Kontrol Sosial Tokoh Masyarakat (Ustad) dalam Mengatasi Penyimpangan Perilaku Remaja di Desa Limbung Kecamatan Sungai Raya Kubu Raya

Tujuan penelitian ini untuk mengambarkan kontrol sosial tokoh masyakat dalam mengendalikan kenakalan sosial remaja , dan untuk mengidentifikasi jenis kenakalan remaja serta mengetahui faktor penyebab kenakalan sosial remaja di Desa Limbung Kecamatan Sungai Raya Kubu Raya tahun 2012. Hasil penelitian penunjukkan jenis kenakalan remaja yang paling dominan dilakukan remaja adalah merokok, judi billiar dan pergaulan bebas. Dan penyebab kenakalan tersebut faktor diri sendiri, keluarga yang kurang harmonis, kurang komunikatif, kurang teladan dari kedua orang tua atau keluarga lainnya, tidak tegas dalam setiap penyimpangan dan faktor dari lingkungan pergaulan remaja serta Mass Media yang dapat di akses dimana saja. Keterlibatan ustaz dalam mengendalikan kenakalan tersebut dengan pendekatan preventif dengan memberikan penyuluhan, nasehat agama kepada remaja, warga baik secara langsung maupun tidak langsung dalam pengajian yang diselenggarakan setiap seminggu sekali atau kesempatan lainnya. Dalam pendekatan refresif dengan menegur, memberikan sangsi pada pelaku tidak dilaksanakan. Dalam penelitian juga ditemukan pendekatan kuratif berupa melakukan pembinaan yang terlibat dalam kenakalan sosial tidak pernah dilakukan oleh para ustazd. Kata Kunci : kenakalan, kontrol Sosia

Performance of modified non-linear shooting method for simulation of 2nd order two-point BVPS

In this research article, numerical solution of nonlinear 2nd order two-point boundary value problems (TPBVPs) is discussed by the help of nonlinear shooting method (NLSM), and through the modified nonlinear shooting method (MNLSM). In MNLSM, fourth order Runge-Kutta method for systems is replaced by Adams Bashforth Moulton method which is a predictor-corrector scheme. Results acquired numerically through NLSM and MNLSM of TPBVPs are discussed and analyzed. Results of the tested problems obtained numerically indicate that the performance of MNLSM is rapid and provided desirable results of TPBVPs, meanwhile MNLSM required less time to implement as comparable to the NLSM for the solution of TPBVPs

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Role of chondroitin sulfate proteoglycans (CSPGs) in synaptic plasticity and neurotransmission in mammalian spinal cord.

Chronic unilateral hemisection (HX) of the adult rat spinal cord diminishes conduction through intact fibers in the ventrolateral funiculus (VLF) contralateral to HX. Intraspinal injections of Chondroitinase-ABC, known to digest chondroitin sulfate proteoglycans (CSPGs) in the vicinity of injury, prevented this decline of axonal conduction. This was associated with improved locomotor function. We further injected three purified CSPGs into the lateral column of the uninjured cord at T10: NG2 and neurocan, which increase in the vicinity of a spinal injury, and aggrecan, which decreases. Intraspinal injection of NG2 acutely depressed axonal conduction through the injection region in a dose dependent manner. Similar injections of saline, aggrecan, or neurocan had no significant effect. These results identify a novel acute action of CSPGs on axonal conduction in spinal cord, and suggest that antagonism of proteoglycans reverses or prevents the decline of axonal conduction, in addition to stimulating axonal growth

High speed computing of ice thickness equation for ice sheet model

Two-dimensional (2-D) ice flow thermodynamics coupled model acts as a vital role for visualizing the ice sheet behaviours of the Antarctica region and the climate system. One of the parameters used in this model is ice thickness. Explicit method of finite difference method (FDM) is used to discretize the ice thickness equation. After that, the equation will be performed on Compute Unified Device Architecture (CUDA) programming by using Graphics Processing Unit (GPU) platform. Nowadays, the demand of GPU for solving the computational problem has been increasing due to the low price and high performance computation properties. This paper investigates the performance of GPU hardware supported by the CUDA parallel programming and capable to compute a large sparse complex system of the ice thickness equation of 2D ice flow thermodynamics model using multiple cores simultaneously and efficiently. The parallel performance evaluation (PPE) is evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Bio-polishing sludge adsorbents for dye removal

The objective of this work is to evaluate the removal of methylene blue dye by bio-polishing sludge-based adsorbents. The adsorbents were characterized according to the specific surface area, pH upon the treatment and surface functional groups. The adsorption of dye was carried out at room temperature, and the adsorption data were analyzed using the isotherm and kinetics models. The bio-polishing sludge is rich in ash content, and the presence of surface functional groups varied with the treatment strategies. The specific surface area of adsorbents is between 7.25 and 20.8 m2/g. Results show that the maximum removal of methylene blue by sludge adsorbents was observed to have the following order: untreated sludge (SR) > zinc chloride-treated (SZ) > microwave-dried (SW) = potassium carbonate-treated (SK) > acid-washed (SH). The maximum adsorption capacities for SR and SZ as predicted by the Langmuir model are 170 and 135 mg/g, respectively. Although SR demonstrates a higher maximum removal than SZ, the latter exhibits greater removal intensity and rate constant even at high dye concentration. The bio-polishing sludge is a promising adsorbent for dye wastewater treatment

On Multiphase-Linear Ranking Functions

Multiphase ranking functions ($\mathit{M{\Phi}RFs}$) were proposed as a means to prove the termination of a loop in which the computation progresses through a number of "phases", and the progress of each phase is described by a different linear ranking function. Our work provides new insights regarding such functions for loops described by a conjunction of linear constraints (single-path loops). We provide a complete polynomial-time solution to the problem of existence and of synthesis of $\mathit{M{\Phi}RF}$ of bounded depth (number of phases), when variables range over rational or real numbers; a complete solution for the (harder) case that variables are integer, with a matching lower-bound proof, showing that the problem is coNP-complete; and a new theorem which bounds the number of iterations for loops with $\mathit{M{\Phi}RFs}$. Surprisingly, the bound is linear, even when the variables involved change in non-linear way. We also consider a type of lexicographic ranking functions, $\mathit{LLRFs}$, more expressive than types of lexicographic functions for which complete solutions have been given so far. We prove that for the above type of loops, lexicographic functions can be reduced to $\mathit{M{\Phi}RFs}$, and thus the questions of complexity of detection and synthesis, and of resulting iteration bounds, are also answered for this class.Comment: typos correcte

Space-times which are asymptotic to certain Friedman-Robertson-Walker space-times at timelike infinity

We define space-times which are asymptotic to radiation dominant Friedman-Robertson-Walker space-times at timelike infinity and study the asymptotic structure. We discuss the local asymptotic symmetry and give a definition of the total energy from the electric part of the Weyl tensor.Comment: 8 pages, Revte