Skip to main content

Network Analysis

Network analysis with quantum dynamics clarifies why photosystem II exploits both chlorophyll a and b


In green plants, chlorophyll a and chlorophyll b are the predominant pigments bound to light-harvesting proteins. While the individual characteristics of these chlorophylls are well understood, the advantages of their coexistence remain unclear. In this study, we establish a method to simulate excitation energy transfer within the entire photosystem II supercomplex by using network analysis integrated with quantum dynamic calculations.

We then investigate the effects of the coexistence of chlorophyll a and chlorophyll b by comparing various chlorophyll compositions. Our results reveal that the natural chlorophyll composition allows the excited energy to preferentially flow through specific domains that act as safety valves, preventing downstream overflow.

Our findings suggest that the light-harvesting proteins in a photosystem II supercomplex achieve evolutionary advantages with the natural chlorophyll a/b ratio, capturing light energy efficiently and safely across various light intensities. Using our framework, one can better understand how green plants harvest light energy and adapt to changing environmental conditions.

network analysis, graph theory, centrality measures, node degree, shortest path, connectivity, data science, social networks, computer networks, community detection, clustering, visualization, cybersecurity, traffic flow, IoT, supply chain, biological networks, machine learning, big data, real-time monitoring

#NetworkAnalysis, #GraphTheory, #CentralityMeasures, #NodeDegree, #ShortestPath, #ConnectivityStudy, #DataScienceTools, #SocialNetworkAnalysis, #ComputerNetworkAnalysis, #CommunityDetectionTools, #ClusteringAlgorithms, #VisualizationMethods, #CybersecurityTools, #TrafficFlowAnalysis, #IoTAnalytics, #SupplyChainOptimization, #BioNetworkAnalysis, #MachineLearningNetwork, #BigDataNetwork, #RealTimeNetwork

Comments

Post a Comment

Popular posts from this blog

HealthAIoT: Revolutionizing Smart Healthcare! HealthAIoT combines Artificial Intelligence and the Internet of Things to transform healthcare through real-time monitoring, predictive analytics, and personalized treatment. It enables smarter diagnostics, remote patient care, and proactive health management, enhancing efficiency and outcomes while reducing costs. HealthAIoT is the future of connected, intelligent, and patient-centric healthcare systems. What is HealthAIoT? HealthAIoT is the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in the healthcare industry. It integrates smart devices, sensors, and wearables with AI-powered software to monitor, diagnose, and manage health conditions in real-time. This fusion is enabling a new era of smart, connected, and intelligent healthcare systems . Key Components IoT Devices in Healthcare Wearables (e.g., smartwatches, fitness trackers) Medical devices (e.g., glucose monitors, heart rate sensors) Rem...
Post a Comment
Read more
Detecting Co-Resident Attacks in 5G Clouds! Detecting co-resident attacks in 5G clouds involves identifying malicious activities where attackers share physical cloud resources with victims to steal data or disrupt services. Techniques like machine learning, behavioral analysis, and resource monitoring help detect unusual patterns, ensuring stronger security and privacy in 5G cloud environments. Detecting Co-Resident Attacks in 5G Clouds In a 5G cloud environment, many different users (including businesses and individuals) share the same physical infrastructure through virtualization technologies like Virtual Machines (VMs) and containers. Co-resident attacks occur when a malicious user manages to place their VM or container on the same physical server as a target. Once co-residency is achieved, attackers can exploit shared resources like CPU caches, memory buses, or network interfaces to gather sensitive information or launch denial-of-service (DoS) attacks. Why are Co-Resident Attack...
Post a Comment
Read more
                        Neural Networks Neural networks are computing systems inspired by the human brain, consisting of layers of interconnected nodes (neurons). They process data by learning patterns from input, enabling tasks like image recognition, language translation, and decision-making. Neural networks power many AI applications by adjusting internal weights through training with large datasets.                                                    Structure of a Neural Network Input Layer : This is where the network receives data. Each neuron in this layer represents a feature in the dataset (e.g., pixels in an image or values in a spreadsheet). Hidden Layers : These layers sit between the input and output layers. They perform calculations and learn patterns. The more hidden layers a ne...
Post a Comment
Read more