Weedy plants of Ayer Hitam Forest Reserve, Selangor

The Ayer Hitam Forest Reserve has possibly been under constant threat from invasive species especially the weeds from surrounding development sites. The total number of weed taxa identified from this forest were 33 species, 29 genera and J3 families. Of these taxa, only two species aquatic weeds vizs., Ceratopteris thalictroides (Parkeriaceae) and Hydrilla venieillata (Hydrocharitaceae). The most dominant terrestrial weedy speeies in this forest were Clidemia hirta (Melastomataceae) and Chromalaena odorata (Compositae). All grasses and sedges !lIJi~si,~s were recorded at the disturbed sampling sites only. Limosa pigra was only found at the most disturbed site bordering the forest

Design and fabricate the cleaning cum cooling system for downdraft gasifier / Muhammad Asyraf Mansor

Cleaning cum cooling system is a mechanism to filter the unwanted particle produce by biomass gasification along with producer gas from downdraft gasifier. This cleaning gas device is the third stage after downdraft gasifier and heat exchanger before extracted to the various application. The objectives of this project are to design the cleaning cum cooling system by using Solidworks 2016 software, to fabricate the mechanism by lab-scaled dimensions and to cool and filter the producer gas from unwanted particles. A few researches regarding the gas cleaning system have been study by look into the nature of the unwanted particle in producer gas. By studying all of these properties, a new design or stage of gas cleaning system have construct under a few considerations. This stage of mechanism is compulsory to avoid the problem when used the producer gas in a various application. All three stage of filter body have the similar dimension starting from water scrubber, tar absorber and silica gel which are 110mm diameter and 400mm height. The body for all stages of filter body and connecter have been fabricate using PVC pipe because it easy to fabricate and craft.. The lowest temperature out after past trough cleaning cum cooling system was 30.9℃ which near to the ambient temperature. Many dust and concentrated tar in producer gas have been filtered in this system. Syngas have been produce nicely and presence of blue flame appear. The expected results have been obtained

Analisis Kemampuan Pemahaman dan Self Esteem Matematis melalui Pembelajaran E-Learning Berbasis Portal Rumah Belajar

Belajar matematika memerlukan kecakapan untuk berpikir dan beralasan secara sistematis untuk menyelesaikan soal-soal baru dan mempelajari ide-ide baru yang akan dihadapi oleh peserta didik dimasa yang akan datang. Tujuan penelitian ini adalah untuk menganalisis peningkatan kemampuan pemahaman dan self-esteem matematis siswa dengan sampel secara purposif sampel tiga kelas VIII siswa SMP Bina Mandiri Sukamanah Kab. Garut tahun ajaran 2020/2021. Metode yang digunakan adalah metode campuran tipe konvergen. Instrumen tes yang digunakan adalah pre-test dan post-test sedangkan untuk instrumen non tes adalah lembar observasi dan angket. Data dianalisis menggunakan uji N-gain, uji Kruskal Wallis H dan uji ANOVA dua jalur. Hasil penelitian adalah: (1) peningkatan kemampuan pemahaman matematis siswa yang menggunakan e-learning portal rumah belajar lebih baik daripada siswa yang menggunakan model PBL dan metode ceramah dilihat dari data keseluruhan dan KAM; (2) tidak terdapat interaksi antara pembelajaran yang menggunakan e-learning portal rumah belajar, model PBL dan metode ceramah dengan KAM terhadap peningkatan kemampuan pemahaman matematis siswa; (3) self-esteem matematis siswa yang menggunakan pembelajaran e-learning portal rumah belajar lebih baik daripada yang menggunakan model PBL dan metode ceramah dilihat dari keseluruhan dan KAM

Analisis Kemampuan Pemahaman dan Self Esteem Matematis melalui Pembelajaran E-Learning Berbasis Portal Rumah Belajar

