PPT Akad Murabahah

Rizky Khairunnisa C1C020125, 2023

kad Murabahah

Nuzulma Jusyafitri, 2021

Tugas 9

Nuzulma Jusyafitri, 2021

Tugas 7

Indah Nelianti , 2021

Rizky Khairunnisa C1C020125, 2023

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Fitri Handari, 2021

Semoga Bermanfaat 🙏

Academia Biology, 2024

Mathematical black box models, which hide the structure and behavior of the subsystems, currently dominate science. Mechanisms under study remain hidden. Errors and paradoxes, such as the biodiversity paradox and the limiting similarity hypothesis, often arise from subjective interpretations of these hidden mechanisms. To address these problems in ecology, we have developed transparent mathematical models of the white box type. Here we present and justify the hypothesis that it is possible to construct transparent mathematical white-box models using logical deterministic cellular automata, where the rules used to construct these models are based on the general theory of the relevant domain. So far, white box modeling has allowed us to directly identify the mechanisms of interspecific competition, to test the principle of competitive exclusion and the hypothesis of limiting similarity, to resolve the paradox of biodiversity, and to formulate for the first time the general principle of competitive coexistence. As a framework for reproducing and further developing the method, we present C++ code of two transparent mathematical models of an ecosystem. A shift to transparency in the mathematical modeling paradigm has the potential to revolutionize scientific research and to advance knowledge and technology in a wide variety of domains.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024

The Knapsack problem is a combinatorial optimization problem whose exact solution using exhaustive search method is impractical. Hence, the application of approximate algorithms is usually considered when encountering this optimization problem. This study optimized some approximate algorithms: greedy, dynamic programming, and branch-and-bound for the Knapsack problem with specific objectives of evaluating their time and program complexity, comparing efficiencies, and enhancing performance. Our methodology involved utilizing advanced Parallelization techniques to accelerate the implementation of loop-based optimization algorithms by distributing tasks across multiple processing units concurrently. This simultaneous execution minimized computational time, enhanced overall efficiency, and improved scalability, enabling effective resolution of large-scale optimization challenges. Additionally, coefficients for the Knapsack model were generated using a random number generation algorithm. Through analysis and experimental runs using Halstead metrics and time complexity measures, significant improvements in the enhanced algorithms compared to classical approaches were revealed, particularly in terms of program complexity and computational speed. Notably, the enhanced algorithms demonstrated superior time complexity across varying input sizes, indicating their potential as more efficient solutions for the Knapsack Problem. This research contributes to advancing Theoretical Computer Science by offering a new computational approach for tackling intricate knapsack-model-based problems, thereby expanding the toolkit for addressing real world challenges across diverse application areas.