Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Causes of Poor Cost Control in Iraqi Construction Projects

Controlling cost in construction projects is an essential issue. This study investigates the most critical problems that cause weakness in cost control in Iraqi construction projects. The quantitative technique was used by conducting a survey directed to professionals who work on construction projects. One hundred and sixty-four questionnaire forms were distributed to private sector companies, government companies, and government institutions, and the responses were subjected to the required statistical analysis. The results indicate that the most influential factors are the weakness in keeping up with the use of modern concepts, methods, and technologies, the delay in receiving the amounts due for work done from the owner, fluctuations in market prices, the lack of knowledge and the inefficient use of currently available tools and technology, and the security situation in the country. The results indicate that the Iraqi construction projects suffer from weakness in cost control, which causes them to stop or delay their delivery, and this needs serious and quick solutions. One of the most important solutions is the adoption of modern technologies and their inclusion in the construction sector and the development of special systems for managing costs that are appropriate to the Iraqi construction environment.
Deepening Our Understanding of Reflective Practice in a Safeguarding Child Protection and Welfare Context

This article aims to develop a deeper understanding of reflective practice (RP) in teams in the context of a systemic approach to child protection. It highlights the inherent dichotomy of reflective practice between the very private nature of reflection and its application to the public arena of professional practice. Six social workers in the London Borough of Ealing participated in semi-structured interviews based on their experience of the structures in place to facilitate team reflections, namely daily Morning Check-in (MC) and weekly Group Supervision (GS) meetings. Findings identified four key themes: first RP as a team learning activity; second, the challenge to provide emotional support through RP within the team, third, the time commitment and last the importance of team stability. The impact on social workers’ ability to reflect in their teams is discussed using four dimensions of systemic practice; first, the use of ‘self’ in social work interventions; second, the ‘mentalisation’ of the child and their family; third, the ‘team around the worker’ support and fourth, practitioners’ reflective learning. Much more needs to be understood to effectively accommodate the private process of RP within the reality of public organisational life.
Market Assessment and Commercialization Strategy for the Radial Sandia Cooler

This market assessment and commercialization report characterizes and assesses the market potential of the rotating heat exchanger technology developed at Sandia National Laboratories (SNL), known as the Radial Sandia Cooler. The RSC is a novel, motor-driven, rotating, finned heat exchanger technology. The RSC was evaluated for the residential, commercial, industrial, and transportation markets. Recommendations for commercialization were made based on assessments of the prototype RSC and the Sandia Cooler technology in general, as well as an in-depth analysis of the six most promising products for initial RSC commercialization.
Invasion of Patchiella reaumuri (Kaltenbach, 1843) on taro roots in Asia: a biosecurity concern and new threat to indigenous taro landraces

The invasive taro root aphid, Patchiella reaumuri (Kaltenbach) (Hemiptera: Aphididae) of Western Palearctic origin, is a highly devastating pest of taro, Colocasia esculenta (L.) Schott (Family: Araceae) in the Pacific region and Europe. Consequently, P. reaumuri is considered as an important quarantine pest for taro growing regions. We detected for the first time the infestation of P. reaumuri causing 34.3% yield loss in the field and 62.4% loss in storage. This is the first taxonomically confirmed record of P. reaumuri from Asia and a global first record of its damage in storage. The northeast region of India, being a part of primary centre of origin of taro, is very rich in diversity of taro, where several traditional landraces are important dietary components of the residents, therefore, the invasion of destructive P. reaumuri may wreak havoc, if not timely diagnosed and managed. This study focussed on the detection of P. reaumuri in a new invaded region, on reporting losses caused by P. reaumuri in its new habitat, and the presentation of an illustrative morphological diagnostic and DNA barcode for its diagnosis at quarantine ports .
Carbon concentrations in natural and restoration pools in blanket peatlands

