of ∼5kWp per household can lead to curtailment of ∼60MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors reducing the curtailment up to 60%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safer and optimal operation of a complex energy system is fulfilled. Safer control strategies and more accurate plant sizing could be derived from this work.

The constraint equations in Maxwell theory are investigated. In analogy with some recent results on the constraints of general relativity it is shown, regardless of the signature and dimension of the ambient space, that the "divergence of a vector field" type constraints can always be put into linear first order hyperbolic form for which global existence and uniqueness of solutions to an initial-boundary value problem is guaranteed.

Small Island States (SIDS) are among the nations most exposed to climate change (CC) and are characterised by a high degree of vulnerability. Their special nature means there is a need for more studies focused on the limits to CC adaptation on such fragile nations, particularly in respect of their problems and constraints. This paper addressed a perceived need for research into the limitations of adaptation on SIDS, focusing on the many restrictions which are unique to them. The main research question raised by this study was that how and to what extent the challenges by human activities (e.g., agriculture and tourism) posed to coastlines of SIDS could be addressed. This paper identified and described the adaptation limits they have, by using a review of the literature and an analysis of case studies from a sample of five SIDS in the Caribbean and Pacific regions (Barbados, Trinidad and Tobago, Cook Islands, Fiji, Solomon Islands, and Tonga). The findings of this research showed that an adaptable SIDS is characterised by awareness of various values, appreciation and understanding of a diversity of impacts and vulnerabilities, and acceptance of certain losses through change. The implications of this paper are two-fold. It explains why island nations continue to suffer from the impacts of CC, and suggest some of the means via which adequate policies may support SIDS in their efforts to cope with the threats associated with a changing climate. This study concluded that, despite the technological and ecological limits (hard limits) affecting natural systems, adaptation to CC is not only limited by such complex forces, but also by societal factors (soft limits) that could potentially be overcome by more adequate adaptation strategies.

This work describes a method to carry out 2-D inversion of gravity data in terms of porosity and matrix density distribution using previous DC resistivity inversion results to constraint the fractional pore-water content in the rocks. The inversion is carried out using a controlled random search (CRS) algorithm for global optimization. The method was tested on synthetic data generated from a model representing a graben, and the results show that it can estimate accurate values of contrast-density and porosity. The method was also applied to gravity and dc experimental data collected in NE Portugal, showing results that agree quite well with the known geological information.

What does the way autistics bypass, learn, and eventually master language tell us about human linguistic ability? Here, we argue that non-social acquisition of language, in addition to representing a strong argument for nativist models of human language, may be encompassed within the human-specific orientation and mastery of complex embedded structures, of which language represents one realization. Non-social language learning could thus represent the extension of available linguistic, and non-linguistic material processed by human genetic constraints, allowing language acquisition. This deviation from typical developmental language acquisition may ultimately allow access to language, sometimes in its most elaborate forms, and also explains the possibility of the absence of its development when applied to primarily non-linguistic structured material. However, such enlargement of material-specificity does not cast doubts about its human nature. Regardless of the adaptive success or failure of non-social language learning, it is up to science, legal policies, and ethical principles to strive to maintain autism as a human potentiality to further foster our vision of a plural society.

The Hubble parameter H(z), as a function of redshift, is modified by the presence of a new term originated from the extrinsic curvature in an embedded space-time. Assuming an asymptotic expansion factor $a\sim 0$ or, equivalently $z\rightarrow \infty$, it is obtained a nearly resemblance of the present model ($\beta$-model) with quintessence $w$CDM model with $H(z)\sim H_{wCDM}(z)$ at background level. In conjunction with $\Lambda$CDM, we test the models using a pack of recent datasets like that of the ``Gold 2018'' growth data, the best-fit Planck2018/$\Lambda$CDM parameters on the Cosmic Microwave Background (CMB), the Baryon Acoustic Oscillations (BAO) measurements, the Pantheon Supernovae type Ia and the Hubble parameter data with redshift ranging from $0.01 < z < 2.3$. Performing the Akaike Information Criterion (AIC) to ascertain the statistical viability of the model from Jeffreys' scale, we apply a joint likelihood analysis to the data with the Markov Chain Monte Carlo (MCMC) method. We find that the present model is in very good agreement with observations with a close statistical equivalence with the $\Lambda$CDM and $w$CDM cosmologies at 1-$\sigma$ level. We also show that a mild alleviation of the $\sigma$ tension between the growth amplitude factor and the matter content $(\sigma_8$-$\Omega_m)$ of the observations from CMB and Large Scale Structure (LSS) probes. A comparison of the aforementioned full pack of data in MCMC chains is made with the resulting MCMC from Pantheon SNIa+$H(z)$ in order to analyse the sensitivity of the models and how they respond to a cosmography analysis on the evolution of $H(z)$ and the deceleration parameter $q(z)$. In this sense, we find that only the $\beta$-model can be unaffected to the variations of the previous datasets due to its several minima in the likelihood (degeneracies) of the related parameters with an overall percentage relative difference only up to 4$\%$.

It has been well known that the exponential distribution is the only continuous distribution that has the memoryless property. Here, we observe that, if the exponential distribution dominates an economy as a probability distribution of income acquisition, then the memoryless property imposes equal opportunities on agents in this economy to acquire earnings. Based on this observation, we propose to identify the emergence of an exponential income distribution as a potential necessary condition for guaranteeing the equality of opportunity. Together with other conditions (such as social and economic mobility), it would promote equal opportunities for income acquisition among citizens. Empirically, we employ the latest data available from four representative market-economy countries (the United Kingdom, the United States, Canada, and China) to demonstrate that the exponential distribution is a stylized feature of the income structure of the low- and middle-income class, which occupies the great majority of populations. By contrast, the top income classes in these countries obey the Pareto distribution. To validate the relationship between exponential distribution and equal opportunity, we empirically show how the income structure of the low- and middle-income class in China (from 1978 to 2015) evolved towards an exponential distribution after the market-oriented economic reformation.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

In this paper, we consider a pursuit-evasion game of inertial players, where pursuer’s control is subject to integral constraint, and evader’s control is subject to geometric constraint. In the pursuit problem, the main tool is the strategy of parallel pursuit. Sufficient conditions are obtained for the solvability of pursuit-evasion problems. Also, the main lemma describing the monotonicity of an attainability domain of the evader is proved and an explicit analytical formula for this domain is given. As one of the important results of our paper, the Isaacs Life-line game is solved for a special case.

