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There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here, we present a collaborative approach developed with the FABLE—Food, Agriculture, Biodiversity, Land, and Energy—Consortium to reconcile both global and national elements for developing national food and land-use system pathways. This approach includes three key features: (1) global targets, (2) country-driven multi-objective pathways, and (3) multiple iterations of pathway refinement informed by both national and international impacts. This approach strengthens policy coherence and highlights where greater national and international ambition is needed to achieve global goals (e.g., the SDGs). We discuss how this could be used to support future climate and biodiversity negotiations and what further developments would be needed.

Supply chain management includes strategic, tactical, and operational decisions for long, medium, and short-term planning. Strategic decisions, such as network design, and operational decisions, such as last-mile routing, have mutual implications. Therefore, modelling them separately can lead to sub-optimal solutions. The integrated modelling of these decisions has been addressed as a location routing problem (LRP). This paper aims to identify the solution strategies and methods to solve the LRP, as well as related challenges and research opportunities based on a critical literature review. The findings reveal that 46% of the reviewed publications have adopted a multistage modelling approach to address the LRP, sequentially tackling strategic and operational decisions. Moreover, in addition to the challenge of modelling diverse decision levels, the LRP models need to incorporate variables such as time windows, delivery failure rates, demand density, etc. Five research opportunities are proposed: i) modelling the first and last mile with a strategic approach when making strategic network decisions, ii) integrating environmental and social objectives into the modelling framework, iii) applying the solution methods and algorithms to complex real-world cases, iv) exploring competitive and cooperative models in LRP, and v) evaluating the use of emerging technologies. [ABSTRACT FROM AUTHOR]

Abstract With the different characters of datatypes and large amount of data going to be managed in open-source database, localization to the specific linguistics is the major concern in Ethiopia, as the nation used different datatypes compared to the Gregorian systems. In this regard Amharic localization in open-source database can handle the difficulties in managing data for governmental and non-governmental organizations. Amharic Extension Module was introduced to governmental organizations for the data management capabilities. But, there is no research that can explore the system’s quality, the users’ satisfaction and intension of continuance of Amharic Extension Module from the perspective of both computer literates and illiterates. Therefore, this research work attempt or try to empirically examine and analyze the system quality, the users’ satisfaction and intension of continuance of Amharic Extension Module from the perspective of all users in POESSA The major purpose/aim of this study/research is to brand or make up the research break/gap in the area of localization specific to the Amharic locals, and to show the implication of the practical and theoretical way based on the results of the research. For this purpose, questionnaires were used for the collection of the research data. A total of 395 copies of the questionnaires were distributed and 385 of them are collected without any problem from the organization indicated herewith. The statistical analysis tools such as SPSS and AMOS, and methods such as Structural equation model were used for the analysis of the research data. The results of the research recommended and suggested that system quality can significantly influence confirmation. Meanwhile, confirmation can directly and significantly influence perceived usefulness, performance expectations, and satisfaction. Additionally, performance expectation, perceived usefulness and confirmation can significantly impact/influence satisfaction. The satisfaction directly and most importantly and significantly influences the continuance intension. Finally, the research delivers/provides a concert indication for the legitimacy and validity of the integrated and combined models of UTUAT, ECTM, and D&M ISS in the field of localizations which can be a hypothetical and theoretical foundation for Amharic Extension Module—AEM users’ and services of it.

Achieving broad immunity through vaccination is a cornerstone strategy for long-term management of COVID-19 infections, particularly the prevention of serious cases and hospitalizations. Evidence that vaccine-induced immunity wanes over time points to the need for COVID-19 booster vaccines, and maximum compliance is required to maintain population-level immunity. Little is known of the correlates of intentions to receive booster vaccines among previously vaccinated individuals. The present study applied an integrated model to examine effects of beliefs from multiple social cognition theories alongside sets of generalized, stable beliefs on individuals' booster vaccine intentions. US residents (N = 479) recruited from an online survey panel completed measures of social cognition constructs (attitude, subjective norms, perceived behavioral control, and risk perceptions), generalized beliefs (vaccine hesitancy, political orientation, and free will beliefs), and COVID-19 vaccine intentions. Social cognition constructs were related to booster vaccine intentions, with attitude and subjective norms exhibiting the largest effects. Effects of vaccine hesitancy, political orientation, and free will beliefs on intentions were mediated by the social cognition constructs, and only vaccine hesitancy had a small residual effect on intentions. Findings provide preliminary evidence that contributes to an evidence base of potential targets for intervention messages aimed at promoting booster vaccine intentions.