Open‐water perennial pools are common natural features of peatlands globally, and peatland restoration often results in new pool creation, yet the concentrations of different forms of aquatic carbon (C) in natural and artificial restoration pools are not well studied. We compared carbon concentrations in both natural pools and restoration pools (4–15 years old) on three blanket peatlands in northern Scotland. At all sites, restoration pools were more acidic and had mean dissolved organic carbon (DOC) concentrations in restoration pools of 23, 22, and 31 mg L−1 compared with natural pool means of 11, 11 and 15 mg L−1 respectively across the three sites. Restoration pools had a greater fulvic acid prevalence than the natural pools and their DOC was more aromatic. Restoration pools were supersaturated with dissolved CO2 at around 10 times atmospheric levels, whereas for natural pools, CO2 concentrations were just above atmospheric levels. Dissolved CH4 concentrations were not different between pool types, but were ~200 times higher than atmospheric levels. Regular sampling at one of the peatland sites over 2.5 years showed that particulate organic carbon (POC) concentrations were generally below 7 mg L−1 except during the warm, dry summer of 2013. At this regularly‐sampled site, natural pools were found to process DOC so that mean pool outflow concentrations in overland flow were significantly lower than mean inflow DOC concentrations. Such an effect was not found for the restoration pools. Soil solution and pool water chemistry, and relationships between DOC and CO2 concentrations suggest that different processes are controlling the transformation of C, and therefore the form and amount of C, in natural pools compared to restoration pools.
Assessment of the adequacy of power plant superheater dynamical model

Abstract In this contribution we apply a statistical analysis to evaluate prediction ability of the mathematical model of power plant superheater. Model evaluation by comparison of model-based predictions and measured values is used. The mean square error of prediction (MSEP) between model predictions and measured values has been used as a directly relevant measure of predictive success, with MSEP partitioned into three components to gain further insight into model performance. To analyze MSEP this paper applies next decomposition: error due to mean bias, regression bias and unexplained variation respectively. The results of analysis were used to improve the predictive ability of model.
The Effects of Firm Size and Experience on Safety and Environmental Performance in the Oil and Gas Industry, Part I: A Proposed Treatment for Sample Selection in Detection-Controlled Estimation Models

Consider a case where it is desired to estimate a detection-controlled estimation model in each of two regimes where the observations in each regime are the result of a selection process. This paper proposes a treatment for this sample selection problem for the case of a probit selection equation and Poisson/probit detection-controlled estimation model. While not investigated here, extensions to the proposed treatment for other functional forms of the selection equation and/or the detection-controlled estimation model are believed to be straightforward.
Hazard and Risk Analysis in the Process Industries

The process industries have moved progressively to larger installations with a high degree of technical sophistication, involving major investment. Because of the hazardous nature of the materials in substantial quantities in these installations, they present a hazard to human life and the environment. Over the last decade methods have been developed for the analysis of risk and hazard analysis for use in these areas. The techniques of Hazard Analysis and Operability Studies, as developed and applied internally by operating companies and Hazard Impact Assessment as used by Government Departments in environmental assessment are described in this paper. Examples are given where these techniques have been applied in Australia.
Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images

Retinal vessel images give a wide range of the abnormal pixels of patients. Therefore, classifying the diseases depending on fundus images is a popular approach. This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. Our approach includes three periods: pre-processing of the quality of input images, calculating the saliency map based on curvelet coefficients, and classifying VGG16. To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. The accuracy of the proposed method is about 98.42% and 97.96% with STARE and HRF datasets respectively.
Energy Optimization for Smart Cities Using IoT

ABSTRACT When it comes to smart cities, one of the biggest issues is energy optimization. This is because these cities employ a large number of interconnected devices to autonomously manage city operations, which consumes a lot of energy. This difficulty has been addressed in this paper by using the advantages of contemporary cutting-edge technologies such as the Internet of Things (IoT), 5 G, and cloud computing for energy efficiency in smart cities. With the use of these cutting-edge technologies, we have proposed a model that can be used to optimize energy consumption in smart homes and smart cities alike. Street lighting, building and street billboards, smart homes, and smart parking are among the four essential features of smart cities that would benefit from the proposed model’s energy savings. All smart city electric appliances will be equipped with IoT sensors that will detect movements and react to commands. In order to transport data swiftly between communication channels and the cloud, 5 G technology will be deployed, and the cloud technology will be used to store and retrieve data effectively. The suggested model was evaluated using mathematical modeling, and the findings indicate that the proposed model may assist in improving energy usage in smart cities.
How supervisor, co-worker, and spousal social support influences the experience of workload and work-family conflict: results from a survey of construction workers in Québec

