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Record W3210587733 · doi:10.1002/awwa.1802

AWWA Water Science Author Spotlight

2021· article· en· W3210587733 on OpenAlexaboutno aff
H. Larry Tang

Bibliographic record

VenueAmerican Water Works Association · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCapacitive deionizationManagementOperations researchLibrary scienceEngineeringPolitical scienceComputer scienceLawChemistry

Abstract

fetched live from OpenAlex

Having recently published an article in AWWA Water Science, H. Larry Tang answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. Modeling and Interpretation of Membrane Capacitive Deionization Responses to Different Salt Load Siyu Zhu, Jian Yu, Ty C. Stewart, and H. Larry Tang I'm currently working as an associate professor of environmental engineering at Indiana University of Pennsylvania (IUP). I was granted tenure and promotion in July 2021. Prior to my tenure, my research was primarily a continuance of topics that I was consistently well versed in; this included the disinfection byproduct research that I started when I was a PhD student at Penn State University, as well as this capacitive deionization research that I have worked on since my independence as a principal investigator. Although both of these research fields are still intriguing to me, I recently decided to switch my focus to the analysis of water project data by machine learning (ML) and artificial intelligence (AI). ML and AI have been a hot research area in recent years, but they are not applied much in water environment research. Dr. Tang visits the Hukou Waterfall in China on the Yellow River, known as the most turbid river in the world. I'm originally from China. My first name, Hao, in Chinese means “as vast as the ocean.” It appears I am destined for a life related to investigating water. When I went to college at age 16, I had no clue of what major to choose. My father, who is a civil engineer, helped me choose a major with a good job prospect: water supply and sewage engineering, which falls under the civil engineering discipline. During my junior year in college, I was fortunate enough to join a professor's research group and worked on a water biofiltration project. After that, I started reading research papers from the library and wrote my first academic paper. Compared with most of my classmates, who had decided to enter the private sector after college to do engineering design work, I felt research and writing were the way I would love to go, and I'm glad I pursued it. I think my open-mindedness is an important trait that will contribute to my success. Being open-minded means I am willing to actively search for evidence against my potential bias. In research, I am not limited to doing research in my comfort zone; I can also explore some eye-opening projects that are intriguing to me. In recruiting team members, I favor the formation of a multicultural team, where convergences of different cultures may spark new ideas that are beneficial for research. My PhD advisor, Dr. Yuefeng Xie, professor of environmental engineering at Penn State, played the most important role in forming my career as a faculty member. Of the numerous pieces of advice and lessons that I learned from him, it is hard to say which one is the best. Dr. Tang is advising a student on the flocculation process in a pilot water treatment system. Engineering advancement toward commercialization was the motivation for our research. With funding from the private sector, with the aim of enlarging and commercializing the capacitive deionization equipment under study, we developed an empirical solution that is able to proactively quantify the desalination efficiencies under various scenarios, which serves as a milestone on the road of practical engineering design. During the weekends, I love to spend some hours at a shooting range. With my Remington .243 caliber heavy-barrel precision rifle and handloaded cartridges with 105-grain Hornady bullets, I enjoy the feeling of hitting the target on the bullseye 800 yards down the range. Accurate long-range shooting is not a simple aim-and-shoot activity. It involves physics (gravity, fluid mechanics, and even the earth's Coriolis effect need to be considered), chemistry (handloading cartridges with appropriate amounts of gunpowder for different bullets), biology (steady hand and breath control), and engineering (applying all those sciences to work out a shooting solution). This is exactly what environmental engineering is built upon—a solid foundation of physics, chemistry, and biology. My students enjoy my teaching style of using the theories and practices of accurate long-range shooting to explain environmental engineering concepts. For example, a hot-loaded cartridge drives the bullet out of the barrel at a higher velocity, which exaggerates the horizontal drift and minimizes the vertical drop, and this phenomenon can be used to explain pressure dependency on temperature and can be used as an example of interpreting the horizontal drift by the Bernoulli principle. In addition to teaching, this interest also intersects with my research, as it allows me to develop a habit of thinking about some critical concepts in greater depth. A breadth of knowledge could be a future challenge for many researchers. Since environmental engineering and water research are based on multidisciplinary fields, continuous learning in other disciplines is needed. Take ML and AI, for example: software development and data science tools are indispensable if they are widely applied in the environmental engineering field. This article on desalination with a promising capacitive deionization technology adds to the knowledge pool of available desalination approaches. As the public and regulatory agencies have more and more concerns about water environment issues, I look forward to seeing commercialization of the technology in the household market (e.g., a household desalination product) for improving public health and the industrial market (e.g., to meet more and more stringent regulations on effluent salinity). I grew up in China, where haze often occurred. However, I was not aware of that before the US Embassy in Beijing started publishing particulate matter (PM2.5) trend data about 10 years ago. A similar abnormality struck me when my research data revealed a substantial difference between the Susquehanna River water quality in the United States and the Xiangjiang River water quality in China. I realized that there is a critical need for environmental research in the foreseeable future, and this strengthens my belief in a bright future of working in the environmental field. As mentioned earlier, I am a big fan of accurate long-range target shooting. I like spending hours in the wild, just to shoot one bullet. During the weekdays, I like swimming to keep fit. Benefiting from the IUP facility, I use the university swimming pool, which has a water temperature maintained at 85 °F, as the venue of my 1,000-yard routine swimming exercise. When I am at home, my wife leaves me the assignment of walking our one-year-old Labrador retriever through the neighborhood. To learn more about Dr. Tang's research, visit his AWWA Water Science article, available online at https://doi.org/10.1002/aws2.1166.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.253
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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