Digital Commons Network^™

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Reimagining Sustainable Sulfur Concrete: A Systematic Review Of Sulfur, Waste Integration, And Durability, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa Elgamal

All Works

Sulfur concrete (SC) - a thermoplastic, waterless composite in which molten sulfur-based binder replaces Portland cement - is attracting growing research attention as a sustainable construction alternative capable of superior performance in chemically aggressive environments. This systematic literature review synthesises evidence from 127 sources (2000–2025), examining three interlinked dimensions: (i) the role of waste and industrial by-product materials as aggregate and filler substitutes; (ii) the influence of aggregate type, morphology, and grading on mechanical and microstructural performance; and (iii) durability under acid, saline, and freeze-thaw exposures. A transparent, PRISMA-aligned screening and classification protocol - here used as a structured review …

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

Robust Hybrid Tree-Based Machine Learning-Assisted Optimization Of Well Parameters To Reduce Asphaltene Precipitation Risk In Oil Fields, Malek Jalilian, Madani, Alireza Keshavarz, Stefan Iglauer, Abbas Khaksar Manshad, Amir H. Mohammadi

Research outputs 2022 to 2026

Asphaltene precipitation is a persistent flow-assurance issue in carbonate oil wells, leading to increased intervention frequency and production losses. This study utilizes multi-decade surveillance and operational data from a mature carbonate field (1983–2023) to train and optimize five tree-based models: Decision Tree, Random Forest, Extra Trees, Gradient-Boosting Decision Tree, and CatBoost, employing a Tree-Structured Parzen Estimator. These models, constrained by operational parameters, were integrated to identify optimal settings that can minimize the frequency of asphaltene precipitation and the subsequent cleanups. The novelty of this research lies in the direct integration of interpretable tree ensembles with an optimizer that adheres to …

Thermophysical Characteristics Evaluation Of Carbon And Metal Oxide-Based Hybrid Phase Change Materials For Thermal Energy Storage Applications, Utpol K. Paul, Md Shahriar Mohtasim, Md Golam Kibria, Barun K. Das

Research outputs 2022 to 2026

Phase change materials (PCM) have limited practical applications due to their low heat conductivity. The main goal of this work is to improve the thermophysical properties of PCM by adding different compositions of hybrid carbon and metal oxide-based nanoparticles for incorporation into hybrid photovoltaic/thermal solar systems to accommodate the built environment and thermal energy storage as a building envelope for thermal comfort. Three types of nanoparticles have been selected including Graphene, Al2O3, and ZnO and synthesized samples with the combinations of 0.5 wt% Al2O3 + 2 wt% ZnO, 0.75 wt% Al2O3 + 2 wt% ZnO, 1 wt% Al2O3 + 2 …

Infestation By Phorid Flies Disrupts Behavior And Immune Function In Honey Bees Monitored By Radio-Frequency Identification, Gloria Ruiz-Guzmán, Oliverio Delgado-Carrillo, Francisco J. Balvino-Olvera, María De Jesús Aguilar-Aguilar, Violeta Patiño-Conde, Paulo De Souza, Ulises Olivares-Pinto, Mauricio Quesada

Research outputs 2022 to 2026

Honey bees (Apis mellifera Linnaeus) are important pollinators in natural and agricultural ecosystems and play a key role in global food production. However, colonies are increasingly affected by multiple stressors, including emerging parasites, which can compromise their health and performance. Among these, phorid flies (Diptera: Phoridae) have been observed in association with weak or collapsing honey bee colonies in various regions in Mexico, yet their effects on honey bee behavior and immune function under field conditions remain poorly understood. In this study, we investigated the impact of a phorid fly infestation on the behavior, survival, and immune defense of A. …

Suicide Ideation Detection Using Social Media Data And Ensemble Machine Learning Model, Erol Kina, Jin Ghoo Choi, Abid Ishaq, Rahman Shafique, Monica Gracia Villar, Eduardo Silva Alvarado, Isabel De La Torre Diez, Imran Ashraf

