Mitochondrial Network Configuration Influences Sarcomere and Myosin Filament Structure in Striated Muscles

Sustained muscle contraction occurs through interactions between actin and myosin filaments within sarcomeres and requires a constant supply of adenosine triphosphate (ATP) from nearby mitochondria. However, it remains unclear how different physical configurations between sarcomeres and mitochondria alter the energetic support for contractile function. Here, we show that sarcomere cross-sectional area (CSA) varies along its length in a cell type-dependent manner where the reduction in Z-disk CSA relative to the sarcomere center is closely coordinated with mitochondrial network configuration. Further, we find myosin filaments near the sarcomere periphery are curved relative to interior filaments with greater curvature for filaments near mitochondria compared to the sarcoplasmic reticulum. Finally, we demonstrate myosin filament lattice spacing is smaller at filament ends than filament centers in a cell type-dependent manner. These data suggest that both sarcomere structure and myofilament interactions are influenced by the location and orientation of mitochondria within a muscle cell.

A Network-Based View on Extreme Expedition Team Composition in Alpine Tourism

Conceptually drawing on network theory as its theoretical lens, this study examines two prime notions of network configuration of commercial expeditions. Exploring the role of both structural holes and network closure as indicators of team configuration for those venturing out in such extreme adventure, this study clarifies the impact of social structures, network closure, and structural holes in particular on performance outcomes in the context of expedition mountaineering. Presence and bridging of structural holes did turn out to be a significant predictor for the success or failure of an expedition. The findings show network closure to significantly influence the performance of mountaineering teams that make for a successful ascent. The capacity to span structural holes, commonly portrayed as serving as an eye-opener for options otherwise not found, does not appear to assist teams that make for successful ascents, however.

Liberalisation and the regional air network configuration from Nigeria to other West African Countries

This paper examines the liberalisation and the regional air network configuration from Nigeria to other West African regions. It aims to study the impacts of liberalisation on the regional spatial structure of air networks from Nigeria to West Africa in the pre and post-liberalisation. The pre-liberalisation covers between 1988–2000, and the post-liberalisation ranges from 2001 to 2018. The methodology involves using the graph theory to calculate the route and the network topology in the pre and post-liberalisation and compare the resulting index. This hypothesis was tested using the alpha index. The alpha index analysis compares the level of connection in a pre-and post-liberalisation network via graphical depictions of each period’s route and network structure and the resulting alpha index. The pre-liberalisation alpha index for the route and network was 0.297, while the post-liberalisation alpha index was 0.334. The alpha index ranged from 0 to 1 and was the perfect network for the post‐liberalisation period. In post-liberalisation, the alpha index of the route and network are higher than in pre-liberalisation. Hence, the connection is better in post-liberalisation.

Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.

Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction

In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17% . In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease.

Design Specification for Network Fault Diagnosis System

As the network becomes more and more complex and heterogeneous, the problem of network management becomes more and more prominent. The research and application of network management is of great significance for ensuring the normal operation of network and improving the reliability and availability of network system. The main problems of network management are network configuration, failure, performance, security, planning and scaling, etc. On the basis of in-depth research and discussion on network management and fault management, this paper designs the system requirements of network fault diagnosis system.

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications - This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing requirements. Originality/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.