Abstract This study investigates the effects of three different sources of social support (supervisor, co-workers, and spouse) on experienced workload and on work-family conflict (WFC) among a population of on-site construction workers in Québec, Canada. The findings of this study contribute to current knowledge by providing evidence of the means by which social support most influences WFC experienced by on-site construction workers (e.g. through workload modulation). More precisely, the study contributes to the literature by demonstrating that social support at work—despite its being limited by so-called masculine culture at the workplace—is instrumental to reduce the adverse effects of workload on WFC. In total, 811 workers in the Québec construction industry took part in this cross-sectional study. Results of mediation analysis show that all sources of social support reduce WFC by diminishing the perception of workload. However, even though workers report less social support from their supervisors than from their spouses, indirect effect analysis shows that supervisor social support is the source that most reduces the perception of workload and, in turn, WFC. Results of the ratio comparison indicate that supervisor and co-worker social support have more important direct impacts on WFC, whereas spousal social support has a more important indirect impact on WFC through a reduction in workload, suggesting that organizations need to foster social support at work (e.g. by promoting a culture of support) in order to ensure that work-family balance is valued at the workplace.
A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis

A Non-parametric Ensemble Empirical Mode Decomposition (NCEEMD) method is a novel technique for nonlinear and non-stationary signal analysis to detect a gearbox fault. The NCEEMD method was based on the CEEMD, but the Gaussian white noise was replaced by the fractional Gaussian noise. The NCEEMD method does not need to choose the appropriate SNR and the number of ensemble trials before signal processing, which makes it a non-parametric method. This new approach was evaluated using a simulated malfunction signal representing two typical faults in gearbox systems: modulation and rub-impact. Its performance was evaluated in terms of MSE and computation time. A comparative study between the EMD, EEMD, CEEMD, and NCEEMD methods showed that the latter performed better by improving the computation time and accuracy of CEEMD. The proposed method is a non-parametric method that provides a powerful tool in extracting the modulation and the rub-impact features from a vibration signal. The NCEEMD method helps to track down the gearbox faults and resolve this crucial problem in mechanical machines.
Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition

ABSTRACT In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, a whale optimisation algorithm (WOA) developed with a fuzzy system has been used (which is called FWOA). The reason for using the fuzzy system is the lack of correct identification of the boundary between the two stages of exploration and exploitation. Thus, the tuning of the effective parameters of the WOA is left to the fuzzy system of the Mamdani type. RBF-NN was trained by chimp optimisation algorithm (ChOA), genetic algorithm (GA), Evolution Strategy (ES), league championship algorithm (LCA), grey wolf algorithms (GWO), gravitational search algorithm (GSA), and WOA to compare the proposed algorithm. The measured criteria are convergence speed, ability to avoid local optimisation, and classification rate. The simulation results showed that FWOA with 97.49% classification accuracy rate in sonar data performed better than the other seven benchmark algorithms.
Circuit Modeling and Analysis of Wearable Antennas on the Effect of Bending for Various Feeds

The promising utilization of wearable antennas has experienced gigantic growth during the last decade. An antenna is one of the most significant and crucial components of wireless wearable devices. They need to be particularly designed to work while worn on and off the body. The wearable antenna embedded into clothing finds its use in wireless communications including tracking and navigation, mobile and wearable computing, and public safety and security. For user accessibility, there is a growing requirement for incorporating antennas on or in clothing. Determining the dielectric characteristics of the flexible substrates utilized in the design of the wearable antenna is also essential. In this paper, a Microstrip Ring Resonator (MRR) is employed to determine the dielectric properties of fabric substrates followed by state-of-the-art designs of wearable antennas and their bending effects at ISM band frequencies. An electrical equivalent model is designed to realize the potentials inside the geometry of an antenna under bending environment. This is followed by observing the effect of bending for different feeding methods on the wearable antenna's parameters when bending on a certain radius. The robustness of the proposed wearable antenna is examined by measuring the antenna under various bending curvatures for return loss, gain, and efficiencies. This will disclose the various contemplations for designing a wearable antenna from different feeding mechanisms with different materials and exemplifying the antenna's outcomes to dynamic moments of the human body. The performance of the proposed wearable antenna is acceptable even in a deformation environment, and there is a good agreement between the measured and the simulation results.
A Survey of Methods for Improving Ultrasonic Ranging Precision

With the wide application of ultrasonic ranging,requirement for the ranging precision is higher and higher.How to improve the precision of ultrasonic ranging has become the research focus.This paper summarizes the methods of improving ultrasonic ranging precision that have been applied or recognized,and classifies the methods according to the factors influencing the ultrasonic ranging precision.
Wideband radar cross-section reduction by AMC

