Automatically calculating periodic timetables in public railway transport systems is an NP-complete problem – namely the Periodic Event Scheduling Problem (PESP). The original model is restricted to basic periodic timetabling. Extending the model by decisional transport networks with flows induces new possibilities in the timetabling and planning process. Subsequently, the given flexibility results in a generic model extension of PESP that can be applied in subsets of the timetabling process. The successful utilization of this approach is presented for distinct chain paths, duplicated chain paths and non-connected flow graphs that represent integration of routing and timetabling, planning of periodic rail freight train paths and track allocation, respectively. Furthermore, the encoding of this generic model into a binary propositional formula is introduced and the appropriate usage of several techniques like SAT solving and MaxSAT to calculate and optimize the corresponding instances will be presented accordingly. Computational results for real-world scenarios suggest the practical impact and give promising perspectives for further scientific research.

Automatically calculating periodic timetables in public railway transport systems is an NP-complete problem – namely the Periodic Event Scheduling Problem (PESP). The original model is restricted to basic periodic timetabling. Extending the model by decisional transport networks with flows induces new possibilities in the timetabling and planning process. Subsequently, the given flexibility results in a generic model extension of PESP that can be applied in subsets of the timetabling process. The successful utilization of this approach is presented for distinct chain paths, duplicated chain paths and non-connected flow graphs that represent integration of routing and timetabling, planning of periodic rail freight train paths and track allocation, respectively. Furthermore, the encoding of this generic model into a binary propositional formula is introduced and the appropriate usage of several techniques like SAT solving and MaxSAT to calculate and optimize the corresponding instances will be presented accordingly. Computational results for real-world scenarios suggest the practical impact and give promising perspectives for further scientific research.

This study compared seven syntactic measures which can be automatically generated by the Computerized Profiling (CP) software: Mean Length of Utterance in morphemes or words (MLUm or MLUw), Mean Syntactic Length (MSL), the Index of Productive Syntax (IPSyn), the Picture Elicited Scoring Procedure (PESP) for the Language Analysis Remediation and Screening Profile (LARSP), the Syntactic Complexity Score (MSC) scoring of LARSP, and Developmental Sentence Scoring (DSS). Language samples came from 192 children, 106 typically developing children, ages 5;6 to 11;2 and 86 children with language impairment, ages 5;6 to 11;1. Patterns of correlation were consistent for children with or without language impairment. All measures were computed with CP software, and all coding decisions that were made by the software were accepted. The three measures of length (MLUm, MLUw, and MSL) were highly intercorrelated. MSC correlated with the measures of length and with DSS. DSS correlated with the length measures, though not as highly as MSC. DSS also correlated with IPSyn. IPSyn correlated moderately with PESP, correlated less with MSC, and correlated the least with the measures of length. PESP correlated moderately with each measure. PESP, DSS, and IPSyn correlated more highly for the children with language impairment. These measures correlated highly sometimes and sometimes they did not correlate much. This suggests that they are measuring different aspects of syntactic ability.

Several studies using standardized tests have provided evidence for the presence of language disorders in persons with Multiple Sclerosis (MS) and have suggested that persons with MS may have language that is less complex. One aspect not thoroughly studied is that of syntactic complexity in naturalistic, conversational settings. The present study collected language samples from 10 adults with MS and 10 age-matched controls and compared scores on five quantitative measures derived from those samples. No significant differences were found between groups on any measure. This finding suggests that in mild cases of MS or during periods of remission, individuals may retain their ability to use complex language structures in naturalistic discourse.