Academic literature on the topic 'LISREL (Computer file)'

AbstractThis chapter focuses on specific challenges to surveying newly arrived immigrants with a focus on refugees. In addition to the need to provide interviews for immigrants in their native language, it must be taken into account that a considerable proportion of this group has poor or no reading skills in their native language. Two strategies can be used to avoid systematically excluding this population: offering interviews with native-speaking interviewers or using computer-assisted self-interviewing (CASI) with additional audio files that enable respondents to listen to a questionnaire. We discuss the pros and cons of both strategies. Subsequently, using the data from the first wave of the German refugee study ReGES, in which both strategies were offered as a combined approach, we consider their effectiveness and practicability in more detail. Although native-speaking interviewers can increase cooperation and help to not exclude illiterate individuals, they also can encourage a higher social desirability bias. However, illiterate interviewees are more likely to take advantage of the interviewer’s support to read the questions aloud than to use the audio files. Nevertheless, we also found that a small but substantial subgroup of interviewees with little or no reading skills used the audio files often.

Many people worldwide have the problem of visual impairment. The authors' idea is to design a novel image captioning model for assisting the blind people by using deep learning-based architecture. Automatic understanding of the image and providing description of that image involves tasks from two complex fields: computer vision and natural language processing. The first task is to correctly identify objects along with their attributes present in the given image, and the next is to connect all the identified objects along with actions and generating the statements, which should be syntactically correct. From the real-time video, the features are extracted using a convolutional neural network (CNN), and the feature vectors are given as input to long short-term memory (LSTM) network to generate the appropriate captions in a natural language (English). The captions can then be converted into audio files, which the visually impaired people can listen. The model is tested on the two standardized image captioning datasets Flickr 8K and MSCOCO and evaluated using BLEU score.

One of the assumptions underlying EXPRESS is that there is, somewhere, going to be an information base that contains instances of data corresponding to the information model. In this Chapter we examine aspects of this hypothetical information base. We also briefly note some of the software tools that could be useful when you are modeling using EXPRESS. We use the term information base in a very general sense; it is any repository that contains data corresponding to an EXPRESS (or EXPRESS-G) information model. The idea that probably comes to mind when hearing the term is that ‘an information base is a fancy name for a database.’ In our sense, an information base may be a database, but it may also be more than or less than a database. In fact, it may not even be computer-based at all! Some examples of information bases are: • Intelligent Knowledgebases • Knowledgebases • Databases • Computer files • Printed documents These examples are listed in approximately decreasing order of technical complexity and increasing order of technology age. Thus, intelligent knowledgebases are at, or even beyond, the leading edge of technology, while printed documents have been available for some centuries, although the technology for producing these has made dramatic strides over the last decade. Below we briefly describe various types of information bases, starting with databases. Knowledgebases are at the leading edge of the technology and are not treated; we merely note that there appears to be no fundamental reason why EXPRESS models should not be stored and instanced using this advanced technology. Databases provide a structured means of both storing data in a computer system and of querying the data in an efficient manner. Internally, the data may be structured in the form of a network, a hierarchy, or in tables following the relational model. Most new databases are relational, while older ones may be hierarchical or network based. Object-Oriented databases have recently appeared, but as yet there appears to be no consensus on exactly what an OODB is. Databases are designed so that they can be modified and queried by mutiple users.

Abstract Three-dimensional basic solids generated by linear sweeping of primitives are presented to demonstrate that when stored as a script file, every detail involved in the linear sweep can then be listed. A linearly tapered fillet serves as an example to depict the boolean operations. For better visibility of a solid, splitting the display screen into multiple windows for showing various projects of the solid when viewed from different angles, the hidden-line technique, and changing the location of light source(s) in addition to an ambient light are also described.

