книги / Практикум по написанию научной статьи на английском языке

3. Переформулируйте предложения таким образом, чтобы сказуемое было выражено глаголом в страдательном залоге.

*The author develops the domain of the demonstration model based on module descriptors.

*Two object properties hasLearningOf and hasContextOf establish the logical association between learning outcomes and topics.

*We modeled the topics and subsequently classified them into subclasses based on knowledge of chemical engineering.

*Firstly, we categorized the topics into classes and subclasses using parent topics as guidance.

*We relate Mathematics, Thermofluids and ScientificFundamentals to chemical engineering as being its parent topics.

*Each parent topic has subclasses; we consider them to be subsets of the parent

topic.

*For example, we define ChemicalEngineering, BioSystemsEngineering and

MechanicalEngineering as subclasses of Engineering.

*We found the object and the context in the learning outcomes in the topic mereology.

*As fixed learning outcomes introduce topics, the approach suggested minimizes subjectivity effects of classification decisions.

4. Bыполните перевод следующих предложений на английский язык. При переводе сказуемых используйте формы страдательного залога. Сравните свои варианты перевода с ключом.

*Была разработана модель учебного плана/учебный план был смоделирован на основе моделирования знаний/ используя моделирование знаний.

*Планируемые результаты обучения (learning outcomes) были соотнесены с темами, для чего использовались [семантические] свойства глаголов (verb properties) и контекст каждого результата обучения.

*На примере конкретной ситуации (case study) была продемонстрирована функциональность семантического анализа (reasoning) посредством онтологии.

*Онтология может с успехом применяться для разработки учебных про-

грамм.

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*Каждый из результатов обучения связан с одним из модулей, который, в свою очередь, относится к определенному периоду/уровню обучения (a year level).

*Глаголы, обозначающие учебные действия (the learning verbs), классифицируются в соответствии с уровнем обучения.

*Новый материал следует разместить в контексте ранее пройденного (prior learning).

*Эти концепты организованы в виде таксономии посредством разбивки на подклассы (through class-subclass relations).

*В учебных программах средних школ Великобритании была разработана онтология для описания терминов.

*В учебных программах высших учебных заведений онтологический инжиниринг применялся для различных целей (various applications) .

5. Переформулируйте выделенные части предложений по образцу.

It is known that each learning outcome has a learning verb which defines one the learning levels, namely, knowledge, comprehension, application, evaluation and synthesis. → Each learning outcome is known to have a learning verb which defines one of the learning levels, namely, knowledge, comprehension, application, evaluation and synthesis.

•It is proposed that a knowledge model in the form of an ontology reflexes the curriculum and assists in decision making regarding curriculum development.

•It is believed that the information about the prior learning is useful when students demonstrate a lack of knowledge.

•It is known that the analysis of requirements for business-related software systems is often supported by using business process models.

•It is said that the success of small firms depends on their ability to adapt to the constantly changing situation in the market place.

•However, it is suggested that small and medium-sized enterprises are lacking the ability to plan a marketing strategy.

•It is expected that chemical engineers will acquire a certain skill set related to their profession.

•It has been proved that business process modeling is an effective means for analyzing requirements of business-related software systems.

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6. Переведите следующие предложения, обращая внимание на выделенные слова. Они – за исключением помещенных в рамку – являются герундием (неличной формой глагола, которая обозначает процесс выполнения действия).

Business process modeling is an established method for documenting, analyzing and improving organizational operation.

The approach we suggest consists of a process model-based procedure for capturing execution-related data and an algorithm which takes these models as input for generating natural language requirements.

Conceptual mapping was used for developing and validating engineering curricula based on the program outcomes.

We present the first large-scale analysis of hardware failure rates on consumer PCs by studying failures in CPU, DRAM and disk subsystems.

Studying consumer machines brings new challenges not present when studying server-class machines.

The MMU prevents software from directly modifying the page contents.

Disk subsystem failures occur when the operating system is unable to proceed without reading data from the disk.

We begin discussing measuring hardware failure rates by quantifying the probability that the machine will fail.

We begin by analyzing two subsets of machines in our study.

7.Выполните обратный перевод (на английский язык), не обращаясь к исходным предложениям, и сравните свой перевод с оригиналом.

