Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining a Research Agenda for AEC

The call for whitepapers issued by the organizers of this workshop asks for participants to address two main areas: • Identification of the research agenda for AEC • Prioritization of the research needs and measurement of achievements The process of defining a research agenda for any industry should be a combination of methods and the methods must aim to ensure the comprehensiveness of the topics suggested. The previous NSF CE&M workshop [1] has dedicated (almost) a whole chapter (chapter 4) to identifying research topics in construction. The participants adopted the brainstorming method to generate a research agenda. Given the way this current workshop is organized, I think that two additional approaches should be considered: problem analysis and cross-fertilization. While the three approaches are not disjunctive, I think that a wider range of solutions can be generated when each approach is applied separately. This paper presents three research topics: constructability modeling, wearable robotic machines, and development of better methods of construction education. Each topic was determined using one of the three approaches. PROBLEM ANALYSIS / CONSTRUCTABILITY MODELING With the exception of " cookie cutter " residential developments, all construction projects are, even if only to some small extent, different. Each construction project is a prototype with specific objectives and constrains such as time, money, quality, labor (amount and skills), safety policy, etc. The success of a project depends on the knowledge and experience of the participants in planning, design, procurement and field operations. Hence the main challenge of the construction industry is to identify, use (and) further develop the knowledge and experience of the participants in the construction process. The optimum use of construction knowledge and experience in planning, design, procurement, and field operations to achieve overall project objectives is defined by CII as " constructability " [2]. The constructability problem can be defined as " achieving the goals of the owner within the constrains (limitations) of the builder. " Constructability research is not new to the AEC community. Three different research branches of constructability can already be identified: • Human-oriented research. Includes managerial and organizational approaches. In this case the constructability knowledge is assumed to lie within the participants in the process and the main problem is timing their participation in the project. Most of the constructability research done to the day is related to this branch. Among the more than forty papers published in this area, the CII Constructability …
Antimicrobial Susceptibility in the Mycobacteroides abscessus Complex is Restored by an Imipenem-Clarithromycin Combination

Nontuberculous mycobacteria (NTM) are ubiquitous organisms and the incidence of NTM infections has been increasing in recent years. Mycobacteroides abscessus (M. abscessus) is one of the most antimicrobial-resistant NTM; however, no reliable antibiotic regimen can be officially advocated. We evaluated the efficacy of clarithromycin in combination with various antimicrobial agents against the M. abscessus complex. Twenty-nine clinical strains of M. abscessus were isolated from various clinical samples. Of the isolates, 10 (34.5%) were of M. abscessus subsp. abscessus, 18 (62.1%) of M. abscessus subsp. massiliense, and 1 (3.4%) of M. abscessus subsp. bolletii. MICs of three antimicrobial agents (amikacin, imipenem, and moxifloxacin) were measured with or without clarithromycin. The imipenem-clarithromycin combination significantly reduced MICs compared to clarithromycin and imipenem monotherapies, including against resistant strains. The association between susceptibility of the M. abscessus complex and each combination of agents was significant (p = 0.001). Adjusted residuals indicated that the imipenem-clarithromycin combination had the synergistic effect (adjusted residual = 3.1) and suppressed the antagonistic effect (adjusted residual = -3.1). In subspecies of M. abscessus complex, the association with susceptibility of M. abscessus subsp. massiliense was similarly statistically significant (p = 0.036: adjusted residuals of synergistic and antagonistic effect respectively: 2.6 and -2.6). The association with susceptibility of M. abscessus subsp. abscessus also showed a similar trend but did not reach statistical significance. Our data suggest that the imipenem-clarithromycin combination could be the recommended therapeutic choice for the treatment of M. abscessus complex owing to its ability to restore antimicrobial susceptibility.
The impact of human performance variation on the accuracy of manufacturing system simulation models