Research outputs 2022 to 2026

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, …

Over A Century Of Global Decline In The Growth Performance Of Marine Fishes, Helen F. Yan, Hannah V. Watkins, Alexandre C. Siqueira, David R. Bellwood

Research outputs 2022 to 2026

Human-driven pressures are causing large-scale changes in the ecologies and life histories of fishes. Growth performance is a composite life-history trait that captures the trade-off between two fundamental traits: growth and body size. Here, we assess the impacts of fishing and temperature on the growth performance of marine teleost fishes globally over the last century. Using 7683 growth curves encompassing 1479 species, we find a global pattern of decline in growth performance from 1908 onwards, with the greatest declines in commercially valuable fishes. Indeed, managed fisheries experienced a 9% decline in growth performance over the last century, which can equate …

Machine Learning-Based Regression For Magnetic Field Prediction From Odmr Spectral Data, Jesse B. Hernandez

Electronic Theses, Projects, and Dissertations

Optically Detected Magnetic Resonance (ODMR) using nitrogen-vacancy (NV) centers in diamond enables sensitive, room-temperature magnetic field sensing, but real ODMR spectra are often noisy and difficult to analyze with traditional peak-fitting methods. This thesis investigates whether machine learning can reliably predict magnetic field strength directly from ODMR spectra, and compares four model families under a single regression task: a random forest, an artificial neural network (ANN), a one-dimensional convolutional neural network (1D-CNN), and a Transformer.

Training data were generated from an NV-ensemble simulation calibrated to real measurements provided by the Ulsan National Institute of Science and Technology (UNIST), spanning 0 …

Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration, Rachel L. Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than …

Efficient Removal Of Methyl Orange Dye Via A Mnfe₂O₄/Go Nanocomposite With A Ctab Dual-Layer Surfactant Coating, Taher Karami, Soleiman Bahar, Reza Mostafazadeh

Research outputs 2022 to 2026

This study investigated the adsorption of methyl orange (MO) from aqueous solutions using a novel MnFe₂O₄/GO nanocomposite coated with cetyltrimethylammonium bromide (CTAB). The dual-layer surfactant modification facilitates both electrostatic and lipophilic interactions. This enhancement significantly improves dye removal efficiency. The adsorption process was monitored using spectrophotometry at 464 nm, with various characterization techniques confirming the structural and magnetic properties of the nanocomposite. The optimized parameters for maximum adsorption include a 2-minute ultrasonic dispersion, pH 6.8, and a surfactant-to-adsorbent ratio of 1, achieving a maximum adsorption capacity of 285.7 mg/g. The kinetic data followed a pseudo-second-order model, whereas the adsorption isotherm …

A Consolidated Mössbauer Spectrum For Undisturbed Soils From Gusev Crater, Mars, Paulo De Souza

Research outputs 2022 to 2026

Mossbauer analyses from MIMOS II data collected on Mars provided breakthrough insights into iron mineralogy of the planet’s surface. Resulting from limited measurement time, science operations were divided among multiple instruments, restricting the detection of minor Fe-bearing mineral phases (below 5% WSA). MIMOS II records Mössbauer spectra at 10 K intervals, and to enhance signal to noise ratios, spectra from different intervals were combined, when applicable. For instance, temperature variations between 190 K and 270 K in basaltic samples do not significantly impact the spectra, allowing data from all intervals to be integrated. By combining MIMOS II data from various …

Green Waste Biochar And Plant Growth-Promoting Bacteria Enhance Tomato Growth Under Combined Nutrient Deficiency And Salinity Stress, Soumaya Tounsi-Hammami, Munawwar Ali Khan, Mahra Alqemzi, Salama Ali Almehairi, Aneesa Rasheed Anwar

All Works

This study characterized a green-waste-derived biochar from date palms and ghaf trees and investigated its potential as a soil amendment with halotolerant Bacillus spp. to improve tomato seedling quality under dual stress of salinity and nutrient deficiency. Biochar was produced through pyrolysis at 450 °C and then characterized for yield, pH, electrical conductivity, proximate analysis, surface morphology, energy-dispersive X-ray spectroscopy, and heavy-metal content. Its effectiveness was tested both alone and in combination with a Bacillus sp. mix, using a completely randomized design with varying NPK fertilizer levels and saline irrigation. Tomato seedlings were evaluated 45 days after planting for various …

