Basic Windows Research Articles

Life cycle assessment methodology integrated with BIM as a decision-making tool at early-stages of building design

Climate change is one of the major concerns worldwide and greenhouse gas emissions plays an important role in increasing the temperature of the earth’s atmosphere. Furthermore, the construction industry is one of that presents a considerable environmental impact as it consumes large amounts of natural resources and energy. Aiming to investigate the environmental impacts, Life Cycle Assessment (LCA) methodology evaluates products and services showing the similarities and differences in evaluating midpoint and endpoint impact categories. This study reviews LCA from a building perspective and empowers the decision-making process and sustainable building design in the construction sector. The novelty is to estimate the operating energy performance and evaluate the endpoint impacts of building materials at an early designing stage, considering two methods of construction; concrete construction and steel construction. This work follows the methodological framework of LCA based on ISO 14040 and 14044 guidelines within the available database, using Autodesk Green Building Studio application in Autodesk Revit and Open LCA software based on two assessment methods: IMPACT 2002+ and ILCD 2011. A case study of a multi-story office building is applied to achieve the objectives of this work. The building is structured at four levels of subsystems and associated construction materials: basic structure, walls, finishing, and windows. Results indicate that using steel construction instead of concrete construction is more environmentally friendly in such types of buildings. It encourages the application of innovative techniques in the production process to protect natural resources, reduce energy consumption, and protecting the built environment.

MOBILE CHATTING BEHAVIOUR OF ARTS AND SCIENCE COLLEGE STUDENTS

Mobile connectivity is the order of the day. Personas irrespective of their socio-economic status possess mobile device either basic or advanced android or windows or IOS. The chat applications have become popular with younger generation. It has started trickling down to children below the age of eighteen. The behaviour has influenced the aged also. The mobile chat applications have no barriers with regard to age group, nativity, social status and economic status. The increasing dominance of these mobile chat applications need to be studied. It has been eating away our young people’s time and mind. The recent election in Tamil Nadu is the best example. Parties have used these chat applications to make their comments, appeals, abuses and pleas. Wherever we go it is obvious that the students sit with mobile apps ignoring the presence of others. It has become the natural quest of everyone who is penchant in doing research to take up a study on this behaviour. Hence, the investigators have taken up this study to find out arts and science college students mobile chatting behaviour like use of chat applications, time of chatting and chatting with the people associated with them. The study has used simple random sampling technique of 300 arts and science college students of Chennai area. The findings of the study reveal that there are four chat applications namely WhatsApp, Messenger, Skype and Hang out occupying first, second, third and fourth places respectively among arts and science college students.

Regression Model Using Instance Based Learning Streams

Data mining is concerned with the analysis of data for finding patterns and regularities in the data sets. Statistics is a mathematical science concerned with the collection, analysis, interpretation or explanation, and presentation of data. Statistics plays a very important role in the process of data mining analysis and equally visualization of data plays a very important role in decision making process. Instance Based Learning Streams is an instance-based learning algorithm used to perform regression analysis on data streams. The algorithm is able to handle large data streams with less memory and computational power. The paper aims at the implementation of Instance Based Learning Streams as an extension to the massive online analysis framework for data stream mining to develop a regression model. The study reveals that the regression analysis could be performed not only on small data sets but also on data streams as in the present case but the method of analysis will be different in the two cases. In the case of small data set the regression models are linear, multiple and polynomial, while in the case of data streams the entire analysis is performed under the massive online analysis framework by taking the two evaluation parameters basic regression performance evaluator and windows regression performance evaluator. This finding is first of its kind in literature.

Frequent Itemsets Mining Based on Concept Lattice and Sliding Window

In this paper, a frequent itemsets mining algorithm of data stream based on concept lattice and sliding window is presented. This algorithm mines frequent concepts for new inflowing basic window in batches in a sliding window and generates concept lattice Hasse diagram. With introduction into small support degreeand error factorto do the pruning operations for non-frequent concept node, each connection point in the Hasse diagram contains the information of frequent itemsets and support degree. As the generation of Hasse diagram in the new basic windows, we integrate concept lattice vertically with the generated Hasse diagram and sliding window, and ultimately output all frequent itemsets through scanning all the graph nodes of Hasse diagram graph. The experimental results show that the proposed algorithm has a good performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3140