Belajar matematika memerlukan kecakapan untuk berpikir dan beralasan secara sistematis untuk menyelesaikan soal-soal baru dan mempelajari ide-ide baru yang akan dihadapi oleh peserta didik dimasa yang akan datang. Tujuan penelitian ini adalah untuk menganalisis peningkatan kemampuan pemahaman dan self-esteem matematis siswa dengan sampel secara purposif sampel tiga kelas VIII siswa SMP Bina Mandiri Sukamanah Kab. Garut tahun ajaran 2020/2021. Metode yang digunakan adalah metode campuran tipe konvergen. Instrumen tes yang digunakan adalah pre-test dan post-test sedangkan untuk instrumen non tes adalah lembar observasi dan angket. Data dianalisis menggunakan uji N-gain, uji Kruskal Wallis H dan uji ANOVA dua jalur. Hasil penelitian adalah: (1) peningkatan kemampuan pemahaman matematis siswa yang menggunakan e-learning portal rumah belajar lebih baik daripada siswa yang menggunakan model PBL dan metode ceramah dilihat dari data keseluruhan dan KAM; (2) tidak terdapat interaksi antara pembelajaran yang menggunakan e-learning portal rumah belajar, model PBL dan metode ceramah dengan KAM terhadap peningkatan kemampuan pemahaman matematis siswa; (3) self-esteem matematis siswa yang menggunakan pembelajaran e-learning portal rumah belajar lebih baik daripada yang menggunakan model PBL dan metode ceramah dilihat dari keseluruhan dan KAM

Enhanced Hopfield Neural Networks With Artificial Immune System Algorithm For Satisfiability Logic Programming

The emergence of 3-Satisfiability (3-SAT) problem has produced a prolific number of works devoted to the field of logic and data mining. In this study, a new hybrid method in doing logic programming by incorporating 3-SAT logical rules as a computational tool will be presented. Hence, a robust intelligence system that integrates the Hopfield neural network and metaheuristic paradigm is constructed to extract the data set hidden knowledge in the form of 3-Satisfiability logical rule. A hybrid network called HNN-3SATAIS is proposed by assimilating the Hopfield neural network with the enhanced artificial immune system (AIS) algorithm as a training tool in doing 3-Satisfiability logic programming

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Artificial Immune System (AIS) algorithm is a novel and vibrant computational paradigm, enthused by the biological immune system. Over the last few years, the artificial immune system has been sprouting to solve numerous computational and combinatorial optimization problems. In this paper, we introduce the restricted MAX-kSAT as a constraint optimization problem that can be solved by a robust computational technique. Hence, we will implement the artificial immune system algorithm incorporated with the Hopfield neural network to solve the restricted MAX-kSAT problem. The proposed paradigm will be compared with the traditional method, Brute force search algorithm integrated with Hopfield neural network. The results demonstrate that the artificial immune system integrated with Hopfield network outperforms the conventional Hopfield network in solving restricted MAX-kSAT. All in all, the result has provided a concrete evidence of the effectiveness of our proposed paradigm to be applied in other constraint optimization problem. The work presented here has many profound implications for future studies to counter the variety of satisfiability problem

Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem

Maximum 2-satisfiability in radial basis function neural network

Maximum k-Satisfiability (MAX-kSAT) is the logic to determine the maximum number of satisfied clauses. Correctly, this logic plays a prominent role in numerous applications as a combinatorial optimization logic. MAX2SAT is a case of MAX-kSAT and is written in Conjunctive Normal Form (CNF) with two variables in each clause. This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). Hence, we restrict the analysis to MAX2SAT clauses. We utilize Dev C++ as the platform of training and testing our proposed algorithm. In this study, the effectiveness of RBFNN-MAX2SAT can be estimated by evaluating the proposed models with testing data sets. The results obtained are analysed using the ratio of satisfied clause (RSC), the root means square error (RMSE), and CPU time. The simulated results suggest that the proposed algorithm is effective in doing MAX2SAT logic programming by analysing the performance by obtaining lower Root Mean Square Error, high ratio of satisfied clauses and lesser CPU time