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

All Works

As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

All Works

Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

Kindergarten Teachers’ Perceptions Of Instructional Approaches For Teaching Sequencing Concepts: Worksheet, Hands-On, And Beebot-Integrated Lessons, Savannah P. Partin

Electronic Theses and Dissertations

The purpose of this qualitative multiple-case study was to explore kindergarten teachers’ perceptions of three instructional approaches for teaching sequencing concepts: worksheet-based instruction, hands-on manipulative instruction, and BeeBot-integrated instruction. Sequencing is a foundational early literacy skill that supports reading comprehension, story retelling, and understanding the order of events. Although sequencing is commonly taught through traditional instructional methods, limited research has examined how kindergarten teachers perceive the use of educational robotics as a tool for literacy instruction. The study was guided by constructivist theory, sociocultural theory, and developmental systems theory. Participants included six kindergarten teachers from a public elementary school in …

Reducing Water Absorption Of Cement Mortar By Using Modified Clay Brick Waste, Nuntaporn Kongkajun, Somyote Kongkarat, Wannasa Phukutnam, Warittha Sujaritpanich, Edward A. Laitila, Parinya Chakartnarodom

Michigan Tech Publications

Cement mortar is inherently hydrophilic due to its porous structure, which allows water penetration and adversely affects its durability and long-term performance. To mitigate this issue, hydrophobic cementitious materials can be developed by incorporating hydrophobic substances during mixing to provide hydrophobic properties throughout the material, achieved by introducing a wet chemical method using stearic acid-modified clay brick waste (MCBW) as a hydrophobic agent. The MCBW is prepared by treating clay brick waste with stearic acid (SA) and subsequently used as a partial replacement for fine aggregates. The effects of the MCBW incorporation on compressive strength and water resistance are investigated. …

Helixcross – Design And Development Of An Ai-Powered Cross-Examination Simulation For Forensic Expert Testimony Training, Mark Barash, Bruce Budowle

Faculty Research, Scholarly, and Creative Activity

Forensic testimony is where science meets the courtroom, and it is one of the critical forensic skills taught largely by apprenticeship. An examiner who overstates a match, mishandles a probability, or cannot support a method under direct or cross-examination can weaken the evidence in precisely the cases that rely on it. Yet in most forensic laboratories, testimony preparation remains largely informal, mentor-dependent, and unscalable. Consequently, many practitioners first face rigorous cross-examination at trial. To address this gap, HelixCross, an alpha-stage, publicly accessible, prompt-engineered large language model (LLM) agent, was developed. It uses retrieval-augmented generation (RAG) to simulate defense counsel cross-examination …

Balancing Root-Zone Oxygenation And Rhizosphere Stability: The Impact Of Aeration And Hypoxia On Hydroponic Plant Growth, Christian D. Baker

All Graduate Theses and Dissertations, Fall 2023 to Present

As the global population and food demand continue to increase, growers must look to innovative and sustainable methods for plant cultivation. Hydroponics is a popular method of sustainable plant cultivation where plant roots are suspended over a liquid nutrient solution. Hydroponics uses less water compared to traditional agriculture because there is no evaporation or solution runoff. It also maximizes land use, requires fewer pesticides and herbicides, and enables year-round cultivation in controlled environments. However, indoor hydroponic systems rely on artificial lighting, which has higher energy costs than traditional farming methods. Optimizing growing conditions is therefore critical to ensure long-term economic …

Thermally-Driven Equiaxation Of Columnar Grains And Α″ Nanoparticles Enabling Isotropic Β-Ti Alloys With Strength–Ductility Synergy Manufactured By Laser Powder Bed Fusion, Lei Qin, Shengfeng Zhou, Jianbo Jin, Huan Yang, Kunmao Li, Cheng Deng, Yujie Yuan, Lai-Chang Zhang