Estimation of the Number of Distinct Values over Data Stream Based on Compound Sliding Window

Estimating the number of distinct values in a data stream is a vital problem with many applications such as complex join query over multiple data streams. In this paper, we focus on the continuous and periodic distinct values estimation over sliding windows. We propose a compound sliding window model to compute the distinct values over basic sliding windows in an incremental way. LDV, HDV and AHDV are the three algorithms that are based on compound sliding windows. The basic idea behind the compound sliding windows is to organize the basic windows into a Hash table according to distinct values. Whenever a new data arrives at the data stream, it is inserted into a basic window. Once the basic window is full, a scan using distinct values is executed and the distinct values number is updated incrementally. Theoretical analysis and experiment results show that the distinct values estimation algorithms based on compound sliding windows have a great performance benefits.

Software for Speech Signal Research in Patients with Malignant Diseases of the Throat

Software for speech signal research in patients with malignant diseases of the throat is considered in this work. Basic windows of this system are discussed. These windows represent voice parameters set by a speech therapist to diagnose the disease and to make a decision concerning therapy and rehabilitation.

Mining frequent closed itemsets from a landmark window over online data streams

The frequent closed itemsets determine exactly the complete set of frequent itemsets and are usually much smaller than the later. However, mining frequent closed itemsets from a landmark window over data streams is a challenging problem. To solve the problem, this paper presents a novel algorithm (called FP-CDS) that can capture all frequent closed itemsets and a new storage structure (called FP-CDS tree) that can be dynamically adjusted to reflect the evolution of itemsets’ frequencies over time. A landmark window is divided into several basic windows and these basic windows are used as updating units. Potential frequent closed itemsets in each basic window are mined and stored in FP-CDS tree based on some proposed strategies. Extensive experiments are conducted to validate the proposed method.

CellJoin: a parallel stream join operator for the cell processor

Low-latency and high-throughput processing are key requirements of data stream management systems (DSMSs). Hence, multi-core processors that provide high aggregate processing capacity are ideal matches for executing costly DSMS operators. The recently developed Cell processor is a good example of a heterogeneous multi-core architecture and provides a powerful platform for executing data stream operators with high-performance. On the down side, exploiting the full potential of a multi-core processor like Cell is often challenging, mainly due to the heterogeneous nature of the processing elements, the software managed local memory at the co-processor side, and the unconventional programming model in general. In this paper, we study the problem of scalable execution of windowed stream join operators on multi-core processors, and specifically on the Cell processor. By examining various aspects of join execution flow, we determine the right set of techniques to apply in order to minimize the sequential segments and maximize parallelism. Concretely, we show that basic windows coupled with low-overhead pointer-shifting techniques can be used to achieve efficient join window partitioning, column-oriented join window organization can be used to minimize scattered data transfers, delay-optimized double buffering can be used for effective pipelining, rate-aware batching can be used to balance join throughput and tuple delay, and finally single-instruction multiple-data (SIMD) optimized operator code can be used to exploit data parallelism. Our experimental results show that, following the design guidelines and implementation techniques outlined in this paper, windowed stream joins can achieve high scalability (linear in the number of co-processors) by making efficient use of the extensive hardware parallelism provided by the Cell processor (reaching data processing rates of ?13 GB/s) and significantly surpass the performance obtained form conventional high-end processors (supporting a combined input stream rate of 2,000 tuples/s using 15 min windows and without dropping any tuples, resulting in ?8.3 times higher output rate compared to an SSE implementation on dual 3.2 GHz Intel Xeon).

TOPSIS: Finding Top- K significant N-itemsets in sliding windows adaptively

Frequent itemset discovery in data streams is of great importance in different applications. However, the setting of a minimum support threshold is typically hard sometimes. It is interesting for users to find the most K significant n-itemsets (1 ⩽ n ⩽ N) in sliding windows over a data stream. In this paper, algorithms TOPSIS are presented to address this problem. A sliding window is divided into several sub-windows which are called basic windows serving as updating units. Two new data structures TOPS-Tree and RELIS are devised to maintain potential significant itemsets and mined results, respectively. Moreover, three optimal strategies are exploited to reduce time and space consumption of the algorithms: (1) pruning impotent nodes in the newest basic window whenever a sliding window updates, (2) promoting support threshold during mining process dynamically, and (3) adjusting potency parameter adaptively. The precision of the algorithm are also analyzed. Experimental studies are performed to evaluate the good effectiveness and high efficiency of the algorithm.