Computer Aided Design (CAD) has some specific features compared with hand-drawing. Quite beneficial opportunities arise using Layers. Object’s properties (color, linetype, lineweight) may be assigned individually to each object or grouped by belonging to a particular Layer (ByLayer). Layers are like transparent overlays on which we organize and group objects in a drawing. We can use Layers to control the visibility of objects and to assign properties to objects. Layers can be locked to prevent objects from being modified. Layer Properties Manager informs us about layers’ parameters and allows to change these values. We can change the name of a layer and any of its properties, including color and linetype and you can reassign objects from one layer to another. We can control which layer names are listed in the Layer Properties Manager and sort them by name or by property, such as color or visibility. We can save Layer settings as named layer states. We can then restore, edit, import them from other drawings and files, and export them for use in other drawings. Which number of Layers is optimal? The answer depends on several considerations – using every Layer should be adequately argumented. The skilled use of Layers can turn the construction process into a more flexible and rational one. But adding any new Layer must be justified. Otherwise, some uncomfortable problems could arise.

It is difficult to separate technology from education because technology is embedded in teaching. The best technology for teaching is the one that does not interfere with the communication between the teacher and the students. In other words, it should be barely “noticeable” in how it is used, and easy enough to use that it does not require special training, such as window in a room to see the flowers outside, without interfering in any way. The technology used during an in-person classes is so basic, transparent, and simple, since it consists of a classroom, chairs, blackboard and pieces of chalk. However, a piece of chalk could break. In this case, the flow of information is interrupted; which is why the plastic whiteboards and markers are preferable and cleaner. An electronic board may seem to be better with its many improvements with computers; however, its higher technology could get in the way if it is not used properly. During the pandemic, different technologies, like computers and cell phones, connected to internet were massively used. Yet, computers and cell phones with the appropriate software and apps had an additional cost; with no guarantee of antivirus applications against viruses or worms, leaving aside protection for documents, software and hardware. On top of that, a Zoom, Google or any similar online platform was necessary. Using these platforms, other technologies had to be relied on, including Ethernet or a USB adapter. On the other hand, in developing countries before the pandemic, low-tech was used by most students to store and pass information to their peers and teachers. They would use mainly pens and notebooks to write notes, or CD-Rs or USB to pass information. However, during the pandemic, with the advances in internet speed, many students have now moved on to using e-mail and cloud drives where they can transport their files. All the electronics listed to make online education or tech-based education needed electricity to work. In other words: no electricity, no class; no electric supply or stored electricity in batteries, no class. In this paper we analyze the role of technology in education, and how this technology could enhance or obstruct the communication between the teacher and the students.

Plastics are an important material with widespread applications. However, their widespread use and poor end-of-life management have led to their extensive environmental pollution. They can be found in oceans, terrestrial ecosystems, and even remote corners of the Earth. Current methods for microplastic quantification and identification require big investments and highly trained personnel to operate the analytical equipment. In this paper, we propose an algorithm-based method for the quantification of microplastics in soil and organic fertilisers. The method is based on image analysis of a thinly spread sample that was heated until microplastics has visually melted. The algorithm-based method was validated with Focal plane array detector-based micro-Fourier-transform infrared imaging (FPA-μFTIR), frequently used in microplastic characterisation. Herein, we present the pre-liminary results of an ongoing study. In a compost sample, five particles were detected with FPA-μFTIR, whereas the algorithm detected eight. The algorithm has difficulties recognising elongated or oddly shaped particles. These were identified as several particles which led to overestimating the number of microplastic particles in the investigated sample. We will continue with further develop-ment of the computer algorithm by using a training set of images which will be quantified using different methods (visual detection by a human operator, FPA-μFTIR). This growing training set will enable us to incorporate machine learning algorithms (neural networks) in the development of a more reliable particle detection algorithm. We expect that environmental monitoring of microplas-tics will be required under future legislation, therefore the development of cheap, user-friendly so-lutions is crucial. Keywords: Machine learning; Algorithm; Infrared spectroscopy; Soil contamination; Organic ferti-lisers; Compost