Research outputs 2022 to 2026

The transient thermal cycling characteristics during laser powder bed fusion (LPBF) induce elemental segregation and columnar growth, resulting in significant mechanical anisotropy and strength–ductility trade-off in titanium alloys. In order to break through this bottleneck, this work proposes a strategy of inducing equiaxation of columnar grains through recrystallization and regulating the morphology of the α″ phase (from an acicular shape to nanoparticles) to synergistically optimize the uniformity, strength and ductility of LPBF-fabricated Ti-35Nb-5Cu-xMo (Ti355x, x = 0, 1, 2, 4 wt%) alloys. The results demonstrate that LPBF-fabricated Ti355x alloys display equiaxed/columnar microstructures with significant mechanical anisotropy and strength–ductility trade-off. After …

Aluminium Based Hydrotalcite Like Compounds For Direct Lithium Extraction: A Critical Review On Crystallography And Structure Selectivity Design, Amin Sarmadi, Yasaman Boroumand, Mohammad Mahdi A. Shirazi, Magdalena Wajrak, Shayan Abrishami, Matthew Myers, Muhammad Rizwan Azhar, Amir Razmjou

Research outputs 2022 to 2026

The accelerating demand for lithium is intensifying interest in direct lithium extraction (DLE) technologies that minimise the environmental footprint of conventional solar evaporation ponds. Among sorbent-based DLE concepts, aluminium based hydrotalcite like compounds (Al-HTlc), particularly lithium aluminium layered double hydroxide (LiAl-LDH), are considered the most mature sorbent options, offering facile lithium (Li+) recovery and high selectivity in highly saline chloride based solutions. Both properties arise from a distinct intercalation mechanism wherein Li+ and its counter-anions (e.g. Cl− or SO42−) are co-intercalated into the crystal structure upon brine exposure. Li+ selectivity arises partly from a steric match between Li+ (ionic radius …

Mtorc2-Nav1.2 Signaling Drives Early Hyperexcitability In Alzheimer’S Disease Mouse Model, Nolan M Dvorak, Jeffrey L Noebels

Faculty, Staff and Students Publications

Hyperexcitability is a biomarker of early-stage Alzheimer’s Disease (AD) and hastens cognitive decline later in its course. Mechanistic target of rapamycin (mTOR) signaling contributes to the slope of this trajectory, as evidenced by early increased brain expression and the rescue of hyperexcitability by genetic deletion of mTOR complex 2 (mTORC2); however, a molecular mechanism directly linking mTOR signaling to membrane hyperexcitability in early-stage AD remains elusive. Here, we show that hyperactive mTOR signaling stimulates the voltage-gated Na+ channel 1.2 (Nav1.2), a previously identified downstream phosphorylation target of mTORC2 and a key regulator of membrane electrogenesis. Augmented Nav1.2 channel function induced …

The Hidden Heatwave: Thermal Hazards In The New York City Subway System, Abdou Rachid Bah, Maimuna Muntaha, Lenny L. Metlitsky, Cesar Pascal Vasquez, Tyler Ayala, Isaac Morel Lopez, Shaquan A. Larose, Kingston Ditsch, Kip Nielsen, Shaunak Sharma, Reginald U. Avila-Ortega Blake, Hamidreza I. Norouzi

Publications and Research

Underground transit systems (Subway) are susceptible to frequent heat waves, which pose serious health and safety risks to commuters and transit workers. Yet, the patterns and extent of temperature changes within the subway system remain insufficiently understood. To address this gap, we conducted a study of thermal variability across 112 subway stations in New York City. Temperature and relative humidity were measured at both platform and street levels. Results show that during the summer, 70% of subway stations had platform heat index higher than street level heat index. We also developed a machine learning forecasting model to predict platform-level temperatures. …

The Willing And The Compelled: How Power And Place Shape Human-Bison Coexistence In Poland And The United States, Patrick Orville Kelly