The COVID-19 pandemic has had unprecedented health, economic, and social consequences worldwide. Although contact reductions and wearing face coverings have reduced infection rates, and vaccines have reduced illness severity, emergence of new variants of the coronavirus that causes COVID-19, and the shift from pandemic to endemic patterns of infection, highlights the importance of ongoing preventive behavior adherence to manage future outbreaks. Research applying social cognition theories may assist in explaining variance in these behaviors and inform the development of efficacious behavior change interventions to promote adherence. In the present article, we summarize research applying these theories to identify modifiable determinants of COVID-19 preventive behaviors and the mechanisms involved, and their utility in informing interventions. We identify limitations of these applications (e.g., overreliance on correlational data, lack of long-term behavioral follow-up), and suggest how they can be addressed. We demonstrate the virtue of augmenting theories with additional constructs (e.g., moral norms, anticipated regret) and processes (e.g., multiple action phases, automatic processes) to provide comprehensive, parsimonious behavioral explanations. We also outline how the theories contribute to testing mechanisms of action of behavioral interventions. Finally, we recommend future studies applying these theories to inform and test interventions to promote COVID-19 preventive behavior adherence.

In the Mediterranean Sea, interactions between marine species and human activities are prevalent. The coastal distribution of bottlenose dolphins Tursiops truncatus and the predation pressure they put on fishing stocks lead to regular interactions with fisheries. Multispecies occupancy models are a relevant framework to estimate co‐occurrence between two (or more) species while accounting for false negatives and potential interspecific dependance although requiring substantial quantity of data to fit. Here, we extended multispecies occupancy model to integrate multiple datasets to map spatial co‐occurrence between trawlers and bottlenose dolphins in the Gulf of Lion, French Mediterranean Sea combining data from aerial surveys and boat surveys at a large spatial scale. The integrated multispecies occupancy model produced more precise estimate than single‐dataset multispecies occupancy models. Our results support that both integrated and multispecies frameworks are relevant to map distribution and understand species interactions in our case study. Besides, our application of multispecies occupancy models to bottlenose dolphins and fishing trawlers enabled to map co‐occurrence probability, which open promising avenues in the understanding of interactions between human activities and marine mammals that occur at large spatial scales. [ABSTRACT FROM AUTHOR]

Abstract Background As people living with HIV (PLWH) experience earlier and more pronounced onset of noncommunicable diseases (NCDs), advancing integrated care networks and models in low-resource-high-need settings is critical. Leveraging current health system initiatives and addressing gaps in treatment for PLWH, we report our approach using a late-stage (T4) implementation research study to test the adoption and sustainability of a proven-effective implementation strategy which has been minimally applied in low-resource settings for the integration of hypertension control into HIV treatment. We detail our protocol for the Managing Hypertension Among People Living with HIV: an Integrated Model (MAP-IT) trial, which uses a stepped wedge cluster randomized trial (SW-CRT) design to evaluate the effectiveness of practice facilitation on the adoption of a hypertension treatment program for PLWH receiving care at primary healthcare centers (PHCs) in Akwa Ibom State, Nigeria. Design In partnership with the Nigerian Federal Ministry of Health (FMOH) and community organizations, the MAP-IT trial takes place in 30 PHCs. The i-PARiHS framework guided pre-implementation needs assessment. The RE-AIM framework will guide post-implementation activities to evaluate the effect of practice facilitation on the adoption, implementation fidelity, and sustainability of a hypertension program, as well as blood pressure (BP) control. Using a SW-CRT design, PHCs sequentially crossover from the hypertension program only (usual care) to hypertension plus practice facilitation (experimental condition). PHCs will recruit and enroll an average of 28–32 patients to reach a maximum of 960 PLWH participants with uncontrolled hypertension who will be followed longitudinally for BP outcomes. Discussion Given the need for integrated NCD-HIV care platforms in low-resource settings, MAP-IT will underscore the challenges and opportunities for integrating hypertension treatment into HIV care, particularly concerning adoption and sustainability. The evaluation of our integration approach will also highlight the potential impact of a health systems strengthening approach on BP control among PLWH. Trial registration Clinicaltrials.gov ( NCT05031819 ). Registered on 2nd September 2021.

The article discusses the challenges faced by patients with eating disorders as they transition from child and adolescent mental health services (CAMHS) to adult mental health services (AMHS) in high-income countries. Many stakeholders criticize the current binary care model, which often leads to delays in treatment and disruptions in care. Eating disorder experts propose age-integrated services as a solution to improve service continuity and provider trust. However, the practicality and benefits of integrated services need further evaluation, as countries trialling such models have yet to document clinical improvements in patients. The article calls for more research to understand transition outcomes and improve eating disorder care across the lifespan. [Extracted from the article]

Approaches to assessing the economics of airships in an era of import substitution are considered. Existing approaches take no account of customer requirements. Most available methods simply formalize expert evaluations and are subjective. The economic analysis of passenger aircraft must be based on the assessment of investment projects. Indices used to assess long-term investment projects include the net present value, the profitability, the payback period, and the modified internal rate of return. An integrated model of the life cycle costs consisting of several components is considered. Factors analyzed include the costs over the aircraft's life, direct operational costs, and maintenance and repair costs. The role of information technologies at the current stage of aircraft development is discussed. A model of the interaction of participants in the aircraft life cycle is considered. Planning and cost management for the aircraft life cycle are studied. Constant iterative processes involving the exchange of information between manufacturers, operators, and aviation authorities generate integrated requirements for the production of competitive aircraft. In improving aircraft performance, such requirements must be taken into account in the early stages of design. [ABSTRACT FROM AUTHOR]

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.