Indoor scene recognition has great significance for intelligent applications such as mobile robots, location-based services (LBS) and so on. Wherever we are or whatever we do, we are under a specific scene. The human brain can easily discern a scene with a quick glance. However, for a machine to achieve this purpose, on one hand, it often requires plenty of well-annotated data which is time-consuming and labor-intensive. On the other hand, it is hard to learn effective visual representations due to large intra-category variation and inter-categories similarity of indoor scenes. To solve these problems, in this paper, we adopted an unsupervised visual representation learning method which can learn from unlabeled data with a Siamese Convolutional Neural Network (Siamese ConvNet) and graph-based constraints. Specifically, we first mined relationships between unlabeled samples with a graph structure. And then, these relationships can be used as supervision for representation learning with a Siamese network. In this method, firstly, a k-NN graph would be constructed by taking each image as a node in the graph and its k nearest neighbors are linked to form the edges. Then, with this graph, cycle consistency and geodesic distance would be considered as criteria for positive and negative pairs mining respectively. In other words, by detecting cycles in the graph, images with large differences but in the same cycle can be considered as same category (positive pairs). By computing geodesic distance instead of Euclidean distance from one node to another, two nodes with large geodesic distance can be regarded as in different categories (negative pairs). After that, visual representations of indoor scenes can be learned by a Siamese network in an unsupervised manner with the mined pairs as inputs. In order to evaluate the proposed method, we tested it on two scene-centric datasets, MIT67 and Places365. Experiments with different number of categories have been conducted to excavate the potential of proposed method. The results demonstrated that semantic visual representations for indoor scenes can be learned in this unsupervised manner. In addition, with the learned visual representations, indoor scene recognition models trained with the learned representations and a few of labeled samples can achieve competitive performance compared to the state-of-the-art approaches.

Living with a chronic disease involves a variety of daily life limitations that severely affect people in their daily life. Identifying and promoting self-management strategies may improve health outcomes and increase patients’ autonomy. The purpose of the present study was to explore the perceived limitations and self-management strategies of elder patients suffering from chronic diseases. An inductive content analysis was applied, with a purposive sampling of 21 patients living with chronic conditions. Data was collected through semi-structured interviews. Two main-categories were emerged from content analysis, namely “Limitations of daily life” and “Disease Management”. Physical restrictions and negative feelings were referred to as daily burdens, while scheduling and self-care, supporting environment and technology were related to disease management. Information, training and provision of digital literacy may increase patients' self-efficacy in managing their chronic condition.

This paper focuses on robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of robust vector equilibrium and robust vector equilibrium with respect to worst case are introduced, respectively. To generate a subset of robust vector equilibrium, an equivalent min-max optimization problem is constructed. In addition, a direct search algorithm, in which the step size without derivatives and redundant parameters, for solving this min-max problem is proposed. To find a subset of robust vector equilibrium with respect to worst case, an equivalent smoothing optimization problem based on a variant version of ReLU activation function is established. Numerical experiments are also presented to expound the advantages of the algorithms.

In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

The classical uncertainty principle inequalities were imposed as a mathematical constraint over the general relativity geodesic equation. In this way, the uncertainty principle was reformulated in terms of the proper space-time length element, Planck length and a geodesic-derived scalar, leading to a geometric expression for the uncertainty principle (GeUP). This re-formulation confirmed the necessity for a minimum length for the space-time line element in the geodesic, dependent on a geodesic-derived scalar which made the expression Lorentz-covariant. In agreement with quantum gravity theories, GeUP required the imposition of a perturbation over the background Minkowski metric unrelated to classical gravity. When applied to the Schwarzschild metric, a geodesic exclusion zone was found around the singularity where uncertainty in space-time diverged to infinity.

In this paper, we address the problem of stabilization of switched linear systems. The idea is to look for a state feedback control law using evolutionary algorithms (EA) in order to assure the stability of the switched linear systems under control constraints. In some cases when states are not available and only outputs are measurable, the previous method is applied to design an output feedback controller which stabilizes the system. Both stabilizing controllers are developed using deferential evolution and genetic algorithm. Two numerical examples illustrate our proposed theory and point out the effectiveness of our proposed approaches.

The paper is devoted to the investigation of regional observability with constraints on the state of semilinear parabolic systems. The purpose is to reconstruct the initial state between two prescribed functions only on an internal subregion $\omega$ of the system evolution domain $\Omega$. The proofs use two approaches, the subdifferential and HUM approach. Finally, a numerical example is provided to verify the effectiveness of our theory results.

Theories of quantum gravity suggest that Lorentz invariance, the fundamental symmetry of the Theory of Relativity, may be broken at the Planck energy scale. While any deviation from conventional Physics must be minuscule in particular at attainable energies, this hypothesis motivates ever more sensitive tests of Lorentz symmetry. In the photon sector, astrophysical observations, in particular polarization measurements, are a very powerful tool because tiny deviations from Lorentz invariance will accumulate as photons propagate over cosmological distances. The Standard-Model Extension (SME) provides a theoretical framework in the form of an effective field theory that describes low-energy effects due to a more fundamental quantum gravity theory by adding additional terms to the Standard Model Lagrangian. These terms can be ordered by the mass dimension d of the corresponding operator and lead to a wavelength, polarization, and direction dependent phase velocity of light. In this paper, we analyze optical polarization measurements from 63 Active Galactic Nuclei (AGN) and Gamma-ray Bursts (GRBs) in order to search for Lorentz violating signals. We derive the first set limits on each of the 10 individual birefringent coefficients of the minimal SME with d = 4, with 95% confidence limits on the order of 10⁻³⁴ on the dimensionless coefficients.

Many Western studies have indicated that older women are generally more vulnerable in terms of mobility compared to older men, particularly regarding driving. However, the situation may differ in the context of China. This study, based on activity diaries and semi-structured inter-views, focuses on the spatiotemporal behavior of older adults in Tianjin and explores how the constraints posed by activity companions (in terms of type, size, and composition) shape the mo-bilities of older men and women, including activity locations, travel distances, and transporta-tion modes. The key findings are as follows: First, older women are more engaged with their families due to a higher percentage and longer duration of activities spent with family members. Second, older men tend to have more concentrated travel distances near their homes compared to older women. Third, older women exhibit a broader range of activities in different locations and engage in longer-distance leisure travel with family members when compared to older men. In the context of Western literature, this study discusses older women's enhanced social interactions, their earlier retirement in China, and the impact of COVID-19 as factors that help explain these findings. This study contributes to a deeper understanding of accompanied mobilities among Chinese older adults using geographical theory and methods, emphasizing the importance of flexible work schedules for the workforce and the organization of community-based activities to promote the social interactions and mobilities of older adults.

