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Utah Center for Water Resources Research at Utah State University newsletter.

Diabetes is a common metabolic disorder, which effects people across all cultures globally. Lifelong distress is the cause of this disease which has no cure as of now. Various medications available in the market are too expensive and not easily affordable by all. Rural people rely on plant based Ayurvedic medications to heal diabetes as these contain anti-diabetic compounds. These phytoconstituents/anthocyanin derivatives work with several mechanisms that involve phytoconstituent interactions and target molecules in diabetic metabolism. Molecular docking analysis aids in finding out the interaction between receptors and ligands to identify the finest interaction which suits the target. In this case, the study proposed examining the bonding interactions of anti-diabetic compounds/anthocyanin derivatives derived from medicinal plants (Pelargonidin, Cyanidin, Delphinidin, Peonidin, Petunidin, Quercetin and pancreatic alpha-amylase (4X9Y)) with the help of the computational tool. ADME/T test helps decide different pharmacological and physicochemical analysis of lead atoms, degree of adsorption inside the cell, digestion rate, solvency, blood cerebrum boundary penetrability, cancer-causing nature and so on, which are the significant essentials prior to advertising a medication. Peonidin and Quercetin was proposing the best interactions. Nonetheless, to discover a better cure for diabetes, further in-vitro/ in vivo studies have to be carried out.

Recently, renewable energy sources have acquired more attention as compared with conventional energy sources due of their renewable and eco-friendly nature. DC-DC converters are crucial in their applications. Two different configurations based on the conventional boost converter are compared for renewable energy source applications in this paper. In one configuration, two boost converters are connected in series with a single switch (Quadratic Boost Converter), and in another, they are connected in parallel with two switches (Interleaved Boost Converter). The first configuration has a high voltage gain with a low duty cycle, while the second has a low input current ripple. The two configurations are compared in various aspects, like output voltage, voltage gain, inductor current, inductor current ripple and component counts. The configurations are designed in the MATLAB simulation environment by using the PLECS block set to validate the theoretical studies with ideal values. With a duty cycle value of 0.6 and a 12 V input voltage, the output voltage of the quadratic boost converter is 75 V and that of the interleaved boost converter is 30 V.

Alzheimer's Disease (AD) is a progressively debilitating form of dementia that affects millions of individuals worldwide. Although a vast amount of research has investigated the complex interplay between gut microbiota and neurodegeneration, the metaproteomic effects of microbiota on AD pathogenesis remain largely uncharted territory. This study aims to reveal the role of gut microbiota in AD pathogenesis, particularly regarding changes in the proteome and molecular pathways that are intricately linked to disease progression. We operated state-of-the-art Nano-Liquid Chromatography Mass Spectrometry (nLC-MS/MS) to compare the metaproteomic shifts of 3-month-old transgenic (3M-ALZ) and control (3M-ALM, Alzheimer's Littermate) mice, depicting the early onset of AD with those of 12-month-old ALZ and ALM mice displaying the late stage of AD. Combined with computational analysis, the outcomes of the gut-brain axis-focused inquiry furnish priceless knowledge regarding the intersection of gut microbiota and AD. Accordingly, our data indicate that the microbiota, proteome, and molecular changes in the intestine arise long before the manifestation of disease symptoms. Moreover, disparities exist between the normal-aged flora and the gut microbiota of late-stage AD mice, underscoring that the identified vital phyla, proteins, and pathways hold immense potential as markers for the early and late stages of AD. Our research endeavors to offer a comprehensive inquiry into the intricate interplay between gut microbiota and Alzheimer's Disease utilizing metaproteomic approaches, which have not been widely adopted in this domain. This highlights the exigency for further scientific exploration to elucidate the underlying mechanisms that govern this complex and multifaceted linkage., NA