Background: As people living with HIV (PLWH) experience earlier and more pronounced onset of noncommunicable diseases (NCDs), advancing integrated care networks and models in low-resource-high-need settings is critical. Leveraging current health system initiatives and addressing gaps in treatment for PLWH, we report our approach using a late-stage (T4) implementation research study to test the adoption and sustainability of a proven-effective implementation strategy which has been minimally applied in low-resource settings for the integration of hypertension control into HIV treatment. We detail our protocol for the Managing Hypertension Among People Living with HIV: an Integrated Model (MAP-IT) trial, which uses a stepped wedge cluster randomized trial (SW-CRT) design to evaluate the effectiveness of practice facilitation on the adoption of a hypertension treatment program for PLWH receiving care at primary healthcare centers (PHCs) in Akwa Ibom State, Nigeria. Design: In partnership with the Nigerian Federal Ministry of Health (FMOH) and community organizations, the MAP-IT trial takes place in 30 PHCs. The i-PARiHS framework guided pre-implementation needs assessment. The RE-AIM framework will guide post-implementation activities to evaluate the effect of practice facilitation on the adoption, implementation fidelity, and sustainability of a hypertension program, as well as blood pressure (BP) control. Using a SW-CRT design, PHCs sequentially crossover from the hypertension program only (usual care) to hypertension plus practice facilitation (experimental condition). PHCs will recruit and enroll an average of 28–32 patients to reach a maximum of 960 PLWH participants with uncontrolled hypertension who will be followed longitudinally for BP outcomes. Discussion: Given the need for integrated NCD-HIV care platforms in low-resource settings, MAP-IT will underscore the challenges and opportunities for integrating hypertension treatment into HIV care, particularly concerning adoption and sustainability. The evaluation of our integration approach will also highlight the potential impact of a health systems strengthening approach on BP control among PLWH. Trial registration: Clinicaltrials.gov (NCT05031819). Registered on 2nd September 2021. [ABSTRACT FROM AUTHOR]

To ensure the survival of society, an enormous amount of energy is required to sustain the economic and social development of communities. In addition, there is a pressing need to achieve significant reductions in climate change and the associated costs of implementing systems based on traditional energy sources, as well as addressing the issue of providing electricity to isolated areas. In rural environments, there is an alternative energy source with enormous potential, agricultural biomass, which can produce electrical and thermal energy and can progressively help to reduce dependence on fossil fuels. The purpose of this work is to present a dynamic simulation model of a power generation plant that uses the Joule Brayton thermodynamic cycle, based on a gas turbine which is fueled by residual agricultural biomass; the cycle converts mechanical energy to electrical energy. The problem is approached through the characterization of the biomass, mathematical models of the plant components, and simulation of the system behavior in different scenarios. The simulations are processed in Matlab/Simulink, which allows the model to be verified, validating the equilibrium relationship between generation and load demand. [ABSTRACT FROM AUTHOR]

Over the last decades, large‐scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large‐scale population dynamics from a single monitoring programme is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify the population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialized ecological estimates that would be difficult to obtain if data were analysed separately. In this study, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus), we combined 21,464 km of photo‐identification boat surveys collecting spatial capture–recapture data with 24,624 km of aerial line transect following a distance sampling protocol. We analysed spatial capture–recapture data together with distance sampling data to estimate the abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture–recapture model fitted independently to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional‐scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales. [ABSTRACT FROM AUTHOR]

Conceptual models are simplified representations of the main components and processes of a dynamic system, the mechanisms by which they are related, and the ways they are observed (i.e., the data generating processes). Constructing a conceptual model (CM) should be the first step when planning a new stock assessment or updating previous assessments, because it can improve the modelling process by guiding the workflow and "modelling what to model". CMs should be built by summarizing information about a system while also proposing hypotheses or assumptions about the uncertainties and unknown aspects. Several steps are necessary to build a CM: 1) gather known information about the species and the fisheries that interact with it, 2) state the objectives of the stock assessment, 3) define the spatial scale, 4) define the temporal scale, and 5) outline components and processes of the system (biological, fisheries and observation processes) and what drives them. Initial draft CMs should be based on the best available science and constructed using the fundamental principles of ecology, socioecology, fisheries and other relevant sciences. CMs offer a framework for integrating knowledge across domains, and benefit from an elicitation process. The elicitation process is a set of deliberate activities (e.g., workshops) that allow other experts and relevant parties to contribute with their knowledge to enrich draft CMs. CMs are not static entities but rather dynamic constructs that can identify future research directions and evolve to incorporate new insights and knowledge. Fisheries systems for highly migratory pelagic species in the Pacific Ocean (north Pacific Albacore tuna, eastern Pacific Dorado, and south Pacific Swordfish) are used as examples to illustrate how to develop CMs, and demonstrate improvements to the subsequent assessment models following development of the CMs. [Display omitted] • Conceptual models (CMs) represent components and processes of dynamic systems, their relationships, and ways they are observed. • CMs should be based on fundamental principles of fisheries and other sciences, and published and unpublished knowledge. • Eliciting knowledge from experts and relevant parties, on subjects of their competence, drawing from their accumulated experience, enrich CMs. • Better stock assessments are achieved when CMs are built beforehand. [ABSTRACT FROM AUTHOR]