8.Ознакомьтесь с приведенной ниже выдержкой из раздела Методология. Выпишите выделенные слова, выражения и фразы, передающие логи- ко-смысловые отношения между отдельными предложениями. Распределите эти языковые средства обеспечения связности текста по следующим рубрикам:

– обобщение/генерализация;

– противопоставление/контраст;

– подобие/сходство;

– причинно-следственные отношения;

– выражение условия;

– обозначение выводного суждения;

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–аддитивные отношения (передающие дополнительную информацию);

–целевые отношения;

–последовательность;

–прочие.

2. Methodology: development of ChEEdO*

2.1. Curriculum development strategy

Chemical engineering is an applied discipline that brings together different scientific concepts under the same context. Generally, chemical engineering curricula follow a modular structure. Each module comes with a set of learning outcomes, which have to be achieved by the module to be passed. As a student progresses through their degree, there are core concepts that are expected to be covered. The sequence of topics in chemical engineering is important as fundamental concepts learnt in earlier years are built upon in later years. To this end, students benefit from obvious vertical integration within their curriculum that is a clear link between current and prior learning.

In addition to vertical integration, horizontal integration in the curricula is beneficial to the students. Due to the modularised nature of the degree, students are often unable to see the connection between different topics and, consequently, the curriculum lacks integration throughout the degree program. In order to exemplify these connections, horizontal integration has been suggested. This can be done by setting a single piece of coursework that relates to two or more concurrent modules. In addition, staff engagement efforts can be reduced by using single assessment pieces across module. Hence, horizontal integration is able to reduce staff workload.

As evident (Byrne 2006), chemical engineering graduates can be found in highly specialist areas, such as molecular engineering, nanotechnology and microelectronics. To further develop the curriculum, addition of specialization is becoming more desirable. Specialization options may be reflected in the expertise and research interest of the staff teaching the degree (Comes et al. 2006). In terms of teaching efficacy it is often best to align teachers with fields of expertise in order to maintain enthusiasm which assists in students motivation (Patrick et al. 2000). Developing material at a modular level, however, requires an in-depth knowledge of the content within a curriculum across the degree program. In addition, constant evolution of industry and technology alternative skill sets to the traditional programs. However, the program still require core material to be embedded within the curriculum. In order to reflect and develop a curriculum, core material should be identifiable.

Constant evolution of teaching methods, industry, technology and graduate requirements mean that curricula are continuously evolving. In order to develop a curriculum to meet these changes, an in-depth knowledge of current curriculum is required. Then, the addition of new material in later years requires knowledge of prior learning in previous semesters and years. Similarly, the students should be able to

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place their current learning in the context of application. The curricula are also evolving. Therefore, a knowledge model in the form of an ontology is proposed to reflect the curriculum and to assist in decision-making regarding the curriculum development. A modeling approach allows for integration and contextualization of learning and provides a tool to inform learners and teachers about the curriculum content. To this end, ontology is designed to model the knowledge contained within the curriculum. <…>

*ChEEdO – Chemical Engineering Education Ontology

9.Используя материал изучаемых дисциплин, приведите свои примеры употребления этих языковых единиц (на русском и английском языках).

КЛЮЧИ К ЗАДАНИЯМ И УПРАЖНЕНИЯМ

#4

•The curriculum was modeled using knowledge modeling.

•The learning outcomes were related to the topics using verb properties and the context of each learning outcome.

•The functionality of semantic reasoning via the ontology was demonstrated with the case study.

•The ontology can be successfully utilized for curriculum development.

•Each learning outcome is linked to a module, which in its turn belongs to a year level.

•The learning verbs are classified into one of the learning levels.

•New material should be placed in the context of prior learning.

•These concepts are organized in a taxonomy through class-subclass relations.

•Within high school curricula in the UK, an ontology for the description of terminology was developed.

•Ontology engineering in higher education curricula has been used for various application

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РАЗДЕЛ IV

Результаты и обсуждение [англ. Results and discussion]. В данном раз-

деле описываются и обсуждаются результаты, полученные в ходе эксперимента, произведенных расчетов или моделирования процессов и объектов, выступающих как предмет исследования, отдельных параметров исследуемых объектов, особенностей их поведения и пр. Проводится сравнение данных, добытых эмпирическим путем, с данными, полученными при моделировании. В разделе, как правило, содержится интерпретация полученных результатов. Часто эта информация бывает представлена таблицами, графиками и диаграммами, которые со-

провождаются текстом-описанием с элементами рассуждения и доказатель-

ства. В результате значительные фрагменты научной статьи представляют собой последовательное изложение материала, в котором присутствуют умозаключе-

ния, выводы, ссылки на полученные данные и те, которые упоминались ранее, а также информация относительно дальнейших перспектив исследования. Соот-

ветственно, более интенсивно используются средства экспликации логикосмысловых связей, особенно тех, которые имеются между отдельными предложениями и частями текста.