Introduction: The number of people diagnosed with dementia is increasing, creating significant economic burden globally. With the progression of the disease, patients need a caregiver whose wellbeing is important for continuous care. Providing respite as a service, through sharing the responsibility of caregiving or support for the caregiver, is a costly initiative. A peer-to-peer online support platform for dementia caregivers, motivated by the sharing economy, putting exchange of knowhow, resources, and services at its center, has the potential to balance cost concerns with a search for respite. The aim of this research is to assess caregivers' intention to engage in peer-to-peer exchange. Methods: a survey including sociodemographic, technology use, and caregiving variables, structured questionnaires (Zarit caregiver burden, WHO brief quality of life scale, ADCS-ADL and chronic stress scale) were administered, January 2018-May 2019, in the dementia outpatient clinic of a university hospital, to a convenience sample of n = 203 individuals identifying themselves as primary caregivers. A path analysis exploring the drivers of an intention to engage in peer-to-peer service exchange was conducted. Results: in the path model, caregivers experiencing higher caregiver burden showed higher intention to engage (0.079, p < 0.001). Disease stage had no effect while patient activities of daily living, chronic social role related stressors of the caregiver and general quality of life were significant for the effect on the caregiver burden. Existing household support decreased the caregiver burden, affecting the intention to engage. Caregivers who can share more know-how demonstrate a higher intention to engage (0.579, p = 0.021). Caregiver technology affinity (0.458, p = 0.004) and ability and openness to seek professional help for psychological diagnoses (1.595, p = 0.012) also increased intention to engage. Conclusion: The model shows caregiver burden to be a major driver, a, This work was supported by a Koç University internal seed research program fund.

Membrane-based separation can offer significant energy savings overconventional separation methods. Given their highly customizable andporous structures, metal–organic frameworks- (MOFs) are considered asnext-generation membrane materials that can bring about high separationperformance and energy efficiency in various separation applications. Yet, theenormously large number of possible MOF structures necessitates thedevelopment and implementation of efficient modeling approaches toexpedite the design, discovery, and selection of optimal MOF-basedmembranes via directing the experimental efforts, time, and resources to thepotentially useful membrane materials. With the recent developments in thefield of atomic simulations and artificial intelligence methods, a new era ofmembrane modeling has started. This review focuses on the recent advancesmade and key strategies used in the modeling of MOF-based membranes andhighlight the huge potential of combining atomistic modeling of MOFs withmachine learning to explore very large number of MOF membranes andMOF/polymer composite membranes for gas separation. Opportunities andchallenges related to the implementation of data-driven approaches to extractuseful structure–property relations of MOF-based membranes and to producedesign principles for the high-performing MOF-based membranes arediscussed., European Union (EU); Horizon 2020; ERC-2017-Starting Grant; European Research Council (ERC); Research and Innovation Programme; COSMOS; Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2218 National Postdoctoral Research Fellowship Program

Artificial cilia integrating both actuation and sensing functions allow simultaneously sensing environmental properties and manipulating fluids in situ, which are promising for environment monitoring and fluidic applications. However, existing artificial cilia have limited ability to sense environmental cues in fluid flows that have versatile information encoded. This limits their potential to work in complex and dynamic fluid-filled environments. Here, we propose a generic actuation- enhanced sensing mechanism to sense complex environmental cues through the active interaction between artificial cilia and the surrounding fluidic environments. The proposed mechanism is based on fluid-cilia interaction by integrating soft robotic artificial cilia with flexible sen-sors. With a machine learning-based approach, complex environmental cues such as liquid viscosity, environment boundaries, and distributed fluid flows of a wide range of velocities can be sensed, which is beyond the capability of existing artificial cilia. As a proof of concept, we implement this mechanism on magnetically actuated cilia with integrated laser- induced graphene-based sensors and demonstrate sensing fluid apparent viscosity, environment boundaries, and fluid flow speed with a reconfigur-able sensitivity and range. The same principle could be potentially applied to other soft robotic systems integrating other actuation and sensing modalities for diverse environmental and fluidic applications., European Union (EU); Horizon 2020; European Research Council (ERC); Advanced Grant; SoMMoR Project; Max Planck Society; China Scholarship Council

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH4/H-2 separation. We studied 597 synthesized COFs for adsorption of a CH4/H-2 mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH4/H-2 selectivities, CH4 working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH4/H-2 separation using molecular simulations and the results showed that the top hypoCOFs have CH4 selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg(-1) under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH4/H-2 mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH4/H-2 mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH4/H-2 separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents., European Union (EU); Horizon 2020; Research and Innovation Programme; ERC-2017-Starting Grant; COSMOS; Scientific and Technological Research Council of Turkey (TÜBİTAK); 1001-Scientific and Technological Research Projects Funding Program