Objective: We applied an integrated social cognition model to predict physical distancing behavior, a key COVID-19 preventive behavior, over a four-month period. Design: A three-wave longitudinal survey design. Methods: Australian and US residents (N = 601) completed self-report measures of social cognition constructs (attitude, subjective norm, moral norm, perceived behavioral control [PBC]), intention, habit, and physical distancing behavior on an initial occasion (T1) and on two further occasions one week (T2) and four months (T3) later. Results: A structural equation model revealed that subjective norm, moral norm, and PBC, were consistent predictors of physical distancing intention on all three occasions. Intention and habit at T1 and T2 predicted physical distancing behavior at T2 and T3, respectively. Intention at T2 mediated effects of subjective norm, moral norm, and PBC at T2 on physical distancing behavior at T3, and habit at T1 and T2 mediated effects of behavior at T1 and T2 on follow-up behavior at T2 and T3, respectively. Conclusion: Normative (subjective and moral norms) and capacity (PBC) constructs were consistent predictors of physical distancing intention, and intention and habit were consistent predictors of physical distancing behavior. Interventions promoting physical distancing should target change in normative and personal capacity beliefs, and habit. Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2021.1968397. [ABSTRACT FROM AUTHOR]

Stress management interventions have traditionally aimed to change coping behaviour with little attention to mechanisms that drive behaviour change. We sought to test an integrated dual‐process model, accounting for reasoned and automatic processes, for predicting problem‐focussed coping behaviour. The study adopted a two‐wave prospective correlational design with a 1‐week follow‐up. University students aged 17–25 (N = 272) completed survey measures online. At Time 1, participants completed self‐report measures of attitude, subjective norm, perceived behavioural control, intention, behavioural automaticity, and past problem‐focussed coping behaviour. At Time 2, participants completed follow‐up measures of behavioural automaticity and problem‐focussed coping behaviour. Structural equation modelling testing the hypothesised dual‐process model exhibited a good fit to the data, accounting for 50.0% and 45.4% of the variance in intentions and problem‐focussed coping behaviour, respectively. Attitude, subjective norm, perceived behavioural control, and past behaviour directly predicted intention. Intention, past behaviour, and behavioural automaticity directly predicted problem‐focussed coping behaviour. Past behaviour also indirectly predicted problem‐focussed coping behaviour via behavioural automaticity. Results suggest that problem‐focussed coping behaviour tends to be regulated by reasoned psychological processes, and more strongly by automatic psychological processes. Future research aiming to increase problem‐focussed coping should utilise behaviour change methods known to influence these processes. [ABSTRACT FROM AUTHOR]

This research focuses on developing models to estimate the system reliability of Unmanned Ground Vehicles using knowledge and data from similar systems. Reliability is often a stand‐alone requirement and not always fully included in performance and life cycle cost models. Traditional reliability approaches require detailed knowledge of a system and are used in later design sta ges as well as development, operational test and evaluation, and operations. The critical role of reliability and its impact on acquisition program performance, cost, and schedule motivates the need for improved system reliability models in the early design stages. This research seeks to integrate reliability, performance, and cost models in a trade‐off analysis framework in the early acquisition stages. This research uses functional analysis methods to estimate reliability Pre‐Milestone A and assess the impact of reliability on performance and cost models of early system concepts. This research us es technology readiness level (TRL), which is indexed, to assess different levels of reliability for design. An integrated cost and performance model will inform decision ‐makers on the impact of reliability before choosing a system concept for further development. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) has been widely applied to evaluate the performance of banks, enterprises, governments, research institutions, hospitals, and other fields as a non-parametric estimation method for evaluating the relative effectiveness of research objects. However, the composition of its effective frontier surface is based on the input-output data of existing decision units, which makes it challenging to apply the method to predict the future performance level of other decision units. In this paper, the Slack Based Measure (SBM) model in DEA method is used to measure the relative efficiency values of decision units, and then, eleven machine learning models are used to train the absolute efficient frontier to be applied to the performance prediction of new decisions units. To further improve the prediction effect of the models, this paper proposes a training set under the DEA classification method, starting from the training-set sample selection and input feature indicators. In this paper, regression prediction of test set performance based on the training set under different classification combinations is performed, and the prediction effects of proportional relative indicators and absolute number indicators as machine-learning input features are explored. The robustness of the effective frontier surface under the integrated model is verified. An integrated models of DEA and machine learning with better prediction effects is proposed, taking China's regional carbon-dioxide emission (carbon emission) performance prediction as an example. The novelty of this work is mainly as follows: firstly, the integrated model can achieve performance prediction by constructing an effective frontier surface, and the empirical results show that this is a feasible methodological technique. Secondly, two schemes to improve the prediction effectiveness of integrated models are discussed in terms of training set partitioning and feature selection, and the effectiveness of the schemes is demonstrated by using carbon-emission performance prediction as an example. This study has some application value and is a complement to the existing literature. [ABSTRACT FROM AUTHOR]