The research described in this thesis is concerned with human performance modelling as an aid in the process of manufacturing systems design and re-design. Most manufacturing systems are highly complex constructs and their behaviour is of a dynamic and stochastic nature. They have to be constantly designed and re-designed as organisations are continually being pressured to change their manufacturing facilities, technologies, methods, people and products. All design methods have some form of evaluation where discrete event simulation models are usually used to undertake a comparative analysis of different system designs. Within these discrete event simulation models it is common practice to represent workers as simple resources, often using deterministic performance values. Conversely, the work measurement literature indicates that worker task performance varies between different workers carrying out the same task and moreover for the same worker when repeating a task. The current approach of representing workers within discrete event simulation models ignores the potentially large effect that human performance variation can have on system performance. This omission affects in particular simulation models of labour intensive manufacturing systems like manual assembly flow lines. It appears that this adds to the inaccuracy of the simulation model output and that consequently the simulation model does not reflect the behaviour of a real system in an appropriate way. A research programme has been designed to investigate these issues. First, a long term data collection exercise has been conducted to quantify the performance variation of workers in a typical automotive manual assembly flow line. The data have then been used in form of frequency distributions to represent worker performance variation at individual workstations within manual assembly line simulation models. Through designed simulation experiments the impact that this form of worker performance variation representation has on the accuracy of manual assembly line model behaviour has been investigated. Overall this research has found that adding worker performance variation models into manual assembly flow line models has an impact on the accuracy of these simulation models. The magnitude of the impact depends very much on the type of variation to be represented as well as on the system to be modelled. This evidence is an important result to support justification for further research in this area. For a more sophisticated approach of modelling worker performance Computational Organisation Theory using the multi-agent paradigm has been identified as the most suitable way forward.
User’s guide to SNAP for ArcGIS® :ArcGIS interface for scheduling and network analysis program

This document introduces a computer software named SNAP for ArcGIS® , which has been developed to streamline scheduling and transportation planning for timber harvest areas. Using modern optimization techniques, it can be used to spatially schedule timber harvest with consideration of harvesting costs, multiple products, alternative destinations, and transportation systems. SNAP for ArcGIS attempts either to maximize a net present value or minimize discounted costs of harvesting and transportation over the planning horizon while meeting given harvest volume and acreage constraints. SNAP for ArcGIS works in the ArcGIS environment and provides an easy-to-use analytical tool for sophisticated spatial planning of timber harvest.
THE RELATIONSHIP BETWEEN STUDENTS’ SELF-EFFICACY IN LEARNING ENGLISH AND THEIR ENGLISH LEARNING ACHIEVEMENT AT THE TENTH GRADE OF SMAN 04 SOUTH TAMBUN IN 2019/2020 ACADEMIC YEAR

The objective of this study was to find out the relation between students’ self-efficacy belief in learning English and their English learning achievement of the tenth grade students of SMAN 4 South Tambun in 2019/2020 academic year. This study used correlational study between two variables which was included in quantitative research. In this study, the writer took one class of tenth grade students which consist of 36 students as the sample. The writer found the correlation coefficient (r) result between students’ self-efficacy in learning English and their English learning achievement which was 0,628. Afterward, the writer compared r result to the interpretation of correlation table and showed there was positive correlation. Thus, It proves that self-efficacy belief encourages and helps students to improve their skills and self-confidence, and finally achieve better learning outcomes. Keywords: Self-Efficacy, Learning, Learning Achievement, English Learning Achievement.
Diagnostic Value of Contrast-enhanced Digital Mammography Versus Contrast-enhanced MRI for Detecting Residual Disease after Neoadjuvant Chemotherapy for Breast Cancer

Background: Preoperative evaluation of breast cancer using contrast-enhanced digital mammography (CEDM) as a possible alternative to contrast-enhanced magnetic resonance imaging (CEMRI) has gained acceptance. Our aim is to compare the diagnostic performance of CEDM and CEMRI for chemotherapeutic response in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). Methods: From November 2017 to October 2018, 30 patients with invasive carcinoma who performed both CEDM and CEMRI were included. Residual malignancy sizes after NAC were compared with histopathological results. The diagnostic performances for detecting residual cancers were compared using Lin concordance and Pearson correlation coefficients.Results: Thirty patients were included for the analysis. Mean tumor size after NAC was 1.22 cm (range: 0–7.0 cm) for CEDM and 1.13 cm (range: 0–5.1cm) for CEMRI compared with 1.89 cm (range: 0–12.0 cm) at final pathology measurement. Sensitivity for identifying residual lesion was for CEDM and CEMRI is as follows (CEDM 62.5%, 95% CI 40.6–81.2, CEMRI 66.7%, 95% CI 44.7–84.4). The positive predictive value (PPV) for residual lesion was 93.8% (95% CI 69.8–99.8) for CEDM and 88.9% (95% CI 65.3–98.6) for CEMRI. CEDM had a mean difference from pathology measurement of 0.668 cm, with a concordance coefficient of 0.202 and a Pearson correlation coefficient of 0.231 (p=0.220). Conclusions: Diagnostic value of CEDM for detecting residual tumor extent after NAC was comparable to that of CEMRI.
Caudate transcriptome implicates decreased presynaptic autoregulation as the dopamine risk factor for schizophrenia

