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A drone-based rotating cascade impactor for single-particle analysis: Advancing aerosol mixing state research

Atmospheric research 2025年 325卷

作者： Songhui Lee Minwoo Baek Miri Kim Jaebeom Park Minsang Yoo Yongbum Kwon Kang-Ho Ahn Handol Lee Program in Environmental and Polymer Engineering Graduate School of Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea Department of Environmental Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea Korea National Institute of Rare Metals Korea Institute of Industrial Technology Namdong-gu Incheon 21655 Republic of Korea Department of Mechanical Engineering Hanyang University 55 Hanyangdaehak-ro Sangnok-gu Ansan 15588 Republic of Korea Particle Pollution Research and Management Center Inha University Incheon 21999 Republic of Korea

A novel drone-based aerosol sampling system, integrated with a rotating cascade impactor, was developed to enable size-selective sampling of atmospheric particles at upper altitudes. The system features rotating impaction stages that enable particle deposition suitable for single-particle analysis. For the first time, this platform was combined with Raman microspectroscopy (RMS) and surface-enhanced Raman spectroscopy (SERS) to perform detailed physicochemical characterization of airborne particles, including submicron aerosols, across ground level and aloft. Applied during a high-pollution event, the methodology enabled the characterization of particle mixing states, chemical functional groups, and size-dependent features that may be associated with aerosol transformation mechanisms. Single-particle analysis showed that internally mixed particles in the coarse-mode size range (1.35–5.5 μm) accounted for 53 % of the total in the upper-level sample, exceeding single-component particles by 20 %, which suggests multiphase processing and aging. Among sulfate-containing coarse particles, CaSO 4 was the dominant species (38 %), indicating secondary formation through heterogeneous reactions during long-range transport In the submicron range (<1 μm), nitrate particles dominated (77 %), attributed to freshly formed aerosols resulting from coastal air masses with limited aging. These results highlight the significance of integrating high-resolution vertical aerosol profiling into atmospheric observations, revealing differences not captured by ground-based measurements. Beyond the field findings, this study also demonstrates the utility of a lightweight, drone-mounted rotating cascade impactor system, which enables scalable, size-selective, and altitude-resolved single-particle sampling under atmospheric conditions. This integrated approach offers a novel and practical framework for advancing both aerosol instrumentation and our understanding of atmospheric particle dynamics.

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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Computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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STATISTICAL ISSUES AND PROBLEMS IN GROUND-WATER DETECTION MONITORING AT HAZARDOUS-WASTE FACILITIES

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GROUND WATER MONITORING AND REMEDIATION 1988年第4期8卷 135-150页

作者： MCNICHOLS, RJ DAVIS, CB Roger J. McNichols is a professor of industrial engineering at the University of Toledo (Department of Industrial Engineering University of Toledo Toledo OH 43606). After receiving his Ph.D in industrial engineering from The Ohio State University he joined the faculty of Texas A and M University where he directed the Maintainability Engineering Graduate Program at Red River Army Depot. At UT he has served as associate dean of engineering and as chairman of the Systems engineering doctoral program. His research and consulting interests include reliability quality control manufacturing mathematical modeling and applied statistics. Charles B. Davis is an associate professor of mathematics at the University of Toledo (Department of Mathematics University of Toledo Toledo OH 43606). After receiving his M.S. in mathematics and statistics and his Ph.D. in statistics from the University of New Mexico he joined the Mathematics Department at UT where he established the graduate program in statistics. His research and consulting interests include statistical modeling statistical computation simultaneous inference and data analysis.

Ground water monitoring presents interesting statistical challenges, including controlling the risk of entering compliance monitoring, incorporating all modes of inherent variability into the statistical model on which tests are based, and taming the detection limit problem, all while maintaining demonstrable sensitivity to real contamination. Some of these challenges exceed textbook statistics considerably, even when considered alone, and good solutions are scarce. When these challenges are combined, the task of developing good statistical procedures or good regulations can be formidable. This article presents a number of realities of ground water monitoring that should be considered when developing statistical procedures. Recommendations made for addressing these realities include the following: (1) the false positive rate should be controlled on a facility-wide basis, rather than per well or per parameter as required in the proposed regulation (40 CFR §264); (2) multiple comparisons with control procedures are preferable to analysis of variance (ANOVA) for controlling the overall false positive rate; (3) retests can be made an explicit part of the statistical procedure in order to increase power and decrease sensitivity to distribution shape assumptions; (4) commonly used simple methods of handling below detection limit data with parametric tests, including Cohen's procedure as implemented in the U.S. EPA's Technical Enforcement Guidance Document (TEGD), should probably be avoided; (5) the statistical properties of practical quantitation limits for non-naturally occurring compounds should be studied carefully; and (6) so long as the facility-wide false positive rate is controlled, better sensitivity to real contamination is obtained by monitoring fewer well-chosen parameters at a smaller number of well-chosen locations. An evaluation of the proposed revised §264 regulation with respect to these realities reveals that it seems to be a definite improvement over the

