scholarly journals An EPTAS for Scheduling Jobs on Uniform Processors: Using an MILP Relaxation with a Constant Number of Integral Variables

2010 ◽  
Vol 24 (2) ◽  
pp. 457-485 ◽  
Author(s):  
Klaus Jansen
Keyword(s):  
Constant Number ◽  
Uniform Processors

PENGARUH SARANA BELAJAR DAN MOTIVASI BELAJAR TERHADAP KEMANDIRIAN BELAJAR MAHASISWA (Studi Empirical Pada Mahasiswa Beasiswa Bidikmisi UPBJJ-UT Ternate)

2018 ◽  
Vol 19 (2) ◽  
pp. 68
Author(s):  
Raden Sudarwo ◽  
Yusuf Yusuf ◽  
Anfas Anfas
Keyword(s):  
Student Learning ◽  
Significant Influence ◽  
Sense Of Self ◽  
Regression Equation ◽  
Learning Motivation ◽  
Coefficient Of Determination ◽  
Learning Tools ◽  
Constant Number ◽  
Self Sufficiency ◽  
Student Independence

This study aims to determine the influence of learning facilities and student learning motivation towards the independence of student learning. The result of the research shows that there is positive and significant influence of learning tool (X1) on learning independence (Y). It is obtained by tvalue (2,159) with p = 0,034 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning motivation (X2) on learning independence (Y). It is obtained tvalue (7,858) with p = 0,000 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning facilities (X1) and learning motivation (X2) simultaneously to the independence of learning (Y). This shows the coefficient of double correlation RY (1,2) = 0,746 and R² = 0,557 and price Fvalue equal to 48,980 with p = 0,000 <0,05 and Ftable = 3,11 at 5% significant level. Coefficient value X1 = 0,186 and X2 = 0,647, constant number equal to 8,650 so that can be made regression equation Y = 8,650 + 0,186X1 + 0,647X2. The higher the learning means (X1) and the learning motivation (X2), the higher the learning independence (Y). Coefficient of Determination is R² of 0,557. Means 55,7% learning independence is explained by learning tools and learning motivation. Meanwhile, 44,3% is explained by other factors not discussed in this study. The study concludes that partially, learning facilities and student learning motivation has a positive and significant effect on student independence (self-sufficiency) in learning.  In addition, both learning facility and motivation have a positive and significant effect on student learning independence or sense of self-sufficiency. Penelitian ini bertujuan untuk mengetahui pengaruh fasilitas belajar dan motivasi belajar siswa terhadap kemandirian belajar siswa. Hasil penelitian menunjukkan bahwa ada pengaruh yang positif dan signifikan sanara belajar (X1) terhadap kemandirian belajar (Y). Hal ini diperoleh dengan nilai thitung (2,159) dengan p = 0,034 <0,05 dan ttabel pada 5% tingkat signifikan dengan df = 78 sama dengan 1,991. Ada pengaruh positif dan signifikan motivasi belajar (X2) pada kemandirian belajar (Y). Diperoleh nilai thitung (7,858) dengan p = 0,000 <0,05 dan ttabel pada taraf signifikan 5% dengan df = 78 sebesar 1,991. Ada pengaruh yang positif dan signifikan dari fasilitas belajar (X1) dan motivasi belajar (X2) secara bersamaan terhadap kemandirian belajar (Y). Hal ini menunjukkan koefisien korelasi ganda RY (1,2) = 0,746 dan R² = 0,557 dan harga Fhitung sebesar 48,980 dengan p = 0,000 <0,05 dan Ftabel = 3,11 pada taraf signifikan 5%. Nilai koefisien X1 = 0,186 dan X2 = 0,647, bilangan konstan sebesar 8,650 sehingga dapat dibuat persamaan regresi Y = 8,650 + 0,186X1 + 0,647X2. Semakin tinggi nilai sarana belajar (X1) dan motivasi belajar (X2), semakin tinggi kemandirian belajar (Y). Koefisien Determinasi adalah R² 0,557. Berarti 55,7% kemandirian belajar dijelaskan oleh alat belajar dan motivasi belajar. Sementara itu, 44,3% dijelaskan oleh faktor-faktor lain yang tidak dibahas dalam penelitian ini. Penelitian ini menyimpulkan bahwa secara parsial, baik ketersediaan sarana prasaran belajar dan motivasi berpengaruh positif dan signifikan pada kemandirian mahasiswa, dari dari kedua variable tersebut motivasi mempunyai pengaruh lebih besar. Secara simultan ketersediaan sarana prasarana dalam belajar dan pembelajaran, serta motivasi berpengaruh positif terhadap kemandirian belajar.

Clique Here: On the Distributed Complexity in Fully-Connected Networks

2016 ◽  
Vol 26 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Benny Applebaum ◽  
Dariusz R. Kowalski ◽  
Boaz Patt-Shamir ◽  
Adi Rosén
Keyword(s):  
Boolean Function ◽  
Boolean Functions ◽  
Message Passing ◽  
Constant Number ◽  
Running Time ◽  
Message Size ◽  
Explicit Function ◽  
Randomized Protocols ◽  
Fully Connected ◽  
Fully Connected Networks

We consider a message passing model with n nodes, each connected to all other nodes by a link that can deliver a message of B bits in a time unit (typically, B = O(log n)). We assume that each node has an input of size L bits (typically, L = O(n log n)) and the nodes cooperate in order to compute some function (i.e., perform a distributed task). We are interested in the number of rounds required to compute the function. We give two results regarding this model. First, we show that most boolean functions require ‸ L/B ‹ − 1 rounds to compute deterministically, and that even if we consider randomized protocols that are allowed to err, the expected running time remains [Formula: see text] for most boolean function. Second, trying to find explicit functions that require superconstant time, we consider the pointer chasing problem. In this problem, each node i is given an array Ai of length n whose entries are in [n], and the task is to find, for any [Formula: see text], the value of [Formula: see text]. We give a deterministic O(log n/ log log n) round protocol for this function using message size B = O(log n), a slight but non-trivial improvement over the O(log n) bound provided by standard “pointer doubling.” The question of an explicit function (or functionality) that requires super constant number of rounds in this setting remains, however, open.

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