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"The low-cost Wireless Sensor Network (WSN) consists of small battery powered devices called sensors, with limited energy capacity. Once deployed, accessibility to any sensor node for maintenance and battery replacement is not feasible due to the spatial scattering of the nodes. This will lead to an unreliable, limited lifetime and a poor connectivity network. In this paper a novel bio-inspired cluster-based deployment algorithm is proposed for energy optimization of the WSN and ultimately to improve the network lifetime. In the cluster initialization phase, a single cluster is formed with a single cluster head at the center of the sensing terrain. The second phase is for optimum cluster formation surrounding the inner cluster, based on swarming bees and a piping technique. Each cluster member distributes its data to its corresponding cluster head and the cluster head communicates with the base station, which reduces the communication distance of each node. The simulation results show that, when compared with other clustering algorithms, the proposed algorithm can significantly reduce the number of clusters by 38% and improve the network lifetime by a factor of 1/4."

"The low-cost Wireless Sensor Network (WSN) consists of small battery powered devices called sensors, with limited energy capacity. Once deployed, accessibility to any sensor node for maintenance and battery replacement is not feasible due to the spatial scattering of the nodes. This will lead to an unreliable, limited lifetime and a poor connectivity network. In this paper a novel bio-inspired cluster-based deployment algorithm is proposed for energy optimization of the WSN and ultimately to improve the network lifetime. In the cluster initialization phase, a single cluster is formed with a single cluster head at the center of the sensing terrain. The second phase is for optimum cluster formation surrounding the inner cluster, based on swarming bees and a piping technique. Each cluster member distributes its data to its corresponding cluster head and the cluster head communicates with the base station, which reduces the communication distance of each node. The simulation results show that, when compared with other clustering algorithms, the proposed algorithm can significantly reduce the number of clusters by 38% and improve the network lifetime by a factor of 1/4."

"Tujuan penelitian ini adalah memahami seberapa efektifkah strategi Far Enemy yang diterapkan Al Qaeda dan Jaringannya melawan Amerika Serikat, NATO dan Sekutunya dalam Perang Asimetrik di Afghanistan dan Irak antara 2001-2011. Juga ingin menjelaskan fungsi strategi Perang Gerilyawan Kota dan taktik Swarming dalam penerapan strategi Far Enemy tersebut. Akhirnya, akan dibuktikan apakah dalam terorisme abad modern, kuatnya jaringan kelompok sempalan dan sel teroris, yang didukung penguasaan teknologi informasi, adalah syarat mutlak dalam menerapkan strategi Far Enemy. Tesis ini memakai metode kwalitatif dengan pembahasan persoalan secara deskriptif analisis. Dilakukan dengan meneliti studi kepustakaan, menganalisa bacaan dalam pengumpulan data dan membandingkan pendapat pro dan kontra dengan hipotesa yang dipilih. Keberhasilan Al Qaeda menerapkan dengan jitu teori perang Sun Tzu, yakni memperlemah kekuatan ekonomi lawan dengan memperpanjang perang, bersumber pada penguasaan terhadap budaya politik Afghanistan, dan sokongan kuat kelompok pemberontak Taliban dan suku Pashtun berdasarkan Pashtunwali Code (Hukum Pashtunwali).

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The focus of this study is to understand the effectiveness of the Far Enemy strategy to Al Qaeda and its network in the asimetric war against United States, NATO and allies in Afghanistan and Irak, 2001-2011. It will also explain the choice of Urban Guerrilla Warfare and its Swarming tactic by Al Qaeda. In the end, we can come to the assumption of the importance of information technology and political culture dominance by a strong network to apply the Far Enemy strategy in modern terrorism.

This research used qualitative methods with analytical descriptive analysis. Referring to the Art of War theory by Sun Tzu, Al Qaeda has shown its capability of prolonging the war in Afghanistan and Irak, to weaken its opponents economy and human resources. Such dominance over the conflict owes much to the Pashtunwali Code, which is honored by the Taliban and Pashtun tribe."

"Masalah penyusunan barang dalam kontainer atau yang dikenal sebagai Container Loading Problem (CLP) adalah salah satu tantangan utama dalam industri logistik. Penelitian ini berfokus pada penggunaan algoritma Cat Swarming Optimization (CSO) untuk mengoptimalkan penyusunan barang dengan tujuan memaksimalkan penggunaan ruang dalam kontainer, mengurangi ruang kosong, dan menjaga stabilitas muatan. Algoritma CSO didasarkan pada perilaku kucing dalam dua mode utama, yaitu Seeking Mode dan Tracing Mode, yang secara kolaboratif mencari solusi terbaik untuk menyusun barang dalam kontainer dengan berbagai ukuran dan dimensi. Penelitian ini menerapkan model optimasi berbasis CSO untuk mencari solusi optimal dari berbagai kombinasi penyusunan barang dengan memanfaatkan fleksibilitas rotasi barang dan pengaturan posisi berdasarkan berat dan volume. Efektivitas algoritma ini diuji dengan menggunakan data sampel berupa barang berbentuk persegi panjang yang disusun di dalam kontainer dengan dimensi tertentu. Hasil penelitian menunjukkan bahwa algoritma CSO mampu meningkatkan efisiensi penggunaan ruang kontainer dengan meminimalkan ruang kosong dan memastikan stabilitas tumpukan barang. Dengan menggunakan visualisasi tiga dimensi (3D) yang interaktif, sistem ini juga memungkinkan pengguna untuk memantau dan menyesuaikan susunan barang secara real-time, memberikan solusi penyusunan yang aman, efisien, dan siap digunakan dalam skenario logistik nyata. Penelitian ini diharapkan dapat memberikan kontribusi yang signifikan dalam meningkatkan efisiensi operasional di sektor logistik dan transportasi.

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The Container Loading Problem (CLP) is one of the main challenges in the logistics industry. This research focuses on the use of the Cat Swarming Optimization (CSO) algorithm to optimize item arrangement with the goal of maximizing container space utilization, reducing empty space, and maintaining load stability. The CSO algorithm is based on the behavior of cats in two main modes, namely Seeking Mode and Tracing Mode, which collaboratively search for the best solution to arrange items of various sizes and dimensions within a container. This study applies a CSO-based optimization model to find optimal solutions for various item arrangement combinations by utilizing the flexibility of item rotation and position adjustments based on weight and volume. The effectiveness of this algorithm is tested using sample data of rectangular items arranged in a container with specific dimensions. The results demonstrate that the CSO algorithm is able to improve container space efficiency by minimizing empty space and ensuring the stability of stacked items. By using interactive three-dimensional (3D) visualization, this system also allows users to monitor and adjust item arrangements in real-time, providing safe, efficient, and ready-to-use solutions for real-world logistics scenarios. This research is expected to contribute significantly to enhancing operational efficiency in the logistics and transportation sectors.

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