Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the Impacts of Safety Knowledge on Construction Workers' Attentional Allocation and Hazard Detection Using Remote Eye-Tracking Technology

Although several studies have highlighted the importance of attention in reducing the number of injuries in the construction industry, few have attempted to empirically measure the attention of construction workers. One technique that can be used to measure worker attention is eye tracking, which is widely accepted as the most direct and continuous measure of attention because where one looks is highly correlated with where one is focusing his or her attention. Thus, with the fundamental objective of measuring the impacts of safety knowledge (specifically, training, work experience, and injury exposure) on construction workers’ attentional allocation, this study demonstrates the application of eye tracking to the realm of construction safety practices. To achieve this objective, a laboratory experiment was designed in which participants identified safety hazards presented in 35 construction site images ordered randomly, each of which showedmultiple hazards varying in safety risk. During the experiment, the eye movements of 27 construction workers were recorded using a head-mounted EyeLink II system. The impact of worker safety knowledge in terms of training, work experience, and injury exposure (independent variables) on eyetracking metrics (dependent variables) was then assessed by implementing numerous permutation simulations. The results show that tacit safety knowledge acquired from work experience and injury exposure can significantly improve construction workers’ hazard detection and visual search strategies. The results also demonstrate that (1) there is minimal difference, with or without the Occupational Safety and Health Administration 10-h certificate, in workers’ search strategies and attentional patterns while exposed to or seeing hazardous situations; (2) relative to less experienced workers (<5 years), more experienced workers (>10 years) need less processing time and deploy more frequent short fixations on hazardous areas to maintain situational awareness of the environment; and (3) injury exposure significantly impacts a worker’s visual search strategy and attentional allocation. In sum, practical safety knowledge and judgment on a jobsite requires the interaction of both tacit and explicit knowledge gained through work experience, injury exposure, and interactive safety training. This study significantly contributes to the literature by demonstrating the potential application of eye-tracking technology in studying the attentional allocation of construction workers. Regarding practice, the results of the study show that eye tracking can be used to improve worker training and preparedness, which will yield safer working conditions, detect at-risk workers, and improve the effectiveness of safety-training programs.DOI: 10.1061/(ASCE) ME.1943-5479.0000526. © 2017 American Society of Civil Engineers.
Energy-Efficient High-Speed ASIC Implementation of Convolutional Neural Network Using Novel Reduced Critical-Path Design

Convolutional Neural Network (CNN) plays an important role in several machine learning tasks related to speech, image, and video processing applications. The increasing demand for faster processing in real-time applications requires high-speed implementation of CNN. However, in general, CNN involves higher latency due to the computationally intensive behavior of the convolutional layer. While state-of-the-art architecture provides efficient dataflow of the convolutional operations, this paper proposes a hardware-efficient, high-speed convolution block for ASIC implementation of the CNN algorithm. The proposed convolution block is designed using a novel bit-level-multiply-accumulator (BLMAC) with a modified Booth encoder and a Wallace reduction tree. The critical path of the overall architecture is significantly shortened due to the time-optimized implementation of the proposed BLMAC, which is a main component of the convolution process. Critical path analysis and own dataflow strategy are also provided to demonstrate the acceleration of the proposed design. The proposed architecture was synthesized using Synopsys Design Compiler to prove accelerated processing power. The ASIC synthesis results of the proposed architecture using a 65nm standard cell library show at least 53.2% reduction in latency, 52.2% reduction in area-delay product, and 54.2% reduction in power-delay product compared to the state-of-the-art architecture.
Investigating the effects of host trust, credibility, and authenticity in podcast advertising

ABSTRACT After decades of catering to niche audiences, podcasting is now recognized as a booming industry with more than 100 million Americans tuning in every month. Moreover, marketers acknowledge that podcast hosts are becoming trusted influencers with loyal communities, offering greater opportunity for consumer engagement and positive brand outcomes. This qualitative study examined open-ended survey responses associated with a 2 × 2 experimental study (N = 330) to investigate the degree to which podcast hosts cultivate trust, credibility, and authenticity with their audiences; as well as how parasocial relationships with podcast hosts ultimately influence listeners’ brand attitudes and behaviors. Insights from this study extend Parasocial Interaction theory to a new and emerging context, as well as help marketers better understand how to engage more effectively with podcast audiences.
Seismic risk assessment of steel frames equipped with steel panel wall

