Organizational, direct support and general support maintenance manual including repair parts and special tools list (including depot maintenance repair parts and special tools) for machine gun, 7.62-mm, M73 (1005-869-8816); machine gun, 7.62-mm, M73A1 (1005-937-7323); machine gun, 7.62-mm, M219 (1005-077-2354).

Includes index.

Organizational, direct support and general support maintenance repair parts and special tools list (including depot maintenance repair parts) for machine gun, 7.62-mm, M60 W/E, (1005-00-605-7710) ...

Includes index.

Organizational, direct support, general support and depot maintenance manual including repair parts and special tools list : machine gun, caliber .50, Browning, M2, heavy barrel, flexible, W/E (1005-322-9715), mount, tripod, machine gun, M3, W/E (1005-322-9716), mount, machine gun, antiaircraft, M63 ... commander's cupola (1005-013-6944).

"July 1968."

Organizational and direct support maintenance repair parts and special tools list (including depot maintenance repair parts and special tools) for machine gun, 7.62MM, M240 (1005-01-025-8095) and machine gun, 7.62MM, M240C (1005-01-085-4758).

Includes index.

Direct support and general support maintenance repair parts and special tools lists : armament subsystem, helicopter, 7.62 millimeter machine gun -- 40 millimeter grenade launcher, M28A1E1 (1090-00-081-1046) (used on AH-1Q Helicopter).

"October 1975."

Organizational maintenance repair parts and special tools list : armament subsystem, helicopter, 7.62 millimeter machine gun-2.75 inch rocket launcher, M21 (1090-923-5971) (used on UH-1B and UH-1C helicopters).

Includes index.

Direct support, general support, and depot maintenance repair parts and special tools list : armament subsystem, helicopter, 7.62 millimeter machine gun-2.75 inch rocket launcher, M21 (1090-923-5971) (used on UH-1B and UH-1C helicopters).

"October 1970."

Unit and intermediate direct support maintenance repair parts and special tools list (including depot maintenance repair parts and special tools) for machine gun, 7.62mm, M240 (1005-01-025-8095) and machine gun, 7.62mm, M240C (1005-01-085-4758) and machine gun, 7.62 mm, M240E1 (1005-01-252-4288) [microform]

"June 1988."

Organizational, direct support, and general support maintenance repair parts and special tools list ... for reeling machine, cable, engine driven, RL-207/G and RL-207A/G (NSN 3895-00-892-4583).

"August 1976."

Modern American lathe practice; a new complete and practical work on the "king of machine shop tools," the American lathe. Giving its origin and development. Its design. Its various types as manufactured by different builders ... etc.

Mode of access: Internet.

Using Machine Learning to Enhance Software Tools for Internet Information Management

Postprint

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data.

Patent translation with CAT tools and Machine Translation

The aim of this essay, which focuses on patent translation, is to compare the use of Computer-Assisted Translation (CAT) and Machine Translation (MT). During my curricular internship at a specialized-translation agency called Centro Traduzioni Imolese, I was able to practice patent translation thanks to CAT tools like SDL Trados Studio, something I have never studied at university in Forlì. Nowadays, however, Machine Translation is widely used in patent translation as well, due to the vast number of technical terms and their repetitiveness in patents, so the machine can translate automatically and rapidly all repeated terms with the same word, thanks to the use of corpora and translation memories linked to the patent field. In the first chapter I will give a definition of what a patent is, and I will introduce the concept of patent literature; afterwards, I will illustrate the differences between Computer-Assisted Translation and Machine Translation used in patent translation. In the second chapter I will translate two portions of patent 102019000018530, via the Matecat online application, translating the first part with CAT and the second part with MT, then doing the same for the second portion selected from the patent. Finally, in the third chapter, I will analyse the two translations, comparing the results in order to discover which is the more efficient method for translating patents.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.