Increased dopamine (DA) signaling in the striatum has been a cornerstone hypothesis about psychosis for over 50 years. Increased dopamine release results in psychotic symptoms, while D2 dopamine receptor (DRD2) antagonists are antipsychotic. Recent schizophrenia GWAS identified risk-associated common variants near the DRD2 gene, but the risk mechanism has been unclear. We performed RNA-sequencing in postmortem caudate nucleus from 444 individuals and identified many new genes associated with risk for schizophrenia through genetic modulation of expression. Genetic risk for schizophrenia is associated specifically with decreased expression of the short isoform of DRD2, which encodes the presynaptic autoreceptor, and not with expression of the long isoform postsynaptic receptor. These data implicate decreased control of presynaptic DA release as a genetic mechanism of schizophrenia risk. Using a new approach based on deep neural networks, we construct caudate gene expression networks that highlight interactions involving schizophrenia risk genes and uncover potential novel therapeutic targets.
Metrics for Emitter Selection for Multistatic Synthetic Aperture Radar

Abstract : A bistatic implementation of synthetic aperture radar (SAR) to form images of the ground from an aircraft makes use of separate emitters and receivers. When not using cooperative emitters, ground based communications systems can provide illumination. One way to improve performance of these waveforms, which are not designed for SAR, is a multistatic implementation, formed from multiple bistatic systems. This leads to the problem of selecting a subset from a potentially large set of emitters to use for image formation. A framework for this selection between sets of emitters is proposed using multiple objective optimization. This approach requires use of objective functions to score the inputs to the selection process. The four objective functions selected to score sets of emitters are: signal to noise ratio, waveform ambiguity function's integrated sidelobes , effective multistatic resolution area, and contrast ratio. To speed calculations, an approximation is found for the point spread function. Simulation is used to compare approximation with theory, showing its utility for emitter selection. Finally a qualitative example of emitter selection is presented.
A unit testing approach to building novice programmers' skills and confidence

This paper discusses the integration of unit tests into a first semester programming course. The students were supplied with unit tests to support their learning and assessments. A questionnaire was completed by the student cohort about their use and perceptions of these unit tests. As a result of both the students and our experiences we examine the advantages and disadvantages of introducing unit tests early and make some pedagogical recommendations for the introduction and use of unit tests in first year programming.
Decentralized control design approaches for formation control of unmanned aerial vehicles

The leader follower type formation of Unmanned Aerial Vehicles usually demands decentralized yet co-operative control among the vehicles. The decentralized control approach is superior to centralized control in view of lesser involvement of delay, minimal information sharing requirement, reduced computational effort for controller design etc. The dynamic model of leader follower formation with an information structure constraint, in which each vehicle except the leader have the information of all the states of vehicle in front of it. The formation is treated as an interconnected system with overlapping control gains in the sense an UAV share information only with its neighbouring ones.In this thesis, two approaches are used: (i) Inclusion principle (ii) Graph theory based approach for designing control gains. In the inclusion principle approach, control gain is designed separately for each disjoint subsystem in the expanded space. The static state feedback control law and linear matrix inequalities tool boxes are used for designing the controllers for each subsystem. Finally decentralized controllers are contracted back so as to be applied to the original system. In the graph theory approach, an overlapping information flow structure is constructed that determines the outputs of the system available in constructing any input signal of the system. The Graph theory is used to transform the overlapping interconnected system to decentralized one. The static state feedback type controller is used and a DK iterative algorithm is used to find out control gain. Then, a comparison between these two decentralized approaches is reported in the thesis so as to obtain the relative merits and demerits. There is delay in information flow form leader to follower in the formation so frequency domain stability analysis is done for time delay system. Frequency sweeping test is conducted for getting maximum tolerable communication delay between any two UAVs.
MODEL, FRAMEWORK, AND PLATFORM OF HEALTH PERSUASIVE SOCIAL NETWORK

Persuasive technology (PT) has the potential to support individuals to perform self-management and social support as a part of health behavior change. This has led a few researchers in the intersection of the areas of health behavior change and software engineering to apply behavior change and persuasion theories to software development practices, enabling them to create innovative design principles and development-evaluation frameworks. Unfortunately these are too general for designing and evaluating health PT. Therefore, this dissertation proposes a model, framework, and platform of PT specifically designed for health intervention. The model and framework inform what, why, and how conceptually the suggested and required health behavior change strategies should be transformed into system features; and the platform explains how technically the transformation should be done. The platform includes functional requirements and provides most of the basic and standard computer code to develop the system features of such PT. The model, framework, and platform were designed to work with various health behavior change programs. Nevertheless, in this dissertation, they support health behavior change for physical activity. As an implementation of and tool to evaluate the model, framework, and platform, a technology called Persuasive Social Network for Physical Activity (PersonA) is introduced. PersonA is a combination of automatic input of physical activity data, a smart phone, and social networking. Two systems (SocioPedometer and PAMS) as leverages of PersonA have been developed and evaluated. The model, framework, and platform were evaluated based on the results of SocioPedometer’s usability testing and 4-week trials (n=14) and on PAMS’s usability testing (n=5). The results suggest that the systems were usable and accessible and that users were satisfied and enjoyed using it. Additional evaluations to the model and framework were conducted with the main purpose of eliciting users’ preferences with respect to the characteristics and system features proposed in the model and framework. They rated most of the characteristics as extremely important (average 4.27 of a 5.00 maximum) and most of the system features as very important (average of 4.09). The platform allowed the two systems to be easily developed by customizing the data input and information presented.
Stock Market Prediction and Risk Analysis using NLP and Machine Learning