This paper aims to conduct risk assessment on steel frame equipped with steel panel wall (SPWF) through probabilistic seismic demand analysis. First, cyclic test on a SPWF specimen with one‐third scale, one bay, and single story is performed, and the typical limit performance levels were determined in accordance with the test results and the stipulated in FEMA 356. Then, finite element models were built for a 12‐story steel frame and two 12‐story SPWF structures with different lateral stiffness ratio. The seismic performance of these models are investigated through nonlinear time–history analyses, and their limits capacities are determined from incremental dynamic analyses. Besides, fragility functions are developed for these models associated with 10%/50 years and 2%/50 years events, as defined in SAC project. Finally, the annual probabilities of each limits and the collapse probabilities in 50 years for the 3 models are calculated and analyzed, and the function associated with the collapse probability and lateral stiffness ratio is developed. The effectiveness of steel panel wall in reducing the seismic risk of the existing steel frame buildings is validated on the basis of the risk analyses.
Diagnostic Advances in Childhood Tuberculosis—Improving Specimen Collection and Yield of Microbiological Diagnosis for Intrathoracic Tuberculosis

There is no microbiological gold standard for childhood tuberculosis (TB) diagnosis. The paucibacillary nature of the disease, challenges in sample collection in young children, and the limitations of currently available microbiological tests restrict microbiological confirmation of intrathoracic TB to the minority of children. Recent WHO guidelines recommend the use of novel rapid molecular assays as initial diagnostic tests for TB and endorse alternative sample collection methods for children. However, the uptake of these tools in high-endemic settings remains low. In this review, we appraise historic and new microbiological tests and sample collection techniques that can be used for the diagnosis of intrathoracic TB in children. We explore challenges and possible ways to improve diagnostic yield despite limitations, and identify research gaps to address in order to improve the microbiological diagnosis of intrathoracic TB in children.
Does Including Momentum Factor Into Fama-French Five-Factor Model Predict Better Return In Indonesia?

The addition of momentum factor to the Fama-French Five-Factor Model in recent studies encourages investors to reconsider their investment strategies. The aim of this research is to compare the explanatory power of the Fama-French Five-Factor Model Plus Momentum (FF5F Plus Momentum) and the Fama-French Five-Factor Model (FF5F) to portfolio return of stocks listed in the Kompas100 Index over the 2010-2019 period. This research also aims to describe the relation and significance of each factor (market risk, size, book-to-market equity or value, profitability, investment, and momentum) in the FF5F Plus Momentum model to the excess return. This research uses the Ordinary Least Square method for multiple linear regression on 115 observations done to the 24 portfolios formed. The research finds that the market risk premium, size and book-to-market equity have positive and significant effects on return, profitability and investment have no significant effect on return and momentum has negative and significant effect on return. FF5F Plus Momentum model is found to have higher explanatory power than the FF5F model. This result suggests investors to consider the stocks in the Kompas100 Index that have small market capitalization, high book-to-market equity and small yearly return in the previous one-year period for their portfolio
Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China

Existing obesity- and lipid-related indices are inconsistent with metabolic syndrome (MetS) in chronic kidney disease (CKD) patients. We compared seven indicators, including waist circumference (WC), body mass index (BMI), visceral fat area (VFA), subcutaneous fat area (SFA), visceral adiposity index (VAI), Chinese VAI and lipid accumulation product (LAP), to evaluate their ability to predict MetS in CKD patients with and without Type 2 diabetes mellitus (T2DM) under various criteria. Multivariate logistic regression analysis was used to investigate the independent associations between the indices and metabolic syndrome among 547 non-dialysis CKD patients, aged ≥18 years. The predictive power of these indices was assessed using receiver operating characteristic (ROC) curve analysis. After adjusting for potential confounders, the correlation between VAI and MetS was strongest based on the optimal cut-off value of 1.51 (sensitivity 86.84%, specificity 91.18%) and 2.35 (sensitivity 83.54%, specificity 86.08%), with OR values of 40.585 (8.683–189.695) and 5.076 (1.247–20.657) for males and females with CKD and T2DM. In CKD patients without T2DM, based on the optimal cut-off values of 1.806 (sensitivity 98.11%, specificity 72.73%) and 3.11 (sensitivity 84.62%, specificity 83.82%), the OR values were 7.514 (3.757–15.027) and 3.008 (1.789–5.056) for males and females, respectively. The area under ROC curve (AUC) and Youden index of VAI were the highest among the seven indexes, indicating its superiority in predicting MetS in both male and female CKD patients, especially those with T2DM.
Analysis of Factors Affecting Intent to Use Mobile Commerce Services in India