A new planar wideband chessboard structure is designed to reduce the radar cross-section (RCS) of an object. This proposed chessboard configuration is composed of two different artificial magnetic conductor (AMC) tiles. One of these tiles is formed by saltire arrow unit cells, whereas the other one is formed by four E-shaped ones. A 180° ± 30° phase difference between these tiles achieved by precise design of the AMC unit cells of more than 9.4–23.28 GHz (85%) reduces the RCS by more than 10 dB. The fabricated board results show very good agreement with the simulated ones, which verify the ability and capability of the proposed structure.
A COMPARATIVE STUDY ON CYBER SECURITY THREATS DETECTION IN INTERNET OF THINGS

Internet of Things (IoT) is an evolving digital technology, which is mainly meant to bridge physical and virtual world. New business model has been emerged because of people, objects, machines and Internet connectivity along with new interactions amid humanity and remaining world. IoT is considered as a gateway for cyber-attacks since various resources such as systems, applications, data storage, and services are connected through IoT that relentlessly provide services in the organization. IoT security is challenging factor due to prevailing software piracy and malware attacks presently. The economic and reputational damages are caused by these threats due to crucial information burglary. IoT malware detection is yet another challenging factor due to security design deficiency besides IoT devices specific characteristics such as processor architecture heterogeneity, particularly on identifying cross-architecture IoT malware. Hence, IoT malware detection area is main objective of this research by security community recently. The familiar dynamic or static analyses to detect IoT malware is greatly deployed in various researches with its benefits. A systematic review relating to latest research studies and technologies of classical, Deep Learning (DL) and Machine Learning (ML) methodologies for cyber security threats recognition are outlined and are view is given in this paper. Every approach pertaining to its objective, approach and outcomes have been examined for every selected work. Deep Learning (DL) approach is greatly utilized for malware infected files and pirated software recognition in IoT network in cloud. Several software piracy and malware attack detection methods has been analyzed in this paper with respect to its advantages and disadvantages. The source code plagiarism is detected through DL methodology and dataset collection is done from Google Code Jam (GCJ) for software piracy investigation. Rather than this, Deep Convolutional Neural Network (DCNN) is mainly involved in identifying malicious infections in IoT network. Mailing dataset is utilized for obtaining malware samples which is used for experimental purpose. It is thereby substantiated that suggested method namely Tensor Flow Deep Neural Network (TF-DNN) classification performance for assessing cyber security threats in IoT are enhanced when compared with classic approaches such as Support Vector Machine (LBP+SVM), Gray Level Cooccurrence Matrix with Support Vector Machine (GLCM+SVM) pertaining to F-measure (F1) and Classification Accuracy (CA).
Post closure human health and ecological risk assessment at Teck Cominco's Kimberley operations, BC : overview of regulatory process and findings of the problem formulation

The Sullivan sulphide ore body was discovered in 1892; Teck Cominco Metals Ltd. (Teck Cominco) and its predecessors operated underground lead/zinc mining, milling, and other industrial operations from 1909 until December 2001. The total area of land disturbance (mine, associated facilities and waste impoundments only) is on the order of 1,090 ha. Since the 1960s, Teck Cominco has been planning and implementing measures to restore and/or protect the environment impacted by mining activity. In 1991, at the request of the BC Ministry of Energy and Mines, Teck Cominco developed a comprehensive Decommissioning and Closure Plan that is being implemented under Mines Act Permit M-74. The BC Ministry of Water, Land and Air Protection required site investigations and remediation to address contaminants and impacts not addressed in the Closure Plan (e.g., hydrocarbons). Remediation has included contaminant removal (where possible), and risk assessment/risk management, to meet Teck Cominco’s post closure objectives. The main contaminants are metals generated directly from mining activity (e.g., waste rock piles, tailing impoundments, fugitive dust) and from subsequent releases due to acid rock drainage. The Risk Assessment (RA) Problem Formulation (PF) for human, terrestrial and aquatic components is complete, and the RA is nearing completion. The PF process was successful in screening out receptor/contaminant combinations where negligible risks were predicted using conservative assumptions, and in identifying specific receptor/contaminant combinations that require additional investigation. The St. Mary River and Mark Creek are identified as primary receiving environment components and are key drivers of the RA. A hydrogeological and geochemical assessment of upgradient waste impoundments is being conducted concurrently with the RA to allow predictions of post closure groundwater and surface water concentrations. This paper focuses on the environmental regulatory framework, and the challenges faced due to two parallel regulatory processes under the Mines Act and Waste Management Act. An overview is also provided regarding the approach taken to conducting this RA at a large mine site where both anthropogenic activities and natural mineralization have resulted in elevated metals concentrations.