1. 1. (Humphrey, p. 84, no. 2)

2. Two pulleys of weights 12 lb and 8 lb are connected by a fine string hanging over a smooth fixed pulley. Over the former is hung a fine string with weights 3 lb and 6 lb at its ends, and over the latter a fine string with weights 4 lb and x lb. Find x so that the string over the fixed pulley remains stationary, and find the tension in it.

3. 2. (Part of Humphrey, p. 75, nos. 566)

4. A mass of 9 lb resting on a smooth horizontal table is connected by a light string, passing over a smooth pulley at the edge of the table to a mass of 7 lb hanging freely. Find the common acceleration, the tension in the string and the pressure on the pulley.

5. 3. Two particles of mass B and C are connected by a light string passing over a smooth pulley. Find their common acceleration.

6. 4. Particles of mass 3 and 6 lb are connected by a light string passing over a smooth weightless pulley; this pulley is suspended from a smooth weightless pulley and offset by a particle of mass 8 lb. Find the acceleration of each particle.

7. 5. A man of 12 stone and a weight of 10 stone are connected by a light rope passing over a pulley. Find the acceleration of the man. If the man pulls himself up the rope so that his acceleration is one half its former value, what is the upward acceleration of the weight?

8. […]

This chapter presents the formalization of the pulley domain. In this domain, the entities involved tend to be simple solid entities like particles and strings, while the relationships between them include notions of support, contact, or motion of some form. Section 3.2 describes the formalization of the pulley world in terms of the types of entities and their properties. The relationships are used for the decompositions of the verbs which are described in section 3.3 where the lexical entries of the verbs are listed. Each verb is subcategorized in terms of the primary relationship involved in the decomposition. The semantic roles are arguments of these relationships. The lexical entries include the decompositions of these primary relationships. Section 3.5 introduces the mapping constraints for assigning syntactic constituents to semantic roles. Examples demonstrate how the syntactic cues can be used with predicate environments to preserve the same semantic role interdependences that are preserved by templates. The last section describes the semantic constraints used in conjunction with the mapping constraints to test that the referent of a syntactic constituent is of the correct semantic type. The last category of constraints described, the pragmatic constraints, are used by inference-driven mapping to fill semantic roles that do not have mappings to syntactic constituents. Chapter 4 describes how inference-driven mapping interprets the lexical entries procedurally to drive the semantic analysis of paragraphs of text.

This chapter summarizes the results that have been presented in the preceding chapters, in particular the process by which inference-driven mapping goes directly from the syntactic parse of a sentence to a “deep” semantic representation that corresponds to a traditional linguistic decomposition. The summary illustrates two of the most important benefits offered by inference-driven mapping over the template approach, namely, (1) the clear distinction between the verb definition and the final semantic representation achieved, and (2) an integrated approach to semantic analysis. The first benefit is of special relevance to linguistic theories about semantic representations, in that it provides a testing ground for such theories. The second benefit is of more relevance to computational models of natural language processors, in terms of interfacing semantic processing with syntactic parsing. The last section suggests directions of future research for pursuing these objectives.

Integrated semantic analysis

As discussed in chapter 2, traditional approaches to semantic processing need several levels of description to produce a “deep” semantic representation from a syntactic parse. The most popular of these approaches, termed the template approach, can be seen as using at least two intermediate levels of description, (1) the template level which is used for assigning mappings from syntactic constituents and semantic roles, and (2) the canonical level where the semantic roles are grouped together to simplify derivation of a “deep” semantic representation. These separate levels of description impose several stages of processing on the implementations, since only certain pieces of information are available at any one stage.

