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In conclusion it can be said that a linear block code Cb (n, k) is able to detect 2n 2k error patterns and correct 2n k error patterns. It can also be said that For a linear block code Cb (n, k) with minimum distance dmin , all the vectors of weight t = dmin 1 or less can be used as coset leaders. This is in agreement with the fact that not 2 all the weight t + 1 error patterns can be corrected, even when some of them can be. On the other hand, all the vectors of the same coset have the same syndrome, whereas syndromes for different cosets are different. By taking a coset leader as the vector ei , any other vector of that coset is the sum of the leader vector and the code vector ci . For this case, the syndrome is calculated as (ci ei ) HT = ci HT ei H T = ei HT (49)

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World War II had an interesting effect on I/O. . . . It threw together a number of psychologists, some industrial, some experimental, some measurement, etc., in a variety of places. Wallace and Flanagan worked with some experimental types in the Army Air Corps (later the USAF), while McGehee and others were in the Navy. Wherry worked with the Army in D.C., and Shartle continued his work on job classi cation. . . . Experimental psychologists learned to respect what applied psychologists could

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with what is known as regular solution theory that interactions were not strongly coupled to mixture composition (see [115] for more details). With this he succeeded in predicting the behaviour of multicomponent systems using only binary data [26]. Following this, models such as the NRTL [116] and UNIQUAC [117] frameworks were developed, which are widely used in the chemical industry today [27]. The problem in the atmosphere is that predictive rather than correlative methods are required. As pointed out by Saxena and Hildemann [25], treating each compound as a molecule would not only be cumbersome but also require binary

positive malingering and, 203 204 predictive assessment and, 194 Chimpanzees, studies involving, 275 Chi-square designs, 116 factor analysis and, 159 homogeneity and, 545 546 logistic regression and, 517 SEM and, 610, 614, 615, 616, 617, 618, 622, 626 survival analysis and, 572 Circular plots, 419 CIRCUM routine, 354 355, 411 circumplex model, 412 Circumplex model: constructs, evaluating, 420 423 de ned, 408 evaluating data to t, 410 411 con rmatory methods, 411, 412 415 distribution tests, 415 exploratory methods, 411 412 examples, 408 410 groups and, 418 420 individuals and, 415 418 overview, 407 408 relational, 423 424 resources for, 424 theory testing, 423 CLARA, 182 CLARANS, 182 Classical test theory (CTT): assumptions, 429 430 development, test, 431 432 evaluation, test, 432 434 item properties, 430 431 overview, 434 score, meaning of, 430 true score model, 429 See also Item response theory (IRT) Classi cation, methods of. See Clustering Classi cation Literature Automated Search Service, 165 Classi cation Society of North America, 165, 182 Cleveland, William, 42 CLIQUE, 182 Clustering: algorithms, 170 174 data mining and, 181 182 data preparation for, 166 tree models and, 166 167 ultrametric and additive inequalities, 166 167 examples of, 167 170 overview, 165 166 in psychology and other elds, 181 software, 182 steps in, 174 data set, selecting, 174 175 number of clusters, selecting, 177 179 selection and weighting, variable, 175 176 standardization, variable, 176 177 validation of results, 179 hypothesis testing, 179 180 interpretation, 179 replication analysis, 180 181 Cocktail party effect, 300 Coding: dummy, 502 503 effect, 15

141 Aggregate Cultures 383 Protocol 142 Aggregate Cultures of PC12 Cells 383 142 Dynamic Formation of PC12 Organoids in RWV Bioreactors 384 Protocol 143 Generation of PC12 Organoids in HARVs Without Beads 385 Protocol 144 Generation of PC12 Organoids with Beads in STLVs 387 15 Conclusions 390 Formation of Sertoli-NT2N Tissue Constructs to Treat Neurodegenerative Disease 390 21 Background 390 211 Engineered Tissue Constructs for Transplantation 391 212 The NT2 Neuronal Precursor Cell 391 213 Sertoli Cells 392 22 Sertoli-NT2N-Aggregated-Cell (SNAC) Tissue Constructs 392 221 NT2 Cells Rapidly Differentiate into NT2N Neurons 392 222 Tissue Constructs Survive and Retain Their Dopaminergic Phenotype When Transplanted into the Brain 393 23 Conclusions 394 24 Preparation of Media and Reagents 394 241 Maintenance Medium (MM) 394 242.

Incubation Medium (IM) 395 243 NT2 Medium (NT2M) 395 244 Freezing Medium (FM) for NT2 Cells 395 245 Trypsin Solution (for Tissue Digestion) 395 246 Trypsin-EDTA Solution (for Lifting Plated Sertoli Cells and NT2 Cells) 395 247 Collagenase Solution 395 248 Tris Buffer 395 249 DNase Stock Solution 395 25 Isolation of Rat Sertoli Cells 395 Protocol 145 Preparation of Sertoli Cells 396 26 Preparation of NT2 Cells 397 Protocol 146 Propagating NT2 Cells 397 Protocol 147 Preparation of NT2 Cells for Coculturing 398 27 Preparation of Sertoli-NT2-Aggregated-Cell (SNAC) Tissue Constructs 398 Protocol 148 Tissue Constructs of Sertoli and NT2 Aggregated Cells (SNAC) 399 The Dorsal Root Ganglion as a Model System 399 31 Introduction 400 32 Preparation of Materials: Embryos, Reagents, and Dissection Tools 401.