Visual inertial SLAM algorithms enable robots to autonomously explore and navigate in unknown scenes. However, most of the current SLAM systems highly rely on static environment assumptions, which fails in the exsitence of motional objects in the real environment. To improve the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper proposes a visual-inertial SLAM framework that fuses semantic and geometric information, called DA-VINS. First, this paper presents a dynamic object classification method based on feature’s current motion state, which obtains temporary static features in the environment. Secondly, a features dynamics check module based on IMU prior and adjacent frame’s geometry constraint is designed to calculate dynamic factors. It also verifies the classification results of temporary static features. Finally, a dynamic adaptive bundle adjustment module based on the features’ dynamic factors is designed to adjust the weights of features in nonlinear optimization. We evaluated our method in public and our dataset. The results show that D-VINS is one of the most real-time, accurate, and robust systems in dynamic scenes.

A fundamental question in biology is whether phenotypes can be predicted by ecological or genomic rules. At least five cases of convergent evolution of the crab-like body plan (with a wide and flattened shape, and a bent abdomen) are known in decapod crustaceans, and have, for over 140 years, been known as ‘carcinization’. The repeated loss of this body plan has been identified as ‘decarcinization’. In reviewing the field, we offer phylogenetic strategies to include poorly known groups, and direct evidence from fossils, that will resolve the history of crab evolution and the degree of phenotypic variation within crabs. Proposed ecological advantages of the crab body are summarized into a hypothesis of phenotypic integration suggesting correlated evolution of the carapace shape and abdomen. Our premise provides fertile ground for future studies of the genomic and developmental basis, and the predictability, of the crab-like body form.

Using a joint statistical analysis, we test a four-dimensional FLWR model embedded in a five-dimensional bulk based on the Nash-Greene embedding theorem. Performing a Markov Chain Monte Carlo (MCMC) modelling, we combine observational data sets as those of the recent ``Gold 2018'' growth data, the best-fit Planck2018/$\Lambda$CDM parameters on the Cosmic Microwave Background (CMB), the Baryon Acoustic Oscillations (BAO) measurements, the Pantheon Supernovae type Ia and the Hubble parameter data with redshift ranging from $0.01 < z < 2.3$ to impose restrictions on the model. From linear Nash-Green fluctuations of the metric, we obtain the related perturbed equations in longitudinal Newtonian gauge to obtain the evolution of growth matter. We show that a mild alleviation is obtained from the $\sigma$ tension between the growth amplitude factor and the matter content $(\sigma_8$-$\Omega_m)$ of the observations from CMB and Large Scale Structure (LSS) probes with degeneracies on the parameters. We also apply the Om(z) diagnosis to distinguish the model from $\Lambda$CDM. The Akaike Information Criterion (AIC) is used and we find a relative statistical consistence of the present model with both $\Lambda$CDM and $w$CDM models up to 1$\%$ of percentage difference at early times.

The acceleration of the universe is described as a consequence of the extrinsic curvature of the space-time embedded in a bulk space, defined by the Einstein-Hilbert. Using the linear approximation of Nash-Green theorem, we obtain the related perturbed equations in which just the gravitational-tensor field equations contribute to propagation of cosmological perturbations. In accordance with Big Bang Nucleosynthesis and solar constraints, we calculate numerically the effective Newtonian function Geff to constrain the related parameters of the model. We numerically solve the growth density equation for two possible family of solutions leading to an interesting overdensity and, in some cases, a mild damping of the growth profiles, with a top amplification of the growth perturbations around 14% in comparison with LCDM model and quintessence. The effective gravitational Phi and Newtonian curvature Psi are also analysed showing mild perturbations in early times induced only by the extrinsic curvature differently from the LambdaCDM standards.

Lunar grabens are the largest tensional linear structures on the Moon. In this paper, 17 grabens were selected to investigate the dips and displacement-length ratios (γ) of graben-bounding faults. Several topographic profiles were generated from selected grabens to measure their rim elevation, width and depth through SLDEM2015 (+LOLA) data. The differences in rim elevation (∆h) and width (∆W) between two topographic profiles on each graben were calculated, yielding 146 sets of data. We plotted ∆h versus ∆W for each and calculated the dip angle (α) of graben-bounding faults. A dip of 39.9° was obtained using the standard linear regression method. In order to improve accuracy, large error data was removed based on error analysis. The results 49.4° and 52.5° were derived by the standard linear regression and mean methods, respectively. Based on the depth and length of grabens, the γ value of the graben-bounding normal fault is also studied in this paper. The γ value is 3.6×10-3 for lunar normal faults according to the study of grabens and the Rupes Recta normal fault. After obtaining the values of α and γ, the increase in lunar radius caused by the formation of grabens was estimated. We suggest that the lunar radius has increased by around 130 m owing to the formation of grabens. This study could aid in the understanding of normal fault growth and provide important constraints on the thermal evolution of the Moon.

Soil sodicity is a major constraint to seedling emergence and crop production, potentially reducing plant growth due to physical and chemical constraints. Examining responses to ion imbalances may assist in the identification of genotypes tolerant to chemical constraints in sodic soils and improve productivity. We evaluated the performance of four wheat (Triticum aestivum. L) genotypes in solutions with five sodium adsorption ratios (SARs) ranging from 0 to 60. For all four genotypes, seedling emergence and shoot dry matter (DM) decreased significantly with increasing SAR. A significant positive correlation was observed between Ca concentration in roots, as well as both root and shoot DM for all genotypes. At SAR values &gt;20, a more tolerant genotype (EGA Gregory), had higher Ca concentrations in root tissues, while a more sensitive genotype (Baxter) exhibited Na-induced Ca deficiency. Thus, selection for genotypes that are able to accumulate Ca in roots in sodic conditions may be a useful trait for selecting genotypes tolerant of soils with high ESP values. However, for soils that restrict plant growth at ESP (SAR) values of 6-10%, it is likely that growth is restricted by physical constraints rather than by a Na-induced Ca deficiency.