The stock market has been an instrument of investment for more than 200 years now. The price movements in the stock market have been an enigma for many financial analysts and they have tackled this problem with very little success. The phenomenal advancement in technology led to increased storage systems, higher processing speed and better algorithms. Thus, it is more possible now to develop a system for predicting stock markets. People have this taboo that only big investors can profit from the stock market and it is a trap for retail investors or small players. The solution we propose here is easy to understand and implement. We first do the sentiment analysis of the selected stock and then suggest whether to buy, sell or hold. Secondly, we calculate the maximum risk involved in the investment using a threefold approach; market risk, sector risk and stock risk. Finally, using Support vector Regression (SVR) with three different approaches, we calculate expected return and compare them with actual returns.
Assessing the Ability of Fog Seals to Seal Pavements, to Rejuvenate in Situ Binder, and to Retard Binder Oxidation

This work was conducted for the purpose of assessing the effectiveness of fog seal treatments as an aid to highway maintenance managers in making sound decisions for fog seal treatments. Replicate cores of both treated and untreated highway and general aviation pavement sections were analyzed. Whole cores were assessed by water permeability and by susceptibility to permanent deformation. Replicate cores were sawed into one-quarter inch slices which were individually analyzed for total air voids, accessible air voids, binder content, oxidative aging and rheology, and for the presence of fog seal material. The fog seal materials used in this project were emulsions of asphalt materials and coal tar type materials. Results showed that if the fog seal is penetrating into the pavement, it is not doing so to a detectable level, the permeability of the pavement is not significantly reduced, and APA tests did not show any softening of the pavements by the treatments. Additionally, 1) differences between untreated and treated pavement slices generally seem more likely due to original binder variability with depth than to the fog seal treatments, although coal-tar treatments appear to harden the top layer; 2) effects of the fog seal treatments on oxidative aging were not observed; and 3) previous work, that the aging rates of asphalt binders are decreased by very low accessible air voids is supported. In summary, the effects of fog seals on pavement durability appear to be minimal, with respect to binder rejuvenation or sealing. Cosmetic effects or protecting against shelling or raveling remain as possible benefits, although they were not assessed by this project. In response to this work, engineers should reassess the cost-benefit balance of fog seal treatments.
Pseudo Convex Framework using Sparse Channel Estimation for Multipath Fading Channels in DS-CDMA Systems

A Direct-Sequence Code-Division Multiple-Access (DS-CDMA) with rake receiver for multiuser system sets up a new dimension in mobile communication system. We propose a pseudo convex framework using an optimum sparse channel estimation technique for DS-CDMA mobile communication systems. Further, the blind channel estimation problem will be examined for rake-based DS-CDMA communication framework with time variant multi-path fading channels This receiver accomplishes an effective assessment of channel according to a maximum convexity criterion, by means of the sparse technique. This estimation method requires a convenient representation of the discrete multipath fading channel based on the sparse theory. In this paper, we have defined a specialized interior-point method for solving large-scale ℓ1 - problem in multiuser detection. Our method can be generalized to handle a variety of extensions such as various channel conditions. A new solution to DS-CDMA based sparse channel estimation is presented in this paper that assures a global optimal solution. Also, it is proven that the said solution can be used as an apparent program that shall enable solutions utilizing interior point techniques involving polynomial time complexity. Through simulation the rationality of the techniques proposed in this paper has been highlighted by results obtained for various modulation schemes and channel parameters. The execution of a pseudo convex framework using sparse channel estimation technique with rake receiver in DS-CDMA framework for multipath fading channels is explored. The overall performance is evaluated in terms of bit error rate (BER) for a range of values of signal to noise ratio (SNR). This framework gives better performance under various modulation schemes using pseudo noise (PN) spreading code. Furthermore, performance of the proposed system is compared with different detectors.