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Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

To retain more color information in multispectral compression and reconstruction for spectral color reproduction, a weighted principal component analysis with consideration of color difference caused by tiny wavelength is proposed in this paper. The weight function, which considers the final tool for evaluating multispectral compression and the reconstruction algorithms is color difference, is the average value of spectral color differences between the spectra of a spectral dataset and the spectra obtained by subtracting tiny value from the spectral dataset. Spectral color difference formula is introduced to calculate spectral color difference between the two spectra. NCS, Munsell, and SOCS (ISO/TR 16,066:2003) are used to construct three weight functions, SCDWF-1, SCDWF-2, and SCDWF-3, respectively, to obtain the corresponding weighted principal component analysis, SCDPCA-1, SCDPCA-2, and SCDPCA-3. The root mean squared error (RMSE) and goodness fitting coefficient (GFC) are employed as the spectral evaluation index and the CIELAB color difference is employed as the colorimetric evaluation index. The feasibility and performance of the proposed methods are tested by comparing the results of principal component analysis (PCA) and the other two weighted PCA by compressing and reconstructing three different sets of test samples NCS, Munsell, and SOCS. Statistical results show that compared with PCA, the proposed SCDPCA can significantly improve the colorimetric accuracy at the expense of a small amount of spectral accuracy. Moreover, the colorimetric and spectral accuracy of SCDPCA is better than that of the other two weighted PCA recently proposed by other researchers. [ABSTRACT FROM AUTHOR]

Purpose: The intelligent identification of stains can quickly and accurately identify stains. At present, stains are identified subjectively by appearance, color, taste, feel, location, etc. Color is an important factor in identifying stains. K/S value is used to analyze the color of textile fabric, and it has additivity. The purpose of the study is to explore its application in stain recognition is of great significance to intelligent washing. Design/methodology/approach: A certain method used to stain the textile, then the K/S value of the textile before and after the stain was analyzed and tested by the color difference instrument. The K/S curve of the stain was calculated by the addition of K/S, and then the stain was identified and distinguished. Findings: The K/S value of the textile stained with stains could be deducted by the K/S value of the color difference meter. After deducting the base cloth, the K/S curve of the same stain is basically the same. Then the stain can be identified and analyzed. Research limitations/implications: The K/S value can be used for stain analysis, but it needs to be analyzed and tested in the laboratory. Practical implications: This study provides a simple method for stains identification. Originality/value: In addition to common methods of stain identification, such as appearance, color, feel, smell, location, stain removal materials, breaking the substrate, IR, etc., K/S value can be used for stain analysis. Identifying stains and washing them in a targeted way to achieve a better washing effect could provide certain technical support for the development of smart washing and smart home appliances. [ABSTRACT FROM AUTHOR]

Purpose: Both glazing and laminating play a certain role in finishing and protecting the surface of printed matter. This study makes a comparative analysis of the two through life cycle assessment (LCA) and theoretically explores the difference between them. Design/methodology/approach: In this study, the life cycle of laminating and glazing processes was calculated through using Simapro software, and the results were compared and analyzed. Findings: Twelve environmental categories were used to quantitatively analyze the environmental impact of the product. The results showed that the results of the 12 environmental categories involved in the analysis of glazing was less than that of laminating, indicating that the impact of the glazing on the environment was less than that of laminating on the environment. Research limitations/implications: In order to simplify the study, we only calculated and analyzed the laminating and glazing. Practical implications: Green packaging is the future, 3R1D, reduce, reuse, recycle and degradable. According to the calculation results, corresponding suggestions can be put forward from production, processing, use, waste and other aspects, and make corresponding contributions to the development of green packaging. Originality/value: The contribution and impact of each stage to the product life cycle can be studied. In addition, different waste disposal methods have different impacts on the environment, and the higher the recovery ratio, the better the environmental benefits of the product. Recycling should then be promoted as proportionately as possible in practice in order to reduce the environmental impact. [ABSTRACT FROM AUTHOR]