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for ecological models and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative ecological response models. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate-ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

In the context of the minimally extended varying speed of light (meVSL) model, both the absolute magnitude and the luminosity distance of type Ia supernovae (SNe Ia) differ from those predicted by general relativity (GR). Utilizing the Pantheon+ data, we examine the plausibility of various dark energy models under meVSL. Both ωCDM and CPL parameterization dark energy (DE) models exhibit indications of cosmic variations in the speed of light at the 1-σ level. For Ωm0=0.30,0.31, and 0.32 with (ω0,ωa)=(−1,0), the 1-σ range of c˜˙0/c˜0(10−13yr−1) is (-8.76 , -0.89), (-11.8 , 3.93), and (-14.8 , -6.98), respectively. Meanwhile, the 1-σ range of c˜˙0/c˜0(10−12yr−1) for CPL dark energy models with −1.05≤ω0≤−0.95 and 0.28≤Ωm0≤0.32, is (-6.31 , -2.98). The value of c˜ at z=3 can exceed that of the present by 0.2∼3 % for ωCDM models and 5∼13 % for CPL models. Additionally, for viable models except for the CPL model with Ωm0=0.28, we find −25.6≤G˜˙0/G˜0(10−12yr−1)≤−0.36. For this particular model, we obtain an increasing rate of the gravitational constant within the range 1.65≤G˜˙0/G˜0(10−12yr−1)≤3.79.

The present analysis deals with the study of the $f(Q, T )$ theory of gravity which was recently considered by many cosmologists. In this theory of gravity, the action is taken as an arbitrary function $f(Q, T )$ where $Q$ is non-metricity and $T$ is the trace of energy-momentum tensor for matter fluid. In this study, we have taken three different forms of the function $f(Q, T )$ as $f(Q,T)=a_1Q+a_2 T$ and $f(Q,T)=a_3 Q^2+a_4 T$ and discussed some physical properties of the same. Also, we have obtained the various cosmological parameters towards Friedmann-Lemaitre-Robertson-walker (FLRW) Universe by defining the transit form of scale factor which yields the Hubble parameter in redshift form as $H(z)=\frac{H_{0}}{(\lambda+1)} \left(\lambda+ (1+z)^{\delta}\right)$. %We have obtained the approx best-fit values of model parameters using the least square method for observational constraints on available datasets like Hubble dataset $H(z)$, Supernova dataset SNe-Ia, etc., by applying the Root Mean Squared Error formula (RMSE). By applying the Root Mean Squared Error formula (RMSE), we were able to determine the approximate best-fit values of model parameters using the least square approach for observational constraints on the datasets Hubble dataset $H(z)$, Supernova dataset SNe-Ia, etc. We have observed that the deceleration parameter $q(z)$ exhibits a signature-flipping (transition) point within the range $0.623 \le z_{0} \le 1.668$ through which it changes its phase from the decelerated to the accelerated expanding universe with $\omega = -1$ at $z=-1$ for the approximate best-fit values of the model parameters. %For obtained approx best-fit values of model parameters we have observed that the deceleration parameter $q(z)$ shows a signature-flipping (transition) point within the range $0.623 \le z_{0} \le 1.668$ through which it changes its phase from decelerated to the accelerated expanding universe with $\omega = -1$ at $z=-1$.

South Africa has an abundant heritage of valuable indigenous plants with recognized medicinal value. The reported critical over-harvesting of wild populations is considered an urgent issue for biodiversity conservation. Growing demand for therapeutic products from indigenous medicinal plants have led to increased interest in its cultivation. Cultivation of these indigenous medicinal plants represent a viable option for improving smallholder farmers’ livelihoods, as well as sustaining the availability of these resources for future generations. Serious bottlenecks however exists for subsistent farmers in the cultivation of these valuable plants. It was pertinent to probe whether the cultivation of medicinal plants provide feasible solutions to rural poverty, while effectively conserving threatened indigenous biodiversity. The paper employed a comprehensive review of existing literature to explore relevant issues constraining smallholder farmers from involvement in a potentially lucrative medicinal plants value-chain. Findings indicate challenges such as inadequate domestication of valuable plants species, continued over-harvesting from wild populations, poor knowledge of required agronomic practices, low efficacy perception regarding derivatives from cultivated plants, among a multitude of others. These constraints exists alongside the conservation-oriented strategy driven by international conservation agencies and wholly adopted by the South African government. Recommendations to improve smallholder involvement in the cultivation of medicinal plants include support to research and extension, targeted inducement to smallholders, contracting and off-take agreements, aimed at promoting an alternative poverty-alleviation-focused economic development strategy. The review adds to the conceptual discourse related to plant diversity, resource conservation, poverty alleviation and economic development within the medicinal plants value-chain.

We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the PSO algorithm for function optimization. Four parameters drive LCPSO: the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF, we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range, and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

Macadamia (Macadamia integrifolia) nuts have become an essential commodity crop in Malawi. The nuts are a lucrative commodity and are used for household consumption and income generation among farming families and as a foreign exchange earning crop at country-level. In addition, macadamia production has increased significantly in recent years in the country. Currently, Malawi is the seventh top producer of macadamia nuts, with a global market share of three percent (1,846 metric tonnes on kernel basis). In 2018, exports of macadamia kernel had a value of US$24.3 million (£19.8 million or MKW 18.01 billion). However, the bulk (85%) of the crop is grown on large commercial plantations, but the smallholder sector is emerging as vital for the future growth of the macadamia sub-sector in Malawi. Furthermore, Malawian smallholders consider macadamia production to be a low-input crop with large returns per unit area (US$14.37 kg^-1ha^-1 or MKW 10,701kg^-1ha^-1) thus a lucrative crop with high potential for poverty reduction and wealth creation among these farming families. This paper, therefore, explores: (i) the historical and current trends in macadamia nut production in Malawi; (ii) analyses the country’s macadamia value chain and (iii) discusses the constraints of macadamia production in Malawi for informed policymaking. Thus, the synthesis of the Malawian macadamia sub-sector provides an understanding of the vital contributions of macadamias to Malawi’s economic growth and improvement of livelihoods.