It is widely recognized that there exists a close relationship between the health condition of manufacturing equipment and the overall quality of the manufactured product. It is, therefore, vital and of paramount practical importance and theoretical significance to develop optimized/integrated models of statistical process control (SPC) and maintenance planning (MP). The paper targets integration of the decisions of MP and SPC for a two-stage dependent manufacturing process. Each stage of the process can either be in the "in-control" state or in the "out-of-control" state such that transitions occur due to manufacturing equipment degradation/failure. Four control charts are developed to monitor the process by formulating the problem based on the renewal theory and considering different potential scenarios. Based on the intuitively appealing concept of opportunistic maintenance, we develop a novel integrated SPC and MP framework referred to as the Opportunistic Maintenance Integrated Model (OMIM), which takes into account both process and equipment conditions. A genetic algorithm (GA) is then applied to find the optimal values of the decision variables minimizing the long-run expected average cost per unit time. As a benchmark to evaluate the performance of the proposed OMIM, another integrated model referred to as the Non-Opportunistic Maintenance Integrated Model (NOMIM) is developed. Numerical results illustrate the superior performance of the proposed OMIM framework in comparison with its counterparts. [ABSTRACT FROM AUTHOR]

Models that capture relationships between a variety of social, economic, and environmental factors are useful tools for community planning; however, they are often complex and difficult for diverse audiences to understand. This creates challenges for participatory planning and community engagement. Conducted in the community of Squamish (British Columbia, Canada), this study develops and examines tools for communicating outcomes of a community scenario modelling exercise to diverse stakeholders. These tools are (i) a “model explorer” and (ii) realistic, immersive visualizations. The model explorer is an online, HTML5-based tool that can be used to learn about the model, view community scenario maps, and explore potential outcomes of the scenarios. The visualizations are virtual environments that are navigated from the first-person perspective, and they were developed using a combination of ArcGIS, Trimble SketchUp, Adobe Photoshop, and the Unity3D game engine. A local government and community stakeholder focus group and public open house event were held to solicit feedback on the scenarios and tools. Findings of the research suggest that the two types of tools can be used in a complementary fashion, and tool integration can better harness their respective strengths in a manner that comprehensively communicates the implications of different development pathways to diverse community members.

Many fisheries in the world are data-moderate, with data types (e.g., total removals, abundance indices, and biological composition data) of varied quality (e.g., limited time series or representative samples) or available data. Integrated stock assessments are useful tools for data-moderate fisheries as they can include all available information, can be updated due to the availability of more information over time, and can directly test the inclusion and exclusion of specific data types. This study uses the simulation testing and systematic data reduction from the US West Coast benchmark assessments to examine the performance of Stock Synthesis with catch and length (SS-CL) compositions only. The simulation testing of various life histories, recruitment variabilities, and data availability scenarios found that the correctly specified SS-CL can estimate unbiased key population quantities such as stock status with as little as 1 year of length data although 5 years or more may be more reliable. The error in key population quantities is decreased with an increase in years and the sample size of length data. The removal of the length compositions from benchmark assessments often caused large model deviations in the outputs compared to the removal of other data sources, indicating the importance of length data in integrated models. Models with catch and length data, excluding abundance indices and age composition, generally provided informative estimates of the stock status relative to the reference model, with most data scenarios falling within the CIs of the reference model. The results of simulation analysis and systematic data reduction indicated that SS-CL is potentially viable for data-moderate assessments in the USA, thus reducing precautionary buffers on catch limits for many stocks previously assessed in a lower tier using catch-only models. SS-CL could also be applied to many stocks around the world, maximizing the use of data available via the well tested, multifeature benefits of SS.

The problem of mathematical description of nonlinear dynamical systems remains relevant today, especially given the need to build modern surveillance systems for complex technical objects, such as power plants. The use of polynomial operators obtained with the help of shortened Volterra series to solve this problem, as practice has shown, proved to be promising, because this method allows to display in the mathematical model both nonlinear and dynamic properties of systems. For further development of the method, it is advisable to analyze different approaches in order to build effective algorithms for obtaining and applying in the problems of monitoring the functioning of nonlinear systems. [ABSTRACT FROM AUTHOR]