Reactive oxygen species (ROS) homeostasis and mitochondrial metabolism are critical for the survival of cancer cells, including cancer stem cells (CSCs), which often cause drug resistance and cancer relapse. Auranofin is a mono-gold anti-rheumatic drug, and it has been repurposed as an anticancer agent working by the induction of both ROS increase and mitochondrial dysfunction. Hypothetically, increasing auranofin's positive charges via incorporating more gold atoms to enhance its mitochondria-targeting capacity could enhance its anti-cancer efficacy. Hence, in this work, both mono-gold and bi-gold compounds were designed and evaluated to test our hypothesis. The results showed that bi-gold compounds generally suppressed cancer cells proliferation better than their mono-gold counterparts. The most potent compound, BGC2a, substantially inhibited the antioxidant enzyme TrxR and increased the cellular ROS. BGC2a induced cell apoptosis, which could not be reversed by the antioxidant agent vitamin C, implying that the ROS induced by TrxR inhibition might not be the decisive cause of cell death. As expected, a significant proportion of BGC2a accumulated within mitochondria, likely contributing to mitochondrial dysfunction, which was further confirmed by measuring oxygen consumption rate, mitochondrial membrane potential, and ATP production. Moreover, BGC2a inhibited colony formation and reduced stem-like side population (SP) cells of A549. Finally, the compound effectively suppressed the tumor growth of both A549 and PANC-1 xenografts. Our study showed that mitochondrial disturbance may be gold-based compounds' major lethal factor in eradicating cancer cells, providing a new approach to developing potent gold-based anti-cancer drugs by increasing mitochondria-targeting capacity. [ABSTRACT FROM AUTHOR]

Risk management is widely recognized as a best practice for public agencies to ensure the successful implementation of major transportation projects. The conventional approach to identify and evaluate project risks is dominated by getting input from subject matter experts at risk workshops. However, the uniqueness of such a risk assessment approach remains unexamined. How different are the risks among various projects? Does the risk register reflect the unique feature of a project? The goal of this study is to measure the similarity of project risks across various groups by evaluating 70 major transportation projects delivered under various methods. The similarity index is calculated at three levels, that is, the entire document of the risk register, individual risk item, and the probability and consequence of each risk using a systematic comparative analysis based on natural language processing (NLP) and a state-of-the-art deep learning algorithm named Word2vec. Our study reports a high similarity of risk registers among different projects at all three levels. The analysis does show a lower similarity of risk registers for public–private partnerships (P3) projects. The primary contributions of this study are (1) develop a new approach to analyze the risk registers at the project level as the main output of risk management practice, and (2) establish the relation of risk uniqueness and project delivery method in transportation projects. Results suggest that a data-driven approach may be possible to help project teams develop a common risk register while allowing the teams to focus on each project's unique risks. [ABSTRACT FROM AUTHOR]

Man-made disasters are often unexpected events that necessitate a comprehensive investigation to ascertain their cause. These investigations are critical for government agencies, insurance corporations, and other stakeholders. Social media platforms are a rich source of information, but the credibility and vast amount of data available make it challenging to extract useful evidence. Therefore, understanding the credibility of information from different groups of users on social media and its role in retrieving and filtering relevant sources is crucial. To illustrate the potential in this regard, this study examines the Notre Dame fire as a case study, analyzing tweets posted between April 15 and 25, 2019. Using a collection of lexicons, the study establishes a text-parsing and lexicon-based rule model to classify users' opinions regarding the causes of the fire incident. Then the study characterizes the distribution of opinions between verified and nonverified users and investigates the temporal dynamics of reactions from subsets of users commenting on the event. The findings suggest that opinions from verified users were consistent with official reports, which highlights the potential value of the shared knowledge of verified users in the early stages of disaster investigation. The study further suggests that disaster responders should consider opinions from nonverified users, as they may aid investigations by identifying potential causes and providing new directions. In conclusion, this paper explores the potential of utilizing social media data as a means of supporting engineering investigations and emphasizes the importance of developing robust methodologies from crowdsourcing opinions for engineering investigations in the context of man-made disasters. [ABSTRACT FROM AUTHOR]