Turkiye, located in the Mediterranean Basin, has started to experience the impacts of climate change, primarily manifesting in severe droughts that significantly affect agricultural output. Water stands as a crucial component in agricultural production. This study aims to propose water efficiency measures essential for climate change adaptation. By accessing pertinent data, partic-ularly in agriculturally reliant developing nations, this research endeavours to analyse yield losses in major crops within agricultural regions using the most suitable methodologies. Furthermore, it seeks to ascertain the financial ramifications of such losses. The methodological framework de-vised for this purpose was implemented in Türkiye's Iğdır Plain, selected as the study site. In 2022, the Iğdır province received 40% less precipitation than its usual levels. This study anticipates substantial losses in agricultural yield due to water scarcity resulting from prolonged drought conditions. Analysis reveals that among the crops cultivated in the region, clover belonging to the field crops category exhibits the highest water dependency, while apricot demonstrates the least reliance on water resources. In light of these considerations, the recommended crop rotation for the Iğdır Plain under water constraints comprises wheat, watermelon, maize, melon, apple, and apricot. Emphasizing enhanced water transmission and utilization efficiency, particularly in ir-rigation zones, prioritizing water stress-resistant crops within basin crop patterns, and favouring alternative crops over those demanding excessive water will foster sustainable water resource management. This approach encourages farmers to actively engage in the adaptation process to climate change and drought conditions while minimizing the agricultural sector's vulnerability to the impacts of climate change, which heavily relies on water consumption

Driving simulators are widely used for understanding human-machine interaction, driver behavior and in driver training. The effectiveness of simulators in these process depends largely on their ability to generate realistic motion cues. Though the conventional filter-based motion cueing strategies have provided reasonable results, these methods suffer from poor workspace management. To address this issue, linear MPC-based strategies have been applied in the past. However, since the kinematics of the motion platform itself is non-linear and the required motion varies with the driving conditions, this approach tends to produce sub-optimal results. This paper presents a nonlinear MPC-based algorithm which incorporates the nonlinear kinematics of the Stewart platform within the MPC algorithm in order to increase the cueing fidelity and utilize maximum workspace. Further, adaptive weights-based tuning is used to smoothen the movement of the platform towards its physical limits. Full-track simulations were carried out and performance indicators were defined to objectively compare the response of the proposed algorithm with classical washout filter and linear MPC-based algorithms. The results indicate a better reference tracking with lower root mean square error and higher shape correlation for the proposed algorithm. Lastly, the effect of the adaptive weights-based tuning was also observed in the form of smoother actuator movements and better workspace utilization.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

During the extraction of palm oil by smallholders in Cameroon, the use of enormous quantities of water results in palm oil mill effluent (POME), which contains substances that are deleterious to the environment at concentrations above the threshold values. A detailed description of the various processes involved is imperative so as to develop methods of reducing loss and minimising the environmental effect caused by the wastes produced. In this study, we characterise the small-holders’ palm oil production sector in Cameroon, along its entire production chain. The main demographics of smallholder farmers are adult males (64.4%) and married (46.7%) with low levels of formal education (51.1% attained only primary education). Plantation establishment involves deforestation of pristine vegetation (46.7%) as well as replacing other farming systems. Processing is carried out by the farmers with their own mills (48.9%) bought at exorbitant prices. Access to finances (51.1%) remains a key limitation to plantation expansion and the adoption of innovations in this sector. Workers’ health issues abound (75.6%) and are treated mainly using ethnomedicine (31.1%), and there is little or no social security; thus, sick workers generally pay their own bills (64.4%). Issues of environmental pollution from production to waste processing abound with solid waste mainly burnt (57.8%) and POME directed into open pits and streams (37.8%) where they become a nuisance and serve as breeding grounds for mosquitoes (51.1%); these issues will require greater state involvement for mitigation. Our findings suggest that farmers in the palm oil sector have deep knowledge base and competence, but government intervention are needed to stimulate further growth in this important sector.

Green Infrastructure promotes the use of natural functions and processes as potential solutions to reduce negative effects derived from anthropocentric interventions such as urbanization. In cities of Latin America, for example, the need for more nature-sound infrastructure is evident due to its degree of urbanization and degradation of ecosystems, as well as the alteration of the local water cycle. In this study, an experimental approach for implementation of a prototype is presented. The experiment took place in a highly urbanized watershed located in the Metropolitan Area of Costa Rica. Initially, understanding the characteristics of the study area at different scales was achieved by applying the Urban Water System Transition Framework to identify the existing level of development of the urban water infrastructure, and potential future stages. Subsequently, preferences related to spatial locations and technologies were identified from different local decision-makers. Those insights were adopted to identify a potential area for implementation of the prototype. The experiment consisted on an adaptation of the local sewer to act as a temporal reservoir to reduce the effects derived from rapid generation of stormwater runoff. Unexpected events, not considered initially in the design, are reported in this study as a means to identify necessary adaptations of the methodology. Our study shows from an experimental learning-experience that the relation between different actors advocating for such technologies influences the implementation and operation of non-conventional technologies. Furthermore, the perception of security associated to green spaces was found as a key driver to increase the willingness of residents to modify their urban environments. In consequence, those aspects should be carefully considered as factors of designs of engineering elements when they are related to complex socio-ecological urban systems.

The Sunyaev-Zeldovich effect (SZe) is a global distortion of Cosmic Microwave Bckground (CMB) spectrum as result of its interaction with a hot electron plasma in the intracluster medium for large gravitational virialized structures such as galaxy clusters. Furthermore, this hot gas of electrons emits X-Rays due to its fall in the gravitational potential well of the cluster. The analysis of SZe and X-Ray data, provide a method for calculating distances to galaxy clusters at any redshift (Angular diameter distance (d_A) and gas mass fraction (f_gas)). On the other side, many of these galaxy clusters produce a Strong Gravitational Lens effect (SGL), which has become an useful astrophysical tool for cosmology. We use these cosmological tests, in addition to the more traditional ones (SNIa, CMB, BAO), to constraint alternative models of dark energy (ωCDM, CPL, IDE, EDE) and study the history of expansion through the cosmographic parameters (H(z), q(z), j(z)). Using Akaike and Bayesian Information Criterion (AIC, BIC) we find that the ωCDM and ΛCDM models are the most favored by the observational data. In addition, we found that at low redshift appears an peculiar behavior of slowdown of aceleration, which occurs only on dynamical dark energy models using only galaxy clusters (d_A,clusters + f_gas).

