MétaCan
Menu
Retour à la cohorte
Enregistrement W3210587733 · doi:10.1002/awwa.1802

AWWA Water Science Author Spotlight

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

Notice bibliographique

RevueAmerican Water Works Association · 2021
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWater Quality Monitoring Technologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaCapacitive deionizationManagementOperations researchLibrary scienceEngineeringPolitical scienceComputer scienceLawChemistry

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,376
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,253
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAmerican Water Works AssociationMême sujetWater Quality Monitoring TechnologiesTravaux en français237 207