Women are significantly underrepresented in construction and engineering industry leadership roles despite having comparable qualifications, experience, and degrees. Improvements require a better understanding of the factors leading to the underrepresentation of females in the construction industry. While prior studies report that a male-dominated culture in construction negatively impacts females' career advancement, minimal research explores the role congruity theory and the likeability and competency dilemma among construction leaders. When striving to succeed in traditionally male-dominated fields, women face a unique challenge because they need to defy gender stereotypes created by cultural norms, encapsulated by the likeability versus competency dilemma. Females who accomplish traditionally male tasks and demonstrate independence, assertiveness, self-reliance, and power are no longer seen as likeable. Through analyzing publicly available LinkedIn recommendations, this paper proposed a data-driven approach for examining the likeability and competency dilemma for female construction leaders. Results showed that female leaders in construction were seen as competent in the same way as their male counterparts, but far less likeable (a likeability score of 28% compared to 51% for males). Developing a text mining model to predict the gender of the person receiving a recommendation, this paper also highlights unconscious bias in describing and reacting to leaders' successes. The machine learning model accurately predicted the gender of the person receiving the recommendation with more than 86% accuracy. Despite possessing the sufficient capability to handle traditionally male work successfully, women receive negative judgment from colleagues, creating challenges in their career paths. Finally, the paper contributes to social role theory and role congruity theory by highlighting the differences in gender roles and leadership roles for women in the construction industry. [ABSTRACT FROM AUTHOR]

[Display omitted] Survivin, a member of the inhibitor of apoptosis protein family, exists as a homodimer and is aberrantly upregulated in a wide spectrum of cancers. It was thought to be an ideal target due to its lack of expression in most adult normal tissues and importance in cancer cell survival. However, it has been challenging to target survivin due to its "undruggable" nature. We previously attempted to target its dimerization domain with a hypothesis that inhibiting survivin dimerization would promote its degradation in proteasome, which led to identification of a lead small-molecule inhibitor, LQZ-7F. LQZ-7F consists of a flat tetracyclic aromatic core with labile hydrazone linking a 1,2,5-oxadiazole moiety. In this study, we tested the hypothesis that LQZ-7F could be developed as a prodrug because the labile hydrazone linker could be hydrolyzed, releasing the tetracyclic aromatic core. To this end, we synthesized the tetracyclic aromatic core (LQZ-7F1) using reported procedure and tested LQZ-7F1 for its biological activities. Here we show that LQZ-7F1 has a significantly improved potency with submicromolar IC 50's and induces spontaneous apoptosis in prostate cancer cells. It also more effectively inhibits survivin dimerization and induces survivin degradation in a proteasome-dependent manner than LQZ-7F. We also show that the combination of LQZ-7F1 and docetaxel have strong synergism in inhibiting prostate cancer cell survival. Together, we conclude that the hydrazone linker with the oxadiazole tail is dispensable for survivin inhibition and the survivin dimerization inhibitor, LQZ-7F, may be developed as a prodrug for prostate cancer treatment and to overcome docetaxel resistance. [ABSTRACT FROM AUTHOR]

[Display omitted] Drug resistance is the major obstacle that undermines effective cancer treatment. Recently, the application of gas signaling molecules, e.g., carbon monoxide (CO), in overcoming drug resistance has gained significant attention. Growing evidence showed that CO could inhibit mitochondria respiratory effect and glycolysis, two major ATP production pathways in cancer cells, and suppress angiogenesis and inhibit the activity of cystathionine β-synthase that is important in regulating cancer cells homeostasis, leading to synergistic effects when combined with cisplatin, doxorubicin, or phototherapy, etc. in certain resistant cancer cells. In the current review, we attempted to have a summary of these research conducted in the past decade using CO in treating drug resistant cancers, and have a detailed interpretation of the underlying mechanisms. The critical challenges will be discussed and potential solutions will also be provided. The information collected in this work will hopefully evoke more effects in using CO for the treatment of drug resistant cancers. [ABSTRACT FROM AUTHOR]

Most of the construction practices in the field of risk identification focus on the expertise, views, and judgments of subject matter experts. While the conventional expert-based approaches provide worth, several challenges exist due to time-consuming and expensive aspects. Moreover, limited experience in major projects makes public agencies susceptible to subjective judgment biases. To address these limitations, this study introduced a data-driven framework for risk identification using historical data and artificial intelligence techniques, particularly word embedding models. The model matches various risk items in past projects by considering the semantic meaning of words to find high frequency and consequence risks. Risk registers from more than 70 U.S. major transportation projects form the input dataset. The model is tested with more than 66% recall and 0.59 F 1 -score for risk detection for new projects. Acquired knowledge from previous projects assists project teams and public agencies to be well-equipped with a risk identification model instead of starting from scratch. • A novel data-driven model for risk template generation is introduced. • A comprehensive risk database for major transportation projects is developed using historical data. • The model learns semantic textual features of risk items using word embedding models. • The model customizes risk templates considering risk prevalence, risk consequences, and project characteristics. • The experimental results revealed more than 66% of testing projects' risk items are covered by risk templates. [ABSTRACT FROM AUTHOR]