Pearl millet (Pennisetum glaucum L.) is the second most important cereal grown in Eritrea after sorghum (Sorghum bicolor L.) grown under low input systems by small scale farmers. The crop utilizes soil moisture efficiently and has ability to tolerate soil toxicity and extreme temperatures than other cereals. It is a sustenance and food security crop important for its nutritive and cultural value and provides dietary energy and nutrition. However, despite the positive attributes and qualities of pearl millets for the present and future agriculture, production has been low. We attribute this to inadequate rainfall distribution, poor crop management by poorly resourced farmers, unavailability and high prices of farm inputs such as fertilizer and pesticides and low adoption of improved varieties by the farmers. This review outlines the constraints, interventions the government/ farmers have and can implement and the prospects of actions that can improve pearl millet production. As much as there have been efforts by the government and stakeholders to address these challenges and improve productivity of pearl millet, more needs to be done to meet the increasing demand of the increasing populations. This will enable farmers to intensify and diversify their agricultural systems and improve food security situation in the country. Unless a combined effort in soil fertility improvement, policies to promote use of modern varieties and conservation of and promotion of this crop biodiversity, the potential of this crop as famine and poverty alleviation among the rural poor will not be realized.

Through the use of Smart Sensor Networks (SSN), Marine Observatories (MO) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of SSN devices’ hardware and software when combined with the Internet. Hence, SSN are regarded as complex distributed systems. As such, SSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed SSN. To this end, we describe in this article how we extended our previously proposed ArchiMO meta-model and design tool by extending an Enterprise Architecture Framework based on the ArchiMate Modeling Language. This extension proposes the incorporation of new Underwater Environmental Constraints (UEC) for SSN in ArchiMO which is coupled with the fusion of sensors with Artificial Intelligence. This serves as the basis for generating a new version of our ArchiMO design tool, which incorporates the new added constraints and invokes the AI created Database. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to handle and control the uncertainties and variances of the provided quality of service that may occur during the performance of the SSN, hence reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of SSN.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

This paper examines the influence of informal competition on SMEs innovation in the Eastern European transition economies. It investigates whether credit constraints mediate this relation. SMEs innovation is presented through four measures: Product innovation, Process innovation, Radical innovation and Green innovation. Using the BEEPs VI that covers the period from 2018-2020, we show that informal competition affects positively the product, process and radical innovation. Yet, it has a non-significant effect on green innovation. Besides, the informal sector increases SMEs credit constraints, which indirectly leads to less corporate innovation. The negative indirect effect restrains the positive direct effect. Hence, a partial mediation effect of credit constraints on the informal competition and the innovation proxies is reported with the exception of green innovation.

The study investigated farmers’ perception on pests and diseases management strategies, agricultural practices, production constraints and selection criteria of elite varieties using a survey research design. A total of 700 questionnaires were administered to smallholder farmers who were farming for household consumption, those producing for sale and household consumption and those who were mainly producing for sale because their primary goal was to produce for the market. Findings revealed that the most prevalent pests identified were whiteflies (Bemisia tabaci), grasshoppers (Zonocerus variegatus), aphids (Aphis gossypii), mealybugs (Phenacoccus manihoti), termites (Isoptera), and grasscutters (Thryonomys swinderianus). The three most common diseases of cassava limiting growth and yield with mixed infection include viral, bacterial, and fungal. The study also revealed that only 12.5% of farmers cultivated improved cassava varieties. The management practices implemented encompassed field sanitation and the application of pesticides. Lack of capital and no availability of market were the most important production and marketing constraints faced by farmers in the study area, whereas poundability and high storage root yield are the most preferred traits farmers consider in selecting a new variety. Results suggest that smallholder farmers should endeavor to undergo various developmental programs to develop themselves to increase their competitive advantage in their locality and in the local and global agricultural sectors. In addition, governments and other policy makers should strategize plans and programs that would contribute to smallholder farmers’ agriculture, community and national development.

Real estate industry is related to the national economy and people's livelihood，characterized by a high degree of financial intensity. The enterprises in this industry need certain financial ability and large shareholder controlling ability to support their survival. However，due to the multiple adverse impacts of current state policies，banks and private capital，the credit crunch，the sudden decrease in withdrawn funds and the limitation of internal financing，the problem of capital restraint of real estate enterprises has become more and more serious. From the perspective of corporate governance，this paper studies the interaction among financial constraints，ownership concentration and corporate performance under different shareholding states by analyzing the quantitative characteristics of equity structure，and looks for the appropriate range of the largest shareholder holding ratio，which has considered the financial performance and risk. It is found that raising the ownership concentration can effectively ease the financing constraints and improve the performance of enterprises，both of which are significant under the state of high ownership concentration， while the financial constraints play a significant intermediary effect under the State of absolute holding， while in the decentralized state of ownership，there is a significant regulatory effect，and the interaction of the three will be different due to the size of the enterprise.

Food security relies mainly on a few major crop such as wheat, maize, rice and yam. Many of the cultivated plant such as Cyperus exculentus are still considered invasive plants and are neglected and underutilized. In the perspective to valorization of the species, this systematic review aimed at identifying the biology, production constraints and uses of tigernut for future research directions. Extensive searches were carried out and studies were screened and extracted using established systematic review methods. A total of 175 papers met the inclusion criteria. Approximately 52% and 21.71% of the studies were undertaken in Europe and Africa respectively. Most of the papers reviewed for the study were published between [2010-2015[. The review highlighted the critical research gaps in genetic diversity using SSR makers and evolutionary biology. Further, production constraints and solution approaches for the promotion of the species were the other gaps identified in the reviewed studies. Production constraints were specifically related to the insufficient mineral fertilizers and difficult in harvesting. Tigernut is used in more fields such as food, medicinal, cosmetic, biofuel and fishing and fish breeding. Such investigations would help in decision-making and elaboration of breeding strategies, and advancing steps towards sustainable use of the species.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

The quickest path problem in the multistate flow networks, also known as the quickest path reliability problem (QPRP), aims at calculating the probability of transmitting a minimum of d units of flow/dat/commodity from a source node to a destination node through one single path within T units of time. Several exact and approximation algorithms have been proposed in the literature to address this problem. Most of the exact algorithms in the literature need prior knowledge of all the network’s minimal paths (MPs), which is considered a weak point. In addition to the time, the budget is always limited in real-world systems, making it an essential consideration in the analysis of systems’ performance. Hence, this study considers the QPRP under the cost constraints and provides an efficient approach based on the node-child matrix to address the problem without knowing the MPs. We show the correctness of the algorithm, compute its complexity results, illustrate it through a benchmark example, and conduct extensive experimental results on the known benchmarks and one thousand randomly generated test problems to demonstrate its practical superiority compared to the existing algorithms in the literature.