Results of a national survey indicating information technology skills needed by nurses at time of entry into the work force.

A national survey was conducted to determine the information technology skills nurse administrators consider critical for new nurses entering the work force. The sample consisted of 2,000 randomly selected members of the American Organization of Nurse Executives. Seven hundred fifty-two usable questionnaires were returned, for a response rate of 38%. The questionnaire used a 5-point Likert scale and consisted of 17 items that assessed various technology skills and demographic information. The questionnaire was developed and pilot tested with content experts to establish content validity. Descriptive analysis of the data revealed that using e-mail effectively, operating basic Windows applications, and searching databases were critical information technology skills. The most critical information technology skill involved knowing nursing-specific software, such as bedside charting and computer-activated medication dispensers. To effectively prepare nursing students with technology skills needed at the time of entry into practice, nursing faculty need to incorporate information technology skills into undergraduate nursing curricula.

What should you do before you start to use your new computer, or once you’ve bought it, how do you keep from breaking it?

After you have purchased your new or refurbished computer and before you attempt to plug it in and turn it on, read all supplied documentation. This may be the time to ask for advice from an experienced user to assist your initial setup. Remember that your computer is a device, which is sensitive to extremes of heat, cold, magnetic fields, and rapid changes in its electrical supply. Buy and use a quality surge suppressor. A quality unit should have a Underwriters Laboratories (UL) 1449 rating of less than or equal to 330 V and be rated at 240 joules minimum. Higher quality units provide sufficient outlets to avoid the need for a web of extension cords, and some provide a jack to suppress voltage spikes transmitted through the telephone connection. If at all possible, do not plug your computer into outlets shared by other devices with a large current draw, such as heaters, air conditioners, or appliances. As you unpack your computer, carefully record all serial and model numbers of all components. Store the model and serial numbers with copies of your original receipts and documentation in a secure location. Save the boxes and packing materials in the event you need to transport the computer. Choose a location for the computer that is accessible, comfortable, and away from potential damaging agents, such as sources of water, food, and smoke. Provide an area for the central processing unit that is well ventilated and out of direct lines of traffic. A computer, like any other tool, has to be accessible or it will not be used. Electrical components are fragile, not only to environmental and power supply extremes, but also to external shocks or bumps. Just as a mere light bulb can fail if jarred while it is on, jolts and bumps can damage the sensitive components of a computer while the hard disk is spinning. Buy quality diskettes or removable media. Remember to store diskettes or removable media away from strong sources of heat or magnetic fields. Use only a felt-tipped indelible marker to write on labels, which have already been affixed to diskettes; otherwise, use ballpoint pens to write only on labels that are not on diskettes. Immediately after you have set up and turned on your computer begin to think of the inevitable day it will not work as designed. With prior planning you should be prepared for the common emergencies. Your computer may not work properly if it suffers a mechanical failure, which is fortunately uncommon. The most common difficulties can be traced to userintroduced problems, such as inadvertent configuration or setup changes, inoculation with viruses, or addition of new programs or components that change critical settings. Emergency planning begins with the creation of diskettes to allow you to start or boot your computer without the information stored on a fixed disk. Use new quality diskettes to create an emergency boot disk containing operating system information, an emergency disk to recover in the event of failure from virus infestation, and, finally, a disk to allow use of the CD-ROM drive. To create a basic Windows 98 or Windows 95 emergency boot disk:

Operational Complexity of Direct Manipulation Tasks in a Windows Environment

A method to quantify operational complexity of direct manipulation tasks in a windows environment is discussed. The method utilises a formula from communication theory due to Shannon and Weaver which describes the uncertainty H(p1,p2,...pn) in outcome of an event which is the result of a Markov process where the individual events have probabilities of occurrence P1,p2,-Pn- A taxonomy of basic windows operations is developed for Microsoft Windows and used to show that a windows dialogue can also be described as a Markov process. The H formula is then applied to determine the total complexity of a sequence of basic windows operations and thus provide some measure of the complexity of a given task as seen by the user. An estimate of the total entropy of a Microsoft Windows language source is obtained, indicating that the redundancy of windows dialogue is about 28%.