Dispositional, intra-personal constructs such as personality traits and generalized beliefs are consistently related to health behaviors, but relatively few studies have tested the theory-based mechanisms by which these constructs relate to health behaviors and compared them across behavior type. In the current study we tested an integrated theoretical model in which belief-based social cognition constructs (attitudes, subjective norms, perceived behavioral control) were proposed to mediate effects of personality traits (conscientiousness, extroversion) and socio-political beliefs (political beliefs, locus of control, free will beliefs) on participation in three health-related behaviors: physical activity, COVID-19 vaccination, and sugar-sweetened beverage restriction. Proposed integrated model effects were tested in a five-week prospective correlational study. Finnish residents completed measures of personality, socio-political, and social cognition constructs with respect to physical activity participation (N = 557), COVID-19 vaccination uptake (N = 1,115), and sugar-sweetened beverage restriction (N = 558) and self-reported their behavior at follow-up. Structural equation models revealed direct effects of intention on behavior, and of social-cognition constructs on intention, across all behaviors. We also found indirect effects of political beliefs on behavior mediated by social cognition constructs and intentions for COVID-19 vaccination and sugar-sweetened beverage restriction behaviors, indirect effects of conscientiousness on behavior mediated by social cognition constructs and intentions for physical activity and sugar-sweetened beverage restriction behaviors, and indirect effects of health locus of control on behavior mediated by social cognition constructs and intentions for physical activity behavior. Finally, we found a negative total effect of populist beliefs on behavior for COVID-19 vaccination behavior. Findings contribute to an evidence base for the effects of dispositional and social cognition constructs of health behaviors, point to a possible mechanism by which these generalized constructs relate to health behavior, and outline how the pattern of effects varies across the different behaviors. • Effects of dispositions on health behaviors should be mediated by social cognition. • Political belief effects on health behaviors mediated by social cognition constructs. • Conscientiousness indirectly related to exercise and sugar-sweetened beverage behaviors. • Indirect effects of locus of control through social cognition for exercise behavior. [ABSTRACT FROM AUTHOR]

Deep neural networks are a class of powerful machine learning model that uses successive layers of nonlinear processing units to extract features from data. However, the training process of such networks is quite computationally intensive and uses commonly used optimization methods that do not guarantee optimum performance. Furthermore, deep learning methods are often sensitive to noise in data and do not operate well in areas where data are incomplete. An alternative, yet little explored, method in enhancing deep learning performance is the use of fuzzy systems. Fuzzy systems have been previously used in conjunction with neural networks. This survey explores the different ways in which deep learning is improved with fuzzy logic systems. The techniques are classified based on how the two paradigms are combined. Finally, the real-life applications of the models are also explored. [ABSTRACT FROM AUTHOR]

Most mechanistic predator–prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator–prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data – including in small amounts – can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed. • Short ecological time series and nonlinear models make parameter inference difficult. • We introduce discrete-time stochastic predator–prey models with stochastic kill rates. • They allow to fit dynamic models to both population density and kill rate data. • Fixed point attractors in fact require kill rate data for practical identifiability. • Even in small amounts, kill rate data always benefits the estimation process. [ABSTRACT FROM AUTHOR]

Over the last several decades, land-use/transport interaction models have evolved. Although these models have the potential to become primary demographic forecasting and planning vehicles in metropolitan transportation planning for most large US urban regions, some gaps and improvements must be addressed. This paper briefly discusses a newly developed and refined integrated land-use/transportation model. It also introduces innovative approaches to modeling an urban area including a variant of a geographic information system-based land-use and environmental suitability analysis, as main components in deriving development potential for a small-cell grid of the study region. This approach enables the inclusion of public and stakeholder input into the modeling process, facilitates micro-level consideration of trip generation, trip distribution, and mode-choice inside the land-use demographic model, thus furthering the integration of transportation and land use in the modeling process. Such considerations and utilization of rule-based approaches and concerns of economic development and environmental and sustainability factors help close some existing gaps of operational models designed for real world practical applications. All of these features contribute toward further improvement of these models.

Data envelopment analysis (DEA) has been widely applied to evaluate the performance of banks, enterprises, governments, research institutions, hospitals, and other fields as a non-parametric estimation method for evaluating the relative effectiveness of research objects. However, the composition of its effective frontier surface is based on the input-output data of existing decision units, which makes it challenging to apply the method to predict the future performance level of other decision units. In this paper, the Slack Based Measure (SBM) model in DEA method is used to measure the relative efficiency values of decision units, and then, eleven machine learning models are used to train the absolute efficient frontier to be applied to the performance prediction of new decisions units. To further improve the prediction effect of the models, this paper proposes a training set under the DEA classification method, starting from the training-set sample selection and input feature indicators. In this paper, regression prediction of test set performance based on the training set under different classification combinations is performed, and the prediction effects of proportional relative indicators and absolute number indicators as machine-learning input features are explored. The robustness of the effective frontier surface under the integrated model is verified. An integrated models of DEA and machine learning with better prediction effects is proposed, taking China’s regional carbon-dioxide emission (carbon emission) performance prediction as an example. The novelty of this work is mainly as follows: firstly, the integrated model can achieve performance prediction by constructing an effective frontier surface, and the empirical results show that this is a feasible methodological technique. Secondly, two schemes to improve the prediction effectiveness of integrated models are discussed in terms of training set partitioning and feature selection, and the effectiveness of the schemes is demonstrated by using carbon-emission performance prediction as an example. This study has some application value and is a complement to the existing literature.