Задания и упражнения

[На материале статей: 1. A clustering approach to domestic electricity load profile characterization using smart metering data. F. McLoughlin et al. / Applied Energy 141 (2015), pp. 190–199. (Dublin Institute of Technology). 2. An ontological approach to chemical engineering curriculum development. M. Bussemaker et al. / Computers and Chemical Engineering 106 (2017) 927–941. (Surrey University & University of

Cambridge, UK)].

1. Ознакомьтесь с выдержкой из статьи 2. Исходя из контекста, подберите русскоязычные эквиваленты для подчеркнутых слов и словосочетаний.

3. Results and discussion

Once the ontology model was constructed, semantic reasoning was used to reclassify the knowledge of the model in ways which were meaningful to the user. The classification used the Pellet 1.5.2. reasoner which allowed for consistency checking concept, satisfiability, classification of classes and subclasses, and realization of which classes an instance belongs to, all according to the defined relationships within the ontology. This can be used for many different scenarios, however to demonstrate the functionality four case studies are chosen, i.e. horizontal integration, vertical integration, curriculum development through contextualization of learning, and curriculum development through inclusion of new material.

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3.1. Horizontal integration in the first year of study

Horizontal integration aims to conceptually connect two co-current modules either through co-teaching or co-assessment. In order to probe the ontology for potential horizontal integration cases we used semantic reasoning to discover which contexts and learning objects are overlapped within two or more learning outcomes. Each learning outcomes is attached to a module, which is taught at a specific level, within a specific semester and overlapping modules can be identified. Therefore, a specified class of learning outcomes is defined to find two or more modules with related learning via the learning outcomes.

2.Выпишите комплексы подлежащее + сказуемое, выраженное глаго-

лом в Passive Voice. Напишите их русскоязычные эквиваленты, изменяя, где

необходимо, порядок слов.

3.Выполните полный письменный перевод отрывка, обращая внимание на выделенные средства передачи логико-смысловых отношений. Выберите для них соответствующие эквиваленты на русском языке и оформите в виде списка.

4.Выполните обратный перевод на английский язык, не обращаясь к исходному тексту. Сравните свой перевод с оригиналом.

5.Выполните аналогичные задания на материале отрывка из статьи 1. Обратите внимание:

–на выделенные в тексте стандартные обороты речи (клише), используемые для ссылки на предшествующее изложение, имеющиеся в тексте рисунки и диаграммы, используемые для привлечения внимания к тому, что будет написано далее и прочих целей;

–выделенные в тексте средства языка, обеспечивающие связность и логику изложения.

Дополните список (см. выше задание #3) соответствующими словами и фразами из текста 1.

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5. Results and discussion

The following section presents results and discussion for each stage of the methodology described in section 4.

5.1.Clustering

The DB index* was calculated for each of clustering technique and for varying number of clusters over three separate random days with the average shown in Fig. 3. SOM** showed a consistently lower DB index overall varying number of clusters, and therefore was selected to segment the data further. The optimal number of segments used to divide the data was chosen at between 8 to 10 clusters as after this point any further decrease in DB index was minimal. It is important to note that the DB index was lowest overall for two clusters. The dataset was divided into nine clusters based on 3x3 hexagonal lattice structure. Cluster centers are shown to be visually separated by different colours. The brighter colours show clusters that are close together whereas the darker colours represent clusters that are further apart. It can be seen that clusters c6 and c9 are the most similar to each other compared to any other cluster pair.

The cluster size is shown as a percentage of total sample size in Fig.4. Clusters c6 and c9 combined represent nearly two thirds of the entire sample and therefore these were further divided using sub-clustering. C6 and c9 were divided into four additional clusters each as shown on the right hand side of Fig. 4.

5.2. Electricity load PC*** characterization

In total, ten PCs were produced using the methodology described in Section 4.2 which represent different patterns of electricity use both in terms of magnitude and timing.