India seems to lead the shift from e-commerce to mobile commerce adoption. Apps and mobile websites are offered in various areas of health, travel, shopping, ticketing, and lifestyle, promising enormous opportunities for consumers and businesses. This study identifies the critical factors affecting m-commerce transaction decisions and explaining their relevance. The significant moderating effect of demographics and types of mobile commerce services namely: banking, ticket booking and shopping services are analyzed on users’ behavioral intention in a single framework. The data collection done through questionnaire required the respondents to describe their behavior during their most recent online transactions, in terms of UTAUT model dimensions in three specific areas m-banking, m-ticketing, and m-shopping. Structured equational modeling was employed to analyze the causal association between variables. The research implications will help in understanding the factors affecting the acceptance levels of various m-commerce services by the consumers in India.
Innovation Models In The Biopharmaceutical Sector

AbstractThe innovation process in the biopharmaceutical sector is influenced by long business cycles, multiple stakeholders and complex interactions. Early models of the innovation process are inadequate to capture the complexity of innovation in the life sciences sector. In particular, narrow classifications which describe innovations as “radical” or “incremental” are not particularly useful when considered in the context of the complex patterns of interrelated innovations observed in practice.Many partial models of the innovation process which equate innovation to inventive research, patenting and product development fail to recognise that innovation is a cyclical and business-driven process and underscore the final phase of the innovation process, namely, achieving timely market diffusion and adoption of innovations to benefit patients and innovators. Innovation is sustained if it is appropriately rewarded. Investment in the science base alone without appropriate reward system for innovations is unlikely to promote renewed competitiveness in the European biopharmaceutical industry.
Research on the Model of Industrial Interconnection Intelligent Manufacturing Supply and Demand Network and Its Robustness

The industrial interconnection intelligent manufacturing supply and demand network is the carrier for the cooperation of related enterprises, and research on its evolution helps to optimize the cooperation between enterprises. This paper considers the factors that affect enterprise cooperation, applies consensus, disconnection, reconnection, and change of opinion into one model, and constructs two models of industrial interconnection intelligent manufacturing supply and demand networks. Because the industrial interconnection intelligent manufacturing supply and demand network is affected by certain factors, the network will be subject to fluctuations and the functions of the network will change. The smaller the function change, the higher the robustness of the model. The related topological structure statistics (such as clustering coefficient, average path length, and degree distribution) of the two models are analyzed, and the analysis results indicate that the network structure of the two models has changed. In addition, different attack algorithms are used to verify the robustness of the model. The experimental results show that under the four different attack algorithms, compared with the BA model, the two models proposed in this paper have stronger robustness. Research on the industrial interconnection of intelligent manufacturing supply and demand networks and its robustness optimization has certain guiding significance.
3D Graphite–Polymer Flexible Strain Sensors with Ultrasensitivity and Durability for Real‐Time Human Vital Sign Monitoring and Musical Instrument Education

The design and fabrication of various types of flexible, portable, and foldable devices have received immense interest owing to the remarkable potential in impacting peoples' lives including real‐time health monitoring, point‐of‐care diagnosis, and athletic training. In this work, the authors present 3D graphite as the key sensing element of polymer composite strain sensors that offers ultrahigh sensitivity and durability in the detection of fine motions. The graphite–polymer sensors in this work provide high bending sensitivities that are reproducible within 3% signal shift after 11 000 bending cycles and exhibit gauge factors of 100 and 52 at tensile strains of 80% and 100%, respectively. The sensing mechanism is modeled, and correlated with experimental studies. The high strain sensitivity compared to graphene based devices is analyzed and understood with respect to levels of defect in materials. Such graphite–polymer sensors are able to detect fine features of human pulses, respiration rates, and throat vibration in real time and are also applied in the detection of posture correctness of musical instrument learners for the first time.
Post-quake structural damage evaluation by neural networks: theory and calibration

Abstract This paper deals with artificial neural networks (ANN) for post-quake structural damages evaluation. Two groups of governing parameters are considered: the structural components group and the secondary components group. The adequate ANN requests the analysis of: the best combination of the components’ damages which governs the global damage of a building; the neural network parameters, i.e. number of hidden layers and neurons as well as the activation functions. A set of 3614 damaged buildings is extracted from a database collected during a post-quake survey by trained staff (Boumerdes, Algeria: M w = 6.8; May 21, 2003 earthquake). The comparison between predicted and observed damages shows that the best ANN corresponds to: one hidden layer with a number of neurons equal to the number of main building components (i.e. 4 or 8 components), and the “hyperbolic tangent” function as activation function. For the collected database, the ANN’s predictions and the observed global damages are in accordance for: 70% of the considered buildings when the global damage is assumed to be influenced only by the secondary (non-structural) elements group, 80% of the considered buildings when the global damage is assumed to be influenced only by the structural group, 87% of the considered buildings when the global damage is assumed to be influenced by both building components groups, i.e. structural and secondary components.
Biopesticides Analysis: An Editorial