INTRODUCTION

One can speak of Computer Integrated Manufacturing (CIM) if the computer is used in all phases of the production of some industrial product. In this paper we will focus on the design of the product-flow and the information-flow, which occurs when products are actually produced. Topics like product-development, marketing and management are beyond the scope of this paper. The technique used in this paper is based on a theory for concurrency, called process algebra (see or). It can be used to describe the total phase of manufacturing, from the ordering of raw materials up to the shipping of the products which are made from this materials. During this process many machines are used, which can operate independently, but often depend on the correct operation of each other. Providing a correct functioning of the total of all machines, computers and transport-services is not a trivial exercise. Before actually building such a system (a CIM-architecture) there must be some design. Such a specification, when validated, describes a properly functioning system.

INTRODUCTION

At this moment there are a lot of programming languages which offer facilities for concurrent programming. The basic notions of some of these languages, for example CSP, occam and LOTOS, are rather close to the basic notions in ACP, and it is not very difficult to give semantics of these languages in the framework of ACP. Milner showed how a simple high level concurrent language can be translated into CCS. However, it is not obvious at first sight how to give process algebra semantics of more complex concurrent programming languages like Ada, Pascal-Plus or POOL. This is an important problem because of the simple fact that a lot of concurrent systems are specified in terms of these languages. In this article we will tackle the problem, and give process algebra semantics of the language POOL.

INTRODUCTION

Extension of process algebra

In this paper we extend process algebra with a new operator that will be helpful to describe process creation. From a methodological point of view the extension of process algebra with new operators is just the right way to incorporate new features. Only in a very rich calculus with many operators one may hope to be able to perform significant algebraic calculations on systems. In many cases a new feature requires new (additional) syntax and more equations, only in very rare circumstances the addition of equations alone suffices to obtain an appropriate model of some new system aspect. The core system ACP, see, describes asynchronous cooperation with synchronous communication.

On top of ACP various features can be added, for instance: asynchronous communication, cooperation in the presence of shared data, broadcasting, interrupts. This note adds process creation to the features that are compatible with process algebra.

For historical remarks and relations with previous literature we refer to.

Process creation

We start on basis of the axiom system ACP which is supposed to be known to the reader. We assume the presence of a finite set of data D and introduce for each d∈D an action cr(d). The action cr(d) stands for: create a process on basis of initial information d. Let cr(D) denote the set {cr(d)|d∈D}.

INTRODUCTION

Many authors have studied the concurrency aspects of sorting, and indeed the n-time bubblesort algorithm (using n processors) is rather thoroughly analyzed already (e.g. see: Hennessy, Kossen and Weijland). However, bubblesort is not the most efficient sorting algorithm in sequential programming, since it is n2-time and for instance heapsort and mergesort are n log n-time sorting algorithms. So, the natural question arises whether it would be possible to design an algorithm using even less than n-time.

In this paper we discuss a concurrent algorithm, capable of sorting n numbers in O(log n) time. This algorithm is based on the idea of square comparison: putting all numbers to be sorted in a square matrix, all comparisons can be made in O(1) time, using n2 processors (one for each cell of the matrix). Then, the algorithm only needs to evaluate the result of this operation.

The algorithm presented here, which is called ranksort, is not the only concurrent time-efficient sorting algorithm. Several sub n-time algorithms have been developed by others (see: Thompson).

One of the basic problems in protocol verification is the following: data are to be transmitted from A to B via some unreliable medium M. A protocol has been proposed for doing so correctly and perhaps efficiently. A rigorous mathematical proof of the correctness claim is desired.

Now protocol verification aims at providing the techniques for giving such a proof. Several formalisms have been advocated, but as yet none has been widely accepted.

The framework we adhere to is process algebra. The first protocol correctness proof by means of process algebra is in Bergstra and Klop, where a simple version of the Alternating Bit Protocol is verified.

We have tried our hands at a more complicated version, called the Concurrent Alternating Bit Protocol (CABP) and found that the number of possible state transitions was prohibitively large. In this article we propose a divide-and-conquer strategy. We group processes into modules, describe and verify their behaviour and finally combine them. For different approaches, see.

In Section 1 we deal with the Concurrent Alternating Bit Protocol (CABP). In Section 2 we present the modular approach. Modules are introduced in Section 3, whereas the verification of the CABP is given in Section 4.