Fig.6 shows diurnal patterns of electricity use for each PC over the six month period. In the majority of classes a characteristic ‘primary peak’ and a smaller ‘secondary peak’ of electricity use are apparent. If a ‘primary peak’ occurs in the morning then the ‘secondary peak’ tends to be smaller in magnitude in the evening. Similarly, the converse is also true. It must be noted that PC8 shows characteristic quite different to any other class in terms of electricity use across 24-hour period and most likely corresponds with a vacant dwelling. A clear distinction can be made between Weekends and Weekdays, where the majority of PCs show electricity use earlier in the morning for the latter. This earlier use of electricity during the Weekdays is most likely due to employment and schooling commitment for the occupants. Similarly, an earlier morning peak is apparent on Saturdays compared to Sundays, with latter showing more electricity use across the afternoon period.

The seasonal component to the classes is illustrated in Fig.8. The brighter colours represent mid/late summer while the darker colours indicate mid/late winter. The change in profile shape between seasons (particularly mornings and evenings) is likely to be influenced by sunrise and sunset times. However, this could also be related to a change in occupancy between Summer and Winter. Similarly, a change in profile

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shape during early morning/afternoon over the Summer may be related to changes in occupancy. For instance, when children are at home during school holidays they spend more time using PCs. However, this could also be related to an increase in external temperatures during the summer. Thus, it may result in a greater cycling of air conditioners. As a similar increase is observed during the night, it may be suggested that it is temperature rather than occupancy influences the use of electricity.

5.3. Customer PC classification

As discussed in Section 4.3, the statistical Mode was used to determine which PC customers used for the majority of time over the six month period. A multi-nominal logistic regression was then applied to determine the likelihood of households with certain characteristics using electricity in a similar manner to each PC. Table 2 presents results for the regression and shows the strenghth of association for each variable with each individual PC. Table 2 also shows standard errors and levels of statistical significance for each variable. <…>

*DB index – Davis-Bouldin validity index

**SOM – Self Organizing Maps

***PC – Profile Class

6.В подразделе 5.2. (Electricity load PC characterization) найдите примеры

выражения мысли в предположительной непрямолинейной манере. За счет чего достигается эффект некатегоричности?

7.Предложите несколько вариантов смягчения категоричности для каждого из следующих суждений, используя: it might be suggested; probably; is likely; is supposed; is unlikely.

– For this purpose, semantic reasoning will be used.

– People use more electricity in winter time due to lower temperatures outside and consequently in their homes.

– Temperature has a greater effect on electricity consumption than occupancy.

– Patterns of electricity use correspond with both seasonal and occupancy factors.

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РАЗДЕЛ V

Заключение [англ. Conclusions] содержит краткую формулировку результатов проведенного исследования. В зависимости от предмета и формата исследования в нем суммируются итоги анализа и осмысления проблемы, подчеркивается практическая значимость работы, делаются выводы и обобщения, даются рекомендации и обозначаются основные направления для дальнейшего исследования в данной области. Объем этого раздела небольшой, примерно 1200–1500 знаков. Языковое оформление предполагает использование ряда стандартных фраз и оборотов речи (клише), союзов и союзных речений, предваряющих выводы и выражающие причинно-следственные отношения. В остальном языковое оформление этого раздела не отличается от других частей статьи: присутствуют сказуемые в страдательном залоге, безличные обороты речи, средства экспликации смысловых связей разного уровня.

Ниже приводится в полном объеме текст заключения из статьи

F.McLoughlin et al. A clustering approach to domestic electricity load profile characterization using smart metering data. Ознакомьтесь с представленной в нем информацией, обращая особое внимание на языковые средства, которые используют авторы для ее передачи: оборот Complex Subject (сложное подлежащее); формы страдательного залога; безличные обороты с it; стандартные клишированные фразы; неличные формы глагола с окончанием -ing.

5. Conclusions

This paper presents a clustering methodology for creating a series of representative electricity load PCs for the domestic sector in Ireland. Clustering methods were evaluated against a DB validity index for segmenting the data into disparate patterns of electricity use within the home. SOM proved to be the most suitable and therefore was used to segment the data prior carrying out any aggregation. In this way characteristic information pertaining to the load profile shape is maintained.

Ten PCs for each day across a six month period were presented thus preserving the diurnal; intra-daily; and seasonality components to electricity use within the home. A multi-nominal logistic regression was then used to link PCs to dwelling, occupant and appliance characteristics. In most cases, individual customers characteristics showed either a positive or negative association with each class indicating which pattern of electricity use was more or less likely to be used within a household. As a result, it is possible to classify customers and the manner with which they use electricity based on their individual characteristics, and without prior knowledge of household electricity consumption.

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