The features of the mechanism of heuristic methods application in marketing modeling are investigated in this paper. The essence of methods of economic analysis in advertising is revealed. Approaches to media planning using heuristic methods are analyzed in detail. Step-by-step evaluation matrix and expert preferences matrix for ranking the advertising evaluation criteria are proposed. The use of integrated models for advertising campaigns modeling is substantiated. Mathematical expressions for their application in the integrated models construction and the use of heuristic methods in media planning are presented. The matrix of expert advantages in terms of ranking the advertising evaluating criteria is proposed. The application of «Delphi» method for processing the results of the analysis of iterative opinions convergence is proposed. The results of modeling the options for evaluating the advertising media choice are presented. It is proved that in the market economy the importance of modeling marketing processes is growing and is that firstly it is necessary to test it on laboratory models, and then in the current market conditions to model the processes required for quality activities in the market. During the crisis most of the companies need strategic forecasting of their activities and for this purpose it is advisable to use methods of economic analysis. It is investigated that heuristic methods (methods of activating creativity, non-standard thinking) include the group of methods for generating solutions of problems. It is determined that the methods of heuristic search are diverse and include both methods of activating creativity and their modification and combination. It is proposed to conduct modeling using heuristic methods in several stages. For this purpose, step-by-step model including thirty steps is used. In this paper expert methods for marketing analysis are used, as they are a set of logical and mathematical techniques, as a result of which experts receive the information necessary for making informed rational management decisions. Both methods of non-directional search including methods of brainstorming, expert assessments, associations and analogies, control questions, collective notebook and directional search, including the algorithm for solving inventive problems are used in this investigation. Heuristic methods are used in marketing analysis because they involve the study and formalization of human thinking in solving various problems. It is proposed in this paper to improve the algorithm for choosing the optimal type of advertising, which is carried out in four stages and has clear heuristic basis. It is offered in this algorithm to include the definition of complex priority in the third stage making it possible to estimate qualitatively the selected options of advertising. The complex indicator for each type of advertising is defined as the sum of products of the relative priorities of advertising on the relative priorities of the criteria for their evaluation. The type of advertising receiving the highest priority can be considered as the best one for the company. [ABSTRACT FROM AUTHOR]

Aquatic ecosystem services are important for human wellbeing, but they are much less studied than terrestrial ecosystem services. The objectives of this study are to broaden, itemize and exemplify the human-nature interactions in modeling the future provision of aquatic ecosystem services. We include shared socioeconomic and representative concentration pathways, used extensively in climate research, as drivers of change for the future development of the Baltic Sea. Then we use biogeochemical and ecosystem models to demonstrate the future development of exemplary supporting, provisioning and cultural ecosystem services for two distinct combinations of regionally downscaled global climate and socioeconomic futures. According to the model simulations, the two global futures ("Sustainable well-being" vs. "Fossil-fuelled development") studied lead to clearly deviating trajectories in the provision of marine ecosystem services. Under the "Sustainable well-being"-scenario primary production decreases by 20%, catches of demersal fish increases and the recreation opportunities increase significantly by the end of the ongoing century. Under the "fossil-fuelled development"-scenario primary production doubles, fisheries focus on less valued pelagic fish and the recreation possibilities will decrease. Long-term projections of aquatic ecosystem services prepared for alternative global socioeconomic futures can be used by policy makers and managers to adaptively and iteratively adjust mitigation and adaptation effort with plausible future changes in the drivers of water pollution. [ABSTRACT FROM AUTHOR]

In the last 10-15 years, we have witnessed remarkable technological changes and particularly spectacular innovations, which have led to a complete change in the way business develops, in the way communication changes or in the way we choose to live our lives. All these changes have forced organizations to develop and improve their customer relationships and try to find solutions to convey consistent messages in their interactions with all interested parties - the state and local authorities, their own employees, suppliers, financial institutions - using a wide range of integrated communication tools. [ABSTRACT FROM AUTHOR]

The travel impedance skim matrix is one of the most essential intermediate products within transportation forecasting models and is a fundamental input for activity-based transportation forecasting models. It reflects interzonal travel time, travel time reliability, travel costs, etc. by time of day. The traditional method to obtain skim matrices is to execute multiple times of time-dependent, shortest-path calculations. However, the computational and memory use burden can easily increase to an intractable level when dealing with mega-scale networks, such as those with thousands of traffic-analysis zones. This research proposes two new approaches to extract the interzonal travel impedance information from the already existing vehicle trajectory data. Vehicle trajectories store travel impedance information in a more compact format when compared to time-dependent link performance profiles. The numerical experiments highlight huge potential advantages of the proposed approaches in terms of saving both memory and CPU time.

