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Abstract Muscle atrophy, characterized by the loss of muscle mass and function, is a hallmark of sarcopenia and cachexia, frequently associated with aging, malignant tumors, chronic heart failure, and malnutrition. Moreover, it poses significant challenges to human health, leading to increased frailty, reduced quality of life, and heightened mortality risks. Despite extensive research on sarcopenia and cachexia, consensus in their assessment remains elusive, with inconsistent conclusions regarding their molecular mechanisms. Muscle atrophy models are crucial tools for advancing research in this field. Currently, animal models of muscle atrophy used for clinical and basic scientific studies are induced through various methods, including aging, genetic editing, nutritional modification, exercise, chronic wasting diseases, and drug administration. Muscle atrophy models also include in vitro and small organism models. Despite their value, each of these models has certain limitations. This review focuses on the limitations and diverse applications of muscle atrophy models to understand sarcopenia and cachexia, and encourage their rational use in future research, therefore deepening the understanding of underlying pathophysiological mechanisms, and ultimately advancing the exploration of therapeutic strategies for sarcopenia and cachexia.

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling. [ABSTRACT FROM AUTHOR]

Metal‐coordination mediated tough hydrogels have been considered as a promising materials for novel utilization in programmable actuation structures. However, the rate‐dependent mechanical behavior attributed to their intrinsic mechanical properties of mixed elasticity and viscosity, imposing a challenge for the realization of controllable deformations. In this work, the effect of stretch rate was incorporated into a variable fractional order model in order to describe and predict the rate‐dependent mechanical response of physically tough hydrogels. Firstly, we provide an iteration algorithm for model estimation and conduct simulations of the stress response under monotonic stretch to verify the applicability of proposed model. The performance of proposed model is compared with the existing model to validate its accuracy. Then, the present model is employed to describe the rate‐dependent stress response under cyclic loading. The change rule of related parameters with stretch rate is also unraveled by the present model, which is further utilized to test the model's predictive ability. Moreover, the physical meaning of variable order is qualitatively explored in conjunction with the evolution mechanism of dynamic bonds. [ABSTRACT FROM AUTHOR]

Special education (SPED) teacher preparation programs are intended to help preservice teachers (PSTs) learn and adopt effective teaching practices to serve students with special needs. To help SPED-PSTs learn needed skills, SPED mentor teachers (MTs) must employ high-leverage practices (HLPs) such as performance feedback, conferencing, and modeling. Although most teacher preparation programs utilize modeling in preparing PSTs, little is known about how SPED-MTs use this mentoring tool. An interpretative phenomenological analysis was conducted to explore how SPED-MTs engaged PSTs in modeling practices during a practicum experience. Data from semi-structured interviews were analyzed to describe SPED-PST and SPED-MT attitudes, experiences, and use of modeling over the course of a special education practicum, focusing on MTs' modeling strategies and PSTs' subsequent behaviors. The relative impacts of simple and cognitive modeling on the PST-MT relationship were compared. SPED-MTs' modeling foci were compared to recommended HLPs. This preliminary study provides recommendations for SPED-MTs' use of modeling with SPED-PSTs. [ABSTRACT FROM AUTHOR]

The development of scientific reasoning competencies is a key goal of science education. To better understand the complex construct of scientific reasoning, which includes modeling as one style of reasoning, thorough investigations of the underlying processes are needed. Therefore, in this study, a typology of preservice science teachers' modeling strategies was developed. Thirty-two preservice science teachers were videotaped while engaging in the modeling task of investigating a black box. Following a qualitative content analysis, sequences of modeling activities were identified. By transforming these sequences of modeling activities into state transition graphs, six types of modeling strategies were derived, differing in the homogeneity and complexity of their modeling processes. The preservice science teachers engaged in activities of (1) exploration only; (2a) exploration and development with a focus on development; (2b) exploration and development with a focus on exploration; (2c) exploration and development, balanced; (3a) exploration, development, and drawing predictions from a model once; or (3b) exploration, development, and repeatedly drawing predictions from a model. Finally, this typology is discussed regarding the process of its development and its potential to inform and guide further research as well as the development of interventions aiming to foster competencies in scientific modeling. [ABSTRACT FROM AUTHOR]

