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Electron Transport Chains01:28

Electron Transport Chains

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The final stage of cellular respiration is oxidative phosphorylation that consists of two steps: the electron transport chain and chemiosmosis. The electron transport chain is a set of proteins found in the inner mitochondrial membrane in eukaryotic cells. Its primary function is to establish a proton gradient that can be used during chemiosmosis to produce ATP and generate electron carriers, such as NAD+ and FAD, that are used in glycolysis and the citric acid cycle.
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The electron transport chain or oxidative phosphorylation is an exothermic process in which free energy released during electron transfer reactions is coupled to ATP synthesis. This process is a significant source of energy in aerobic cells, and therefore inhibitors of the electron transport chain can be detrimental to the cell's metabolic processes.
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Electron Transport Chain: Complex I and II01:46

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The mitochondrial electron transport chain (ETC) is the main energy generation system in the eukaryotic cells. However, mitochondria also produce cytotoxic reactive oxygen species (ROS) due to the large electron flow during oxidative phosphorylation. While Complex I is one of the primary sources of superoxide radicals, ROS production by Complex II is uncommon and may only be observed in cancer cells with mutated complexes.
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Electron Transport Chain Components01:29

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The electron transport chain (ETC) is a crucial metabolic pathway that facilitates energy conversion in prokaryotic and eukaryotic cells. In eukaryotes, the ETC comprises four membrane-associated protein complexes in the inner mitochondrial membrane. In prokaryotes, the ETC in the plasma membrane can vary in composition, with fewer or different complexes depending on the organism and environmental conditions. These complexes transfer electrons from electron donors, such as NADH and FADH2, to...
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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Electron Transport Chain: Complex III and IV01:43

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During the electron transport chain, electrons from NADH and FADH2 are first transferred to complexes I and II, respectively. These two complexes then transfer the electrons to ubiquinol, which carries them further to complex III. Complex III passes the electrons across the intermembrane space to Cyt c, which carries them further to complex IV. Complex IV donates electrons to oxygen and reduces it to water. As electrons pass through complexes I, III, and IV, the energy released aids the pumping...
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Hybrid Clear/Blue Native Electrophoresis for the Separation and Analysis of Mitochondrial Respiratory Chain Supercomplexes
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A Hybrid Optimization Algorithm for Enhancing Transportation and Logistics Scheduling in IoT-Enabled Supply Chains.

Alaa Abdalqahar Jihad1, Ahmed Subhi Abdalkafor2, Esam Taha Yassen2

  • 1Computer Center, University of Anbar, Ramadi 31001, Iraq.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

A new Bidirectional PRS-SA Optimization strategy significantly improves IoT-integrated supply chain logistics. This approach enhances real-time decision-making, outperforming existing methods by 15-25% for more efficient transportation and distribution management.

Keywords:
IoT-enabled supply chainhybrid optimization algorithmoperational efficiencyprism refraction search (PRS)transportation and logistics scheduling

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Area of Science:

  • Supply Chain Management
  • Operations Research
  • Internet of Things (IoT)

Background:

  • IoT-enabled supply chains are crucial for real-time data processing and informed decision-making to reduce logistical costs.
  • Optimizing transportation and logistics scheduling remains challenging due to the need to balance demand, vehicle capacity, and delivery times.

Purpose of the Study:

  • To assess the performance of optimization algorithms (DE, GA, SA, PRS) in IoT-integrated logistics.
  • To introduce a novel combined optimization strategy, Bidirectional PRS-SA (Bi-PRS-SA), for enhanced logistics scheduling.
  • To propose a conceptual framework for integrating Bi-PRS-SA into IoT for dynamic supply chain management.

Main Methods:

  • Evaluated four optimization algorithms: Differential Evolution (DE), Genetic Algorithm (GA), Simulated Annealing (SA), and Prism Refraction Search (PRS).
  • Developed a hybrid Bidirectional PRS-SA (Bi-PRS-SA) optimization approach, combining global and local search capabilities.
  • Proposed a framework for dynamic supply chain management within an IoT ecosystem.

Main Results:

  • The Bi-PRS-SA strategy demonstrated superior performance compared to DE, GA, SA, and PRS, achieving improvements of 15-25%.
  • Statistical validation using the Wilcoxon signed-rank test confirmed the significance of the improvements (p < 0.05).

Conclusions:

  • The Bi-PRS-SA framework provides a robust and scalable solution for real-time logistics management in IoT environments.
  • This hybrid approach effectively balances global and local search, leading to significant efficiency gains in supply chain operations.