Volume 4 • Issue 2 • 1000e115 J Biofertil Biopestici ISSN:2155-6202 JBFBP, an open access journal It is generally acknowledged that the use of pesticides has resulted in increase in crop yield and helped decisively the agriculture sector. Notwithstanding, a rising concern in recent years about excessive usage of pesticides and their metabolites and impact on nature and human-aquatic organisms health is triggered. In this context organic agriculture is a production system that tends to circumvent the use of synthetic pesticides, fertilizers, and other additives [1]. For this reason natural products have been studied extensively as sources for new pesticides [2]. In US the organic section of the food industry has been emerging strongly and had an average annual sales growth rate of 20% for many years [3]. Even with the economic recession, U.S. sales of organic products, food and non-food, in 2008 were still 17.1% over that of 2007 for a total of $24.6 billion [3].
Antenna in package design and measurement for millimeter-wave applications in fan-out wafer-level package

Due to the advantages of good thermal and electrical performance, lower cost, greater design ﬂexibility, fan-out wafer-level package(FOWLP) has been widely used in millimeter-wave applications. In this letter, a fan-out wafer-level package with the size of 12mm × 12mm for the millimeter-wave applications is accomplished by the redistribution layer ﬁrst(RDL-First) process. The double-sided multiple redistribution layers(RDLs) are used to fan out the signals and to form the antenna-in- package(AiP). An antenna integration scheme for the Ultra Short Range automotive Radar(USRR) chips with four transmit and receive channels was achieved. In addition, a 1 × 3 series fed aperture-coupled antenna array in the fan-out area was designed. Correspondingly, a probe based antenna measurement setup for FOWLP-AiP working in E band was carried out. The measurement results are in good agreement with the simulation. key words:
A storyline attribution of the 2011/2012 drought in Southeastern South America

disci-plines. interrogate the under-utilised climate model intercomparison project datasets to develop new research ideas and create new networks and outreach opportunities in the lead up to COP26. Experts in different science fields, supported by a core team of scientists and data special-ists at Bristol, the unique opportunity to explore together interdisciplinary environmental topics summarised in this
Seven ferroptosis-specific expressed genes are considered as potential biomarkers for the diagnosis and treatment of cigarette smoke-induced chronic obstructive pulmonary disease

Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by incomplete reversible airway obstruction, with high mortality and disability rates, and smoking is the primary risk factor for COPD. Studies performed to date have confirmed that iron and ferroptosis play crucial roles in the development of cigarette smoke-induced COPD, but the exact mechanisms have not been fully elucidated. Methods The microarray datasets GSE10006, GSE11784, and GSE20257 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between COPD smokers and non-smokers airway epithelial. Protein-protein interaction (PPI) and hub gene networks were constructed using the STRING database and Cytoscape software. At the same time, the 3 datasets were screened for ferroptosis-related genes that were co-differentially expressed. The ferroptosis-related hub genes (FRHGs) that overlapped with the ferroptosis-related genes and hub genes were then identified. Next, the mRNA-miRNA network was constructed, and Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were performed. Finally, GSE19407, GSE994, and GSE27973 were used to evaluate the expression of hub genes. Results We identified 7 potential FRHGs (NQO1, AKR1C3, AKR1C1, GPX2, TXNRD1, SRXN1, SLC7A11), which showed good diagnostic properties. The molecular functions (MFs) of FRHGs mainly influence biological processes (BPs) responding to oxidative stress. Nrf2 pathways may be the key pathways regulating ferroptosis in cigarette smoke-induced COPD. Meanwhile, co-expressed mRNAs and miRNAs were selected to construct mRNA-miRNA interaction networks. Furthermore, based on the 7 FRHGs mentioned above, we found that benzoic acid showed high drug targeting relevance. Conclusions This work identified 7 FRHGs as potential biomarkers for the diagnosis and treatment of COPD and provided insights into the mechanisms of disease development in cigarette smoke-induced COPD at the transcriptome level.