This work presents a multi-start algorithm for solving the capacitated vehicle routing problem with two-dimensional loading constraints (2L-CVRP) allowing for the rotation of goods. Researches dedicated to graph theory and symmetry considered the vehicle routing problem as a classical application. This problem has complex aspects that stimulate the use of advanced algorithms and symmetry in graphs. The use of graph modeling of the 2L-CVRP problem by undirected graph allowed the high performance of the algorithm. The developed algorithm is based on metaheuristics such as the Constructive Genetic Algorithm (CGA), to construct promising initial solutions; a Tabu Search (TS), to improve the initial solutions on the routing problem; and a Large Neighborhood Search (LNS), for the loading subproblem. Although each one of these algorithms allowed to solve parts of the 2L-CVRP, the combination of these three algorithms to solve this problem was unprecedented in the scientific literature. In our approach, a parallel mechanism for checking the loading feasibility of routes was implemented using multi-threading programming to improve the performance. Additionally, memory structures, like hash-tables, were implemented to save time by storing and querying previously evaluated results for the loading feasibility of routes. For benchmarks, tests were done on well-known instances available in the literature. The results proved that the framework matched or outperformed most of the previous approaches. As the main contribution, this work brings higher quality solutions for large-size instances of the pure CVRP. This paper involves themes related to the symmetry journal, mainly complex algorithms, graphs, search strategies, complexity, graph modeling, and genetic algorithms. In addition, the paper especially focuses on topic-related aspects of special interest of the community involved in symmetry studies, such as, graph algorithms and graph theory.

The Koka gold deposit is located in the Elababu shear zone between the Nakfa terrane and the Adobha Abiy terrane, NW Eritrea. Based on the paragenetic study two main stages of gold mineralization were identified in the Koka gold deposit: 1) an early stage of pyrite-chalcopyrite-sphalerite-galena-gold-quartz vein; and 2) a second stage of pyrite-quartz veins. NaCl-aqueous inclusions, CO2-rich inclusions, and three-phase CO₂-H₂O inclusions occur in the quartz veins at Koka. The ore-bearing quartz veins formed at 268℃, from NaCl-CO₂-H₂O(-CH₄) fluids averaging 5 wt% NaCl eq. The ore-forming mechanisms include fluid immiscibility during stage I, and mixing with meteoric water during stage II. Oxygen, hydrogen and carbon isotopes suggest that the ore-forming fluids originated as mixtures of metamorphic water, meteoric water and magmatic water, whereas sulfur isotope suggest an igneous origin. Features of geology and ore-forming fluid at Koka deposit are similar to those of orogenic gold deposits, suggesting the Koka deposit might be an orogenic gold deposit related to granite.

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing the continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, the operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to the fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop of voltage and frequency upon disconnecting from the main grid. To appropriately model the above, the nonlinear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently-developed "l1-proximal" Surrogate Lagrangian Relaxation are extended. Testing results based on 9-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations thereby avoiding approximations inherent to existing methods, while demonstrating that through the optimization, 1. the number of tap changes is drastically reduced, and 2. the method is capable of handling networks with meshed topologies.

The design and operation of quantum networks are both decisive in the current push towards a global quantum internet. Although space-enabled quantum connectivity has already been identified as a beneficial candidate for long-range quantum channels for over two decades, the architecture of a hybrid space-ground network is still a work in progress. Here, we propose an analysis of such a network based on a best-path approach, where either fiber- or satellite-based elementary links can be concatenated to form a repeater chain. The network consisting of quantum information processing nodes, equipped with both ground and space connections, is mapped into a graph structure, where edge weights represent the achievable secret key rates, chosen as the figure of merit for the network analysis. A weight minimization algorithm allows identifying the best path dynamically, i.e., as the weather conditions, stray light radiance, and satellite orbital position changes. From the results we conclude that satellite links will play a significant role on the future large-scale quantum internet, and both a constellation of satellites and significant advances in filtering technology are required to fulfill continuous coverage.

Recent studies of the difficulties faced by smallholder farmers in many developing countries have echoed their disconnection with formal markets. These limitations have been attributed to a number of factors including stringent quality and volume requirements, among others. While smallholder farmers seek access to formal markets, the existing alternatives through which they sell their produce remain obscure. Using an interview of market outlets and selected smallholder farmers in the area, the study applied a crop marketing index to examine the outlets currently used by farmers and the volume of potatoes sold in each. Findings indicate that smallholder farmers on average sold sixty-eight percent of their produce. The outlet mostly used by farmers was street vendors because the large supermarkets sold potatoes supplied from external sources. It would be helpful for smallholder farmers to aggregate their produce through producer and marketing cooperatives, to better engage with these formal market outlets.

Based on recent progress in cosmological thermodynamics, we present a new thermody- namic formulation of the Friedmann equations in the new Haug-Tatum cosmology model, as well as for other similar models put forward in the literature from different solutions to Einstein’s field equation. Since the CMB temperature has been measured much more accu- rately than the Hubble constant, we demonstrate that our thermodynamic formulation of the Friedmann equation dramatically narrows the acceptable range for the critical density of the universe. Furthermore, we show how our thermodynamic formulation leads to a more precise cosmological constant estimate.

Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between active MDs and idle MDs in the D2D-MEC (Mobile Edge Computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D-MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently addressed in existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and coupling of actions across time slots, we model the problem as a Markov Decision Process (MDP) and perform the multi-agent DRL through Multi-Agent Proximal Policy Optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison with the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%.

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 seconds, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods.

The early Paleozoic is a crucial period in the formation and evolution of the Eastern Kunlun Orogenic Belt (EKOB), and is of great significance for understanding the evolutionary history of the Proto-Tethyan Ocean. This paper presents new petrography, geochemistry, zircon U–Pb dating, and Lu–Hf isotopic research on the Yuejingshan gabbro from the eastern segment of the EKOB. Zircon U–Pb data suggests that the gabbro formed in the Early Silurian (435 ± 2 Ma). All samples have relatively low TiO₂ contents (0.45–2.97%), widely varying MgO (6.58–8.41%) and Mg^# (58–65) contents, and are rich in large ion lithophile elements (LILE such as Rb, Ba, Th, and U) and light rare earth elements (LREE). This indicates that it has a similar geochemical composition to island arc basalt. The major element features indicate that the formation of this gabbro underwent fractional crystallization of clinopyroxene, olivine, and plagioclase. The depletion of high field strength elements (HFSE, such as Nb, Ta, and Ti), and a slightly positive Hf isotope (with εHf_(t) ranging from 1.13 to 2.45) may be related to the partial melting of spinel-bearing peridotite, led by slab fluid metasomatism. The gabbro likely represents magmatic records of the latest period of the early Paleozoic oceanic crust subduction in the Eastern Kunlun. Therefore, the final closure of the Proto-Tethyan Ocean and the beginning of collisional orogeny occurred before the Early Silurian.