Integrated models can support community planning efforts because they have the ability to elucidate social, economic, and environmental relationships and outcomes associated with different local development plans and strategies. However, deciding what to include in an integrated model presents a significant challenge, as including all aspects of a community and local environment is unfeasible, whereas including too few aspects leads to a non-representative model. This research aimed to address this challenge by employing an iterative, participatory process in an integrated modelling effort. Conducted in Squamish (BC, Canada), the research involved developing a community systems model and scenarios (i.e., different community development patterns), modelling the scenarios, evaluating the model through a community focus group, and refining the model and scenarios based on the feedback. Much of the work developing the initial systems model and scenarios was done a previous research phase, and it involved assembling local government and community stakeholder focus groups to discuss issues and possible futures for Squamish. Analysis of the focus group data informed the design of a community systems model and local development scenarios, which were subsequently applied in an integrated modelling exercise. Modelling primarily used ArcGIS and R, and explored a variety of factors including access to amenities, education, walkability, parks/trails, food and farm systems, public transit, housing affordability, threats to critical habitat, etc. Another local government and community stakeholder focus group was held to solicit feedback on the model and scenarios, which were then refined based on the feedback. The research found the participatory approach to beneficial for creating community planning tools with high relevance to local contexts and needs. The model developed in this work has great potential for supporting community planning because it effectively identifies the co-benefits and trade-offs of different development strategies. It is important to develop these types of community planning tools through iterative processes, where they are refined through multiple stages of feedback by a variety of stakeholders, to better capture the local concerns and realities of a place.

The carried out studies show that from the point of view of the issue of efficiency increase of logistical systems, there are several key aspects. Firstly, choice of methods for managing the triad of logistics functions ‘inventory management - warehousing – transportation’, where the inventory management issues are considered as the most relevant ones. Secondly, there is recognized the need to move the studies of multi-level systems within the framework of the concept of supply chain management.

Since 2007, the operating conditions of companies have changed significantly and can be described as more unpredictable. Insolvency of one company may, by the domino effect, have negative impacts on other operators. In extreme cases, these impacts can lead to their bankruptcy. Therefore, it is important to constantly monitor both the financial condition of a company and the financial condition of its business partners. In order to evaluate the financial standing of a company different types of methods can be employed. The aim of the paper was to build two models that specify more than two states of financial standing of manufacturing businesses. The use of the models enables recognition of the deteriorating financial condition of manufacturing companies a few years before insolvency is declared. The traditional discriminant model and Bayesian model were constructed. Cluster analysis was used to select classes of financial standing of the analyzed companies. The models were tested on two sets of samples. A small sample consisted of 224 (112 + 112) companies and a large sample consisted of more than 10,600 companies. The results showed that the traditional discriminant model performs better than the Bayesian model for classifying companies. [ABSTRACT FROM AUTHOR]

During the last few decades, open pit mines have been deepened to the remote depths of the ground such that removing great volumes of waste rocks may jeopardize their profitability. In such circumstances, an early transition from open pit to underground mining may prove more profitable. This is the case in many large-scale open pit mines, where considerable amount of mineral reserve remains beneath the pit bottom. This paper develops a set of classified integer programming models for determining an optimum transition depth (OTD) between open pit and various underground mining methods. The models lie within the scope of long-term production scheduling because the OTD is revealed through scheduling of all mineral reserve. To provide a quantitative analysis, two optimization models with distinct solution strategies are executed on a three-dimensional sector of a real orebody. The results indicate that the integrated models increase the total NPV of the mining operations by up to 8.62%. However, the scenario-based models enhance the primitive solution (the base scenario) by up to 3.42%. It is also shown that the integrated models present solutions that are practically more realistic. [ABSTRACT FROM AUTHOR]

Land-use change is a direct driver of biodiversity and carbon storage loss. Projections of future land use often include notable expansion of cropland areas in response to changes in climate and food demand, although there are large uncertainties in results between models and scenarios. This study examines these uncertainties by comparing three different socioeconomic scenarios (SSP1-3) across three models (IMAGE, GLOBIOM and PLUMv2). It assesses the impacts on biodiversity metrics and direct carbon loss from biomass and soil as a direct consequence of cropland expansion. Results show substantial variation between models and scenarios, with little overlap across all nine projections. Although SSP1 projects the least impact, there are still significant impacts projected. IMAGE and GLOBIOM project the greatest impact across carbon storage and biodiversity metrics due to both extent and location of cropland expansion. Furthermore, for all the biodiversity and carbon metrics used, there is a greater proportion of variance explained by the model used. This demonstrates the importance of improving the accuracy of land-based models. Incorporating effects of land-use change in biodiversity impact assessments would also help better prioritize future protection of biodiverse and carbon-rich areas. [ABSTRACT FROM AUTHOR]

Ecological theories often encompass multiple levels of biological organization, such as genes, individuals, populations, and communities. Despite substantial progress toward ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we describe an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and might strengthen links between statistical ecology and ecological models and theories that span multiple levels of organization. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and outline a simple application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate age-specific survival and reproduction from size-structured data, accounting for imperfect detection of individuals. Given that such parameter estimates would be infeasible without an integrated model, we argue that integrated models will strengthen ecological theory by connecting theoretical and mathematical models directly to empirical data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step toward unifying ecology among levels.