A simple yet accurate model for the transmission of surface plasmon polaritons (SPPs) in a finite metal-insulator-metal (MIM) waveguide to the sides of the apertures is proposed and demonstrated to be more accurate than the available models. It is as simple as using a magnetic current density across the plane of the aperture whose value is defined by the SPPs with any number of modes in the waveguide through the equivalence principle. Then, the generated SPPs on both sides of the aperture are extracted from the convolution integral of the equivalent current density and Green's function. As a result, the model provides the transmission coefficients of the SPPs in the MIM waveguide to the side walls of the aperture accurately and efficiently; not only for symmetric MIM waveguides with a single isolating layer but also non-symmetric ones with multi-layered insulating materials. The results are in very good agreement with those obtained by the finite-difference time-domain method and better than the other approximations available in literature for a wide range of aperture widths., NA

Underwater devices are critical for environmental applications. However, existing prototypes typically use bulky, noisy actuators and limited configurations. Consequently, they struggle to ensure noise-free and gentle inter-actions with underwater species when realizing practical functions. Therefore, we developed a jellyfish-like robotic platform enabled by a synergy of electrohydraulic actuators and a hybrid structure of rigid and soft components. Our 16-cm-diameter noise-free prototype could control the fluid flow to propel while manipulat-ing objects to be kept beneath its body without physical contact, thereby enabling safer interactions. Its against -gravity speed was up to 6.1 cm/s, substantially quicker than other examples in literature, while only requiring a low input power of around 100 mW. Moreover, using the platform, we demonstrated contact-based object ma-nipulation, fluidic mixing, shape adaptation, steering, wireless swimming, and cooperation of two to three robots. This study introduces a versatile jellyfish-like robotic platform with a wide range of functions for diverse applications., European Union (EU); Horizon 2020; European Research Council (ERC); Advanced Grant; SoMMoR Project; Max Planck Society; International Max Planck Research School for Intelligent Systems (IMPRS-IS)

Acoustically-driven bubbles at the micron scale can generate strong microstreaming flows in its surrounding fluidic medium. The tunable acoustic streaming strength of oscillating microbubbles and the diversity of the generated flow patterns enable the design of fast-moving microrobots with multimodal locomotion suitable for biomedical applications. The acoustic microrobots holding two coupled microbubbles inside a rigid body are presented; trapped bubbles inside the L-shaped structure with different orifices generate various streaming flows, thus allowing multiple degrees of freedom in locomotion. The streaming pattern and mean streaming speed depend on the intensity and frequency of the acoustic wave, which can trigger four dominant locomotion modes in the microrobot, denoted as translational and rotational, spinning, rotational, and translational modes. Next, the effect of various geometrical and actuation parameters on the control and navigation of the microrobot is investigated. Furthermore, the surface-slipping multimodal locomotion, flow mixing, particle manipulation capabilities, the effective interaction of high flow rates with cells, and subsequent cancerous cell lysing abilities of the proposed microrobot are demonstrated. Overall, these results introduce a design toolbox for the next generation of acoustic microrobots with higher degrees of freedom with multimodal locomotion in biomedical applications., The authors would like to thank A. Bhargava and P. Wrede for help on acoustic setup perpetration and its discussions, N.K. Subbaiah for help on Nanoscribe 3D printing. This work was funded by The Max Planck Society and Max Planck ETH Center for Learning Systems (CLS).r Open access funding enabled and organized by Projekt DEAL.

The rapid growth in the miniaturized mechanical and electronic devices industry has created the need for temporary attachment systems that can carry out pick-and-place and transfer printing tasks for fragile and tiny parts. Current systems are limited by a fundamental trade-off between adhesive strength and state-changing trigger force, which causes the need for a rapidly switchable adhesive. In this study, an elastomeric microstructure is presented combining a trapezoidal-prism-shaped (TPS) and a mushroom-