The aim of this research is to determine the factors that may prevent high school students from participating in recreational activities and to investigate whether these factors differ within the scope of various variables. This study consisted of total 1459 (681 women and 778 men) student volunteers who educated in high school level. Sampling method was preferred for easy sampling. The face-to-face survey method was used to collect the data. The "Leisure Constraints Scale" developed by Alexandris and Carroll (1997) and adapted to Turkish by Gürbüz, Öncü, and Emir (2012) was used to determine the factors that might prevent individuals from participating in leisure activities. The data obtained for the research were first transferred to the computer and then analyzed by SPSS packet program. The error margin level in the study was taken as p<0.05. The cronbach alpha of the study was found to be 0.91. As a result, it was found that women participated in the leisure time more than men. It was also observed that the participants met with more leisure constrain in Turkey's eastern regions.

The Sanhetun tellurium-gold (Te-Au) deposit, located in the Duobaoshan polymetallic metallogenic belt (DPMB) within the eastern section of the Central Asian Orogenic Belt (CAOB), is a newly discovered small scale gold deposit. The mineralization, with resource of ≥ 4t Au, is mainly hosted in 3 NNE-trending alteration zones between granitic mylonite and volcanogenic-sedimentary formations. It’s the genesis and timing of formation of this deposit are poorly constrained. Here, we report the results of petrographic studies, TESCAN Integrated Mineral Analyzer (TIMA), major and trace element concentrations, and in situ S isotope of pyrite and U-Pb ages of calcite. The results show that there are five species of telluride, including native tellurium, tetradymite, tsumoite, hessite and petzite and four types of pyrite, including coarse-grained euhedral Py1, fine-grained quartz-Py2 vein crosscutting Py1 and anhedral aggregated Py3 and Py4. The pre-ore Py1, containing negligible Au, Te and other trace elements, have a relatively narrow range of δ34S values ranging from -1.20 to -0.57‰. The Py2 have slightly high concentrations of Au and Te and distinctly high concentrations of Mo, Sb, Zn and Mn with markedly positive δ34S values of 4.67 to 14.43‰. The main ore stage Py3 contains high Au and Te concentrations and shows narrow δ34S values ranging from -5.69 to 0.19‰. The post-ore stage Py4 contains low Au concentrations with the δ34S values ranging from 2.66 to 3.86‰. Calcite from ore stage yield a U-Pb age of 135 ± 22 Ma, interpreted as the age of Te-Au mineralization. Our geochemical and geochronological data indicate that the Te was derived from partial melting of mantle metasomatized by the Te-rich fluids that originated from oceanic sediments. We correlate the tectonic setting with the subduction of Paleo Pacific plate in Early Cretaceous.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

The latest EU regulation on geographical indications (EU Regulation No. 1151/2012) has intro-duced a set of new tools for the protection and enhancement of food products in rural areas, under the group name of optional quality term (OQT). The Commission Delegated EU Regulation, No. 665/2014, regulated the conditions for the use of the optional quality term «mountain product» (MP), to support the implementation of a mountain value chain. This new tool is aimed at pro-moting local development, maintaining the economic activities in mountain areas and redistrib-uting wealth, whilst, at the same time, promoting the territory. Pecorino and goat cheeses are typ-ical Italian cheeses made usually with whole raw ewe's or raw goat's milk, without starter cul-ture addition. In an attempt to characterize these productions, the aim of this study was to inves-tigate the evolution of enterococci during the production and ripening of Pecorino cheese made in three different farms, located in Umbria, Italy in areas facing natural or other specific constraints as stipulated by Regulation 1305/2013 on support for rural development by the European Agri-cultural Fund for Rural Development (EAFRD). Enterococci are enteric organisms which are commonly isolated from ewe and goat's milk production in Umbria, Italy. Counts of enterococci in raw milk ranged from 1.75 for ovine milk to 3.62 for ewe milk and a marked reduction was observed after thermization especially in ovine milk. Out of 100 isolates, 69 were E. faecium, 23 E. durans, 8 E. faecalis and 2 E. casseliflavus and the distribution of species between farms and be-tween samples showed a prevalence of E. faecium in ovine farms and E. durans in ewes farms, with an equal distribution between samples. High percentages of susceptible isolates were found for amoxicillin/clavulanic acid, ampicillin, chloramphenicol, sulphamethoxazole, sulphameth-oxazole/trimethoprim, ticarcillin, vancomycin. A high prevalence of resistant strains (> 30%) was observed for amikacin, ciprofloxacin, ceftriaxone, kanamycin, tetracycline. A comparison of this results with those of previous works on similar dairy products revealed high levels of resistance to antimicrobials which needs to be addressed.

The present work reports new mineralogical and whole rock geochemical data from the Breivikbotn silicocarbonatite (Seiland igneous province, North Norway), allowing conclusions to be drawn concerning its origin and the role of late fluid alteration. The rock shows a rare mineral association: calcite + pyroxene + amphibole + zeolite group minerals + garnet + titanite, with apatite, allanite, magnetite and zircon as minor and accessory minerals, and it is classified as silicocarbonatite. Calcite, titanite and pyroxene (Di36-46 Acm22-37 Hd14-21) are primarily magmatic minerals. Amphibole of hastingsitic composition has formed after pyroxene at a late-magmatic stage. Zeolite group minerals (natrolite, gonnardite, Sr-rich thomsonite-(Ca)) were formed during hydrothermal alteration of primary nepheline by fluids/solutions with high Si-Al-Ca activities. Poikilitic garnet (Ti-bearing andradite) has inclusions of all primary minerals, amphibole and zeolites, and presumably crystallized metasomatically during a late metamorphic event (Caledonian orogeny). Whole rock chemical compositions of the silicocarbonatite differs from the global average of calciocarbonatites by elevated silica, aluminium, sodium and iron, but show comparable contents of trace elements (REE, Sr, Ba). Trace element distributions indicate within-plate tectonic setting of the carbonatite. The spatial proximity of carbonatite and alkaline ultramafic rock (melteigite), the presence of “primary nepheline” in carbonatite together with the trace element distributions indicate that the carbonatite was derived from crystal fractionation of a parental carbonated foidite magma. The main prerequisites for the extensive formation of zeolite group minerals in silicocarbonatite are revealed.