Project risk is an important part of managing large projects of any sort. This study contributes to the state of knowledge in project risk management by introducing a data-driven approach to measure risk identification performance using historical data. In the early phases of a project, the identification and assessment of risk is based largely on experience and expert judgment. As a project moves through its life cycle, these identified risks and the assessment of them evolve. Some risks become issues, some are mitigated, and some are retired as no longer important. This study investigated the quality of early risk registers and risk assessments on large transportation projects and compared them to how the identified risks evolved on historical projects. The investigation involved the use of textual analysis of archival risk register documents. Finite-state automation methods akin to Markov chain models were used to track the changes in risk attributes on large infrastructure projects as the projects matured. The objective was to be better able to anticipate how project risks will change as projects move forward and to be better able to forecast changes to the risk register from ex ante to ex post conditions. Results from 11 major US transportation projects suggested that, on average, fewer than 65% of ex ante identified risks ultimately occurred in projects and were mitigated, while more than 35% did not occur and were retired. In addition, more than half of the risks emerged during project execution when new information became available. Based on the categorization of risk management styles, we find that identifying risks early in the project life cycle is necessary, but not sufficient to ensure successful project delivery. A project team with positive doer behavior (i.e., actively monitoring and identifying risks during project execution) performed better in delivering projects on time and within budget. [ABSTRACT FROM AUTHOR]

Will women and men follow distinctively different paths to achieve executive engineering leadership positions in the US architecture, engineering, and construction (AEC) industry? Using Engineering News Record's (ENR's) 2019 Top 400 list, this research analyzed LinkedIn profiles for over 2,800 executives to assess career differences between genders. Statistical comparisons of important features, highlighted by number of companies, titles, education, and network size, revealed a significant impact of gender on individual career paths. A key finding was that men ascend to leadership with a single firm throughout their career, outpacing women almost fourfold (37% to 10%). Applying random forest (RF) as an ensemble classifier, researchers successfully predicted profile gender with accuracy of 98.95% for training and 89.53% for testing samples. Collating and categorizing the activities and milestones of individual and collective executives offer insight regarding successful experiences, skills, and choices to reach leadership roles. This creates a roadmap for current and future early and midlevel professionals to model their own vocational journey and accelerate progression up the corporate ladder. From an industry perspective, firms deprive themselves and customers of the proven wide-ranging benefits of diversity. [ABSTRACT FROM AUTHOR]

The presumed high exposure to volatile organic compounds among construction workers has not been substantiated with biomonitoring evidence. This study found that construction occupation was associated with increased blood concentrations of toluene, ethylbenzene, o-xylene, and m-/p- xylene. The findings inform interventions for exposure reduction using engineering controls and personal protective equipment. Objective: This study aimed to examine blood benzene, toluene, ethylbenzene, o-xylene, and m-/p-xylene (BTEX) concentrations and their trends contrasting construction workers with workers in other occupations from 1999 to 2014 in the United States. Methods: Using data from the National Health and Nutrition Examination Survey, quantile regressions were performed to investigate associations between occupation and blood BTEX concentrations. Results: We found that high-risk and construction occupations were associated with increased blood concentrations of toluene, o-xylene, and m-/p-xylene at the 50–90th percentiles (P50–90), and ethylbenzene at P70–90. Moreover, although blood concentrations of ethylbenzene, o-xylene, and m-/p-xylene trended down among all US workers, no decreasing trend was observed for benzene and toluene among construction workers. Conclusions: Future studies are warranted to address questions about specific tasks to better assess VOC exposure from various trades. [ABSTRACT FROM AUTHOR]

Xu et al. ([10]) developed a rule-based natural language processing (NLP) approach to extracting domain knowledge elements (DKEs) from Chinese text documents in the domain of construction safety management. The approach attempts to solve the problem of construction fire safety management and contributes to guiding project teams in the timely detection of fires, improving safety management efficiency, and reducing fire-related losses. The special collection on Managing Infrastructure in a Data-Rich Era is available in the ASCE Library at https://ascelibrary.org/jmenea/infrastructure data rich era. [Extracted from the article]

Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a "supporting" or "opposing" class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value. [ABSTRACT FROM AUTHOR]