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildlife recovery programs depend not just on animals, but on how people feel about living with them. The degree to which people accept the disruptions wildlife brings to their lives, known as social tolerance, can determine whether a recovery program succeeds or fails, yet it remains understudied and underapplied in management plans. This dissertation studies how people in Poland and the western United States feel about living with the two living species of bison, the largest land animals in Europe and North America. In Poland, we surveyed 242 residents living at different distances from European bison habitat. Surprisingly, the most …

Scalable Quality Assessment Of Ground Motion Records Via Interpretable Deep Learning Architectures, Ali Montazeri Namin

All Graduate Theses and Dissertations, Fall 2023 to Present

Earthquake engineers rely on accurate recordings of ground shaking to design safe and resilient buildings. However, sorting through thousands of these recordings to find the reliable ones and throwing out those ruined by sensor errors or background noise is traditionally done by hand. Because modern seismic networks collect massive amounts of earthquake data every day, this manual checking process is much too slow. To fix this, researchers are turning to artificial intelligence to automatically check the quality of these recordings.

While artificial intelligence offers a fast solution, there are limitations. These computer models can become massive and expensive to run, …

Cascaded Pid Control Of A Passive Bio-Inspired Rotating Empennage Aircraft, Zachary T. Jenkins

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis presents an autopilot for a Bio-Inspired Rotating Empennage (BIRE) aircraft. Most airplanes control yaw using a vertical stabilizer and a moving rudder, a design that has stayed essentially unchanged for nearly a century but adds weight, drag, and surface area to the aircraft. The BIRE removes the vertical tail entirely and instead allows the horizontal tail to rotate about the body x-axis of the airplane; tilting the horizontal tail produces a yaw force, doing the job of a rudder. This thesis first develops a controller for the BIRE where the empennage angle is commanded directly, then extends …

Flight System Development For A Bio-Inspired Rotating Empennage Experimental Unmanned Aerial Vehicle, Ashton Gilbert

All Graduate Theses and Dissertations, Fall 2023 to Present

Unmanned aerial systems, commonly known as drones, are increasingly used for research, environmental monitoring, and testing new aircraft concepts. Many commercially available drone flight controllers work well for standard aircraft designs, but they can be difficult to adapt for experimental vehicles that use unconventional control methods or require detailed monitoring during testing. This creates challenges for researchers who need flexible and reliable tools to safely evaluate new flight technologies. This work presents the development of a custom drone flight control system designed specifically for experimental research. The system integrates custom electronics and software that allow researchers to easily modify control …

Physics-Data-Hybrid Modeling And Control For Vtol Aircraft, Samantha Dawn Burton

All Graduate Theses and Dissertations, Fall 2023 to Present

Vertical takeoff and landing (VTOL) aircraft are hybrid aircraft having both a helicopter flight mode and a fixed-wing plane flight mode. The objective of this dissertation is to examine and evaluate a new approach, physics-data-hybrid (PDH), for VTOL aircraft modeling and control. The PDH approach combines traditional, physics-based methods (Newtonian mechanics) with data driven methods (a machine learning technique called a Koopman operator model) to create an efficient and accurate modeling and control framework—mitigating the disadvantages of the purely physics-based and purely data-driven methods. The PDH approach is used to model scenarios when the VTOL aircraft transitions between helicopter mode …

Characterization Of Droplet Size And Their Distribution Using Low Operating Conditions In A Rotary Bell Cup Spray-Field Via Shadowgraph, Murad Ali Channa, Mehdi Khiadani, Yasir M. Al-Abdeli

Research outputs 2022 to 2026

This study characterises droplet size and distribution across axial (x/D) and radial (r/D) locations in the spray field of a rotary bell-cup atomizer under low operating conditions. Shadowgraph was used to measure droplet size and distribution at 16 dimensionless locations in both directions. Two low rotational-speed ranges, 3–3.5 krpm (case I) and 8–8.5 krpm (case II), were achieved using shaping-air of 30–100 L/min under turning-air of 50 and 60 L/min, respectively, at a constant flow rate of 0.5 L/min. The Sauter mean droplet diameter ranged from ∼ 45–98 µm in case I and narrowed to ∼ 45–85 µm in case …