BIM uses are complex specific processes in architecture, engineering, construction, and operation mediated by Building Information Modeling technologies. Several initiatives are dedicated to detailing these uses in a standardized way, enumerating and describing them in terms of scope, benefits, process maps, required competencies, associated technology, and theoretical framework. Examples of these efforts are Penn State's Computer Integrated Construction Research Program (MESSNER et al., 2019), buildingSMART (2021), and BIM Excellence Organization (SUCCAR; SALEEB; SHER, 2016). This study presents the approach to educate, evaluate and assist Model Uses using templates (Model Use Templates - MUT) of the BIM Excellence Initiative (BIMe). The BIM use is called Model Use in BIMe terminology. In three years, starting in 2021, the initiative intends to detail all the domain model uses listed by the organization (BIMe, 2020). The domain model uses are organized in the series of capture and representation, planning and design, simulation and quantification, operation and maintenance, monitoring and control of buildings and infrastructures. In terms of domain model uses, there is the linking and extending series of BIM integrated to Facility Management, interfaced with the Internet of Things, linked to Enterprise Resource Planning, etc. The initiative developed a Construction Domain Model Use Template (MUT) and applied it as a demonstration for Clash Detection or MUT 4040. This summary will describe the template, its application to Clash Detection, and guidance on how to transform it into a template class to teach Clash Detection with BIM. The MUT consists of an extended description, software list, activity flow, and bibliography. This content is available in the BIM Dictionary associated with the equivalent term (https://bimdictionary.com/en/clash-detection/1). The extended description includes the corresponding term's definition, the detailed description, purpose, and an available online media-list. The detailed description presents the different types of use (e.g., hard, soft, time-based) and benefits. The software list lists platforms and environments used in the model use development. For each platform or environment, there is a list of the vendor or developer, the corresponding technical functionality, the applicable discipline, the software description, the availability of the software in the cloud or location, differentiation of versions, the link to the official website, the model use code that the software can support, specific functionalities associated with the use and availability of a plugin or extension. The activity flow is described using a process map and details in up to 3 hierarchical levels for each macro activity. All the terminology adopted in the MUT is semantically aligned to the various projects and initiatives of BIM Excellence, bringing consistency to the meaning. In the case of MUT 4040, that is, the application of the template for the model use of Clash Detection, the short description is a “Use of the Model representing the use of 3D Models to coordinate different disciplines (e.g., structures and air-conditioning) and to identify/resolve possible conflicts between virtual elements prior to actual construction or fabrication”. The extended description presents the Clash Detection as automated or semi-automated procedures to identify design errors in 3D models, where objects occupy the same space or are too close to violating spatial restrictions. Time-based interferences are conflicts involving temporary objects that compete for the same space at the same time. The benefits are listed, for example, like better project coordination and quality; conflict reduction in the workplace; acceleration of design and delivery processes; and cost reduction through productivity increase. The available online media does not represent the entire process involved in Clash Detection and are generally restricted to confronting models on specific platforms. We advocate that the activity flow should structure the class of model uses in BIM education. In this way, there is a holistic and representative approach to practice. Thus, we advise escaping this model's understanding in a restricted and instrumental way, as it already occurs in most of the online media found. We propose to organize the class program by the macro stages of the activity flow, covering: (i) creation of the strategy for the clash detection in the project in question; (ii) preparation of specific models for federation; (iii) identification of federation environments or model integration; (iv) federation or integration of models; (v) checks for interference in the federated or integrated model; (vi) analysis of the conflicts identified; and (vii) referral to conflict resolution. The details of each of these activities in the template can guide the teacher on how to proceed or prepare educational content. The bibliography listed in the template covers the theoretical framework to support the class in terms of books, scientific articles, and BIM guides. One can develop the class at the level of graduation, extension, or continuing education. Being an undergraduate class, it can be mandatory or elective. Items (i) to (iii) make up the theoretical part of the class, and the rest are essentially practical content. Thus, two types of competency assessment are possible: knowledge and skills. Knowledge can be developed through discussions and seminars. Skills covered are associated with execution or domain skills, according to Succar, Scher, and Willams (2013). Execution skills are associated with learning model verification platforms and collaboration environments. The execution competence generates an instrumental skill that can be provided through individual online training with tutorials. Domain skills are essentially technical (analysis and simulation) and functional (collaboration). These skills must be instigated in a participatory and collaborative way in practical exercises involving cycles of verification of the federated model and adjustments of complementary projects' models. As a suggestion for support material, the teacher should prepare a dataset including models with errors in file naming disobeying conventions, errors in the control elements impacting the overlapping of models, errors of omission or duplication of elements in the models, and errors of data schema in terms of categorization of elements and classification of content. The models must also include issues of all types (hard, soft, and temporal interferences). Errors must be plausible to be identified by different types of verification: visual or script. YouTube presentation: https://youtu.be/cMPaw_kOZtQ