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Policies for physician allocation to triage and treatment in emergency departments

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IISE Transactions on Healthcare Systems engineering 2019年第4期9卷 342-356页

作者： Zayas-Caban, Gabriel Xie, Jingui Green, Linda V. Lewis, Mark E. Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI United States The School of Management University of Science and Technology of China Hefei China Graduate School of Business Columbia University New York NY United States School of Operations Research and Information Engineering Cornell University Ithaca NY United States

In the emergency department (ED), low-acuity patients divert resources from more critical patients. To facilitate flow, EDs are experimenting with new care models, such as the Triage-Treat-and-Release program at the Lutheran Medical Center (LMC) ED in New York, where physicians handle both phases of service for low-acuity patients. Our goal is to determine how physicians should prioritize triage versus treatment to balance initial delays with timely discharges. Triage and treatment are modeled as a two-phase stochastic service system, where patients may leave without receiving treatment. Patients leaving without receiving treatment increases the importance of the second phase. We introduce K-level threshold policies which prioritize treatment unless there are K or more patients in triage. The effect is a class of policies that capture a decision-maker’s valuation of the importance of each activity;lower K values signifies triage priority. Sufficient conditions are provided to ensure these policies yield a stable system. A heuristic is presented for choosing K. Using LMC data, K-level threshold policies, compared to other practical policies, perform well with respect to average rewards and waiting times over a range of parameters. These policies promise physicians an effective and simple way to allocate their time between triage and treatment. © 2019, © 2019 “IISE”.

关键词： Abandonments ED triage and treatment process simulation tandem queue

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Simulation-based analysis of scheduling decisions in an outpatient clinic

Simulation-based analysis of scheduling decisions in an outp...

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IIE Annual Conference and Expo 2013

作者： Akin, Gokce Ivy, Julie S. Huschka, Todd R. Rohleder, Thomas R. Graduate Program in Operations Research North Carolina State University Raleigh NC 27695 United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC 27695 United States Department of Health Care Policy and Research Mayo Clinic 200 1st ST SW Rochester MN 55905 United States

ISBN: (纸本)9780983762423

In an outpatient clinic, in which multiple classes of patients are given appointments, capacity management and scheduling decisions are particularly important. Once an appointment is scheduled;it is not uncommon for the appointment to be rescheduled or cancelled prior to the appointment day, or a patient may simply not show-up on his/her appointment day. Thus it is crucial to consider all of these patient behaviors when assigning an appointment. In this study we consider multiple classes of patients with different demand rates, service times, and different delay-dependent reschedule, cancellation, and no-show rates. We develop a simulation model to analyze the effects of reducing the appointment planning horizon. In our experimental analysis we also evaluate different calendar structures: traditional slots, standardized 20 or 30 minute slots, and slotless designs. We use capacity utilization and "seen" patient proportions as the performance indicators in our model. Our results indicate that we can significantly increase physician utilizations and the percentage of seen patients by reducing the appointment planning horizon. We also observe improvements in the performance indicators if we use a slotless design or standardized appointments with shorter slot lengths.

关键词： Scheduling

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Addendum to “a comparison of heuristics for a school bus scheduling problem”

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Transportation research Part B: Methodological 1987年第4期21卷 311-315页

作者： Jeffrey C. Dauler Henry L.W. Nuttle Program in Operations Research and Department of Industrial Engineering North Carolina State University Raleigh North Carolina 27695-7906 U.S.A.

Given a group of one or more schools, each with a specific arrival time window, and the bus routes serving these schools, the problem is to create an optimum set of combinations of routes to be served by a single bus, where optimality is defined in terms of fleet size and deadhead travel distance. We present the results of a computational study comparing ten heuristics, adapted from procedures which have appeared in the literature, on a set of 30 test problems derived from actual school data. This study is a follow on to that reported in Graham and Nuttle (1986).

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Sensor development suitable for the plant growth prediction model in the plant factory

Sensor development suitable for the plant growth prediction ...

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2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013

作者： Kawakita, Tomohiro Kato, Masaki Isozaki, Masahide Suzuki, Ken Hashimoto, Atsushi Kameoka, Takaharu Department of Planning and Development Dept. Chitose Industrial Co. Ltd. Osaka Japan Department of Operations Research Dept. KOZO KEIKAKU ENGINEERING Inc. Tokyo Japan Department of Yyy Mie Prefecture Agricultual Research Institute Mie Japan Department of Graduate School of Bioresources Mie University Osaka Japan

The plant growth prediction in the plant factory is extremely important to make out the schedule for harvesting from a seeding premeditatedly. If we can grow it premeditatedly to get quantity necessary for necessary time, we can provide the quantity that it is timely, and is necessary for the market. And a function as a plant factory becomes clearer. An accumulated temperature model using the data which measured a piece of fixed point temperature in the plant factory was used so far to perform this prediction. However, we could not predict correct crop time and it was not often met at the time when a market demanded it and appeared in only accumulated temperature. And it was able to trouble many plant factory managers. In this time we developed wireless sensor reason why it is more precision for plant growth prediction. Sensor which we developed can be sensing not only temperature but also humidity and Illumination. And it can monitor the plant growth prediction. There is possibility that we can build the system which predicts more correct crop time by using the wireless sensor which we developed comes out.

关键词： Forecasting

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Characterizations of Ni-loaded lignite char catalysts and their performance enhancements to catalytic steam gasification of coal

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Journal of the Energy Institute 2022年 105卷 53-71页

作者： Tipo, Ronnachai Tippayawong, Nakorn Chaichana, Chatchawan Chimupala, Yothin Chaiklangmuang, Suparin Graduate Program in Industrial Chemistry Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Industrial Chemistry Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Mechanical Engineering Faculty of Engineering Chiang Mai University Chiang50200 Thailand Center of Excellence in Materials Science and Technology Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai50200 Thailand

Catalyst characteristics such as specific surface area, porosity and active site play an essential role in catalyst design. Understanding of the characteristic functions in controlling catalyst activity is necessary for considering the catalyst application, especially the Ni-loaded lignite char catalyst, employed in catalytic steam gasification. In this work, the unmodified and five modified catalysts were used to study the effect of catalyst characteristics on their performance for catalytic steam gasification of coal in the two-stage fixed-bed reactor. The findings indicated that the fresh acid modified catalysts increased specific surface area and micropore formation, including the smaller Ni metal crystallite size, the decreasing of Ni metal nanoparticle size and the well-dispersed of Ni metal nanoparticles. Meanwhile, the fresh modified catalysts by the alkali and the combined alkali-acid treatments promoted mesopore formation. Enhancing catalytic steam gasification of coal, it was found that the mesoporosity of catalyst was the considerable initial characteristic for controlling the catalyst activity. The total gas yields of the mesoporous catalysts were in the range of 50.65–77.70 mmol/gVM,feed, while the total gas yields of the microporous catalysts were 43.82–55.34 mmol/gVM,feed. After catalyst testings, the specific surface areas and number of mesopores in the spent catalysts were increased by steam. Remarkably, these changing pore sizes affected the transformations of the isotherms (type I + II to type II) and the hysteresis loops (type H4 to type H3). Also, steam influenced the conversion of Ni metals to NiO, Ni(OH)2 and Ni2O3. For thermal property, all spent catalysts had high thermal stability. The modified catalysts could decrease carbon deposition more than the unmodified catalyst, which was an upside for catalyst reusability. © 2022 Energy Institute

关键词： Catalyst activity

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Virtual Wearable Sensor Data Generation with Generative Adversarial Networks 23

Virtual Wearable Sensor Data Generation with Generative Adve...

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Proceedings of the Winter Simulation Conference

作者： Yining Huang Hong Wan Xi Chen Operations Research Graduate Program North Carolina State University Raleigh USA Department of Industrial and Systems Engineering North Carolina State University Raleigh USA Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg USA

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.

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