As global energy demand rose by 2.3% in comparison to the last year rate of 1.2%, fossil fuels, biofuels and natural gas are becoming increasingly important in power generation and production of the liquid fuels. However, the presence of the contaminants, such as H2S and CO2 in the derived products from these fuels is the cause of several operational and technical issues ranging from equipment corrosion, fouling, catalyst deactivation, and environmental pollution. Therefore, it is necessary to remove contaminants from these gaseous mixtures for their effective use for multiple applications. The objective of this review is to explore various experimental, theoretical and simulation approaches which are developed to improve the operation and design of different H2S and CO2 removal technologies. The fundamental thermodynamic and kinetic models for the acid gas removal processes through simulation, design related to their improvements in the absorber and the stripper through experimentation and simulation work and relevant optimization studies based on chemical absorption have been discussed. The simulation and experimental work focusing on physical absorption is also summarized. The experimental and simulation-based studies on blends of different solvents are presented. The comparison of various solvents related to H2S and CO2 removal techniques is demonstrated. Feasibility assessment of various H2S removal techniques via experimentation is presented. The effort for process intensification and new modifications has led to better understanding of acid gas removal techniques. Improvements are eminent in enhancing the efficiency of the absorber and stripper through reduction in the heat requirements. The scope of blends of various solvents for use at commercial scale and optimization of the operating conditions can lead to reduction in capital and operating cost. Finally, the future directions of research to improve the cleaning processes have been indicated. [ABSTRACT FROM AUTHOR]

Contemporary society has grown increasingly dependent on the integration of knowledge for decision-making. In this context, systemic modeling is acknowledged as a straightforward tool for representing and analyzing complex problems. To address how systemic modeling is being conducted to guide and support public policy-making, this study offers a brief synthesis of the literature on systemic modeling oriented to help public policy decision-making. The results are compared to three principles for good systemic modeling to support public policy, established by the authors: the model must a) be readable and manageable —to a basic level— by non-experts, b) require as little quantitative data as possible, and c) not generate spurious or ambiguous readings of their content or their outputs. To identify modeling patterns the models were subjected to a content analysis under eleven different categories. To depict the possible co-occurrence of these analyzed categories in order to describe different types of modeling, a multiple correspondence analysis was performed. We found different modeling patterns with a marked trend to use system modeling as a performative device to let emerging cognitively a new entity, the structure of a complex problem. Regarding our proposal for modeling public policy problems, it can be said that the modeling strategies that fit better with the proposed principles are those that were identified as qualitative and oriented to public policy. ● Systemic modeling is acknowledged as a straightforward tool for representing and analyzing complex problems. ● Modeling approaches must have the ability to deal with complex problems, making them more understandable and approachable. ● We make recommendations for choosing the appropriate modeling strategy for complex public policy problems. ● We recommend models that are qualitative, participatory, and use cognitive map type of models. ● Qualitative models provide clearer visions of the structure of a system and address complexity from a plurality of visions. [ABSTRACT FROM AUTHOR]

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• Seven integration strategies were identified and grouped into three categories. • A detailed explanation, critical analysis and recommended usage of each strategy is given. • The compartmental model has the best balance between computation time and accuracy. • The fully coupled strategies are very accurate but too computationally expensive. Kinetics and CFD modeling is widely used for the study of biological treatment systems. This work reviews publications from 2006 to 2023 and compares the strategies applied to integrate the fluid dynamics and kinetics modeling. The identified strategies are grouped into three categories: a) the CFD coupled with the kinetics: mostly prohibitive in terms of its computational cost unless severe simplifications are imposed at the expense of the validity of the results; b) the CFD uncoupled from the kinetics: less computationally expensive, nonetheless, not suitable for real-time simulations. In this category, the time-step sub-cycling strategy is recommended for design modeling purposes; and c) the modeling with indirect coupling: using a CFD-based compartmental model, can get an accurate integration of the kinetics and fluid dynamics with the least computational cost. This strategy is a feasible option for real-time applications. [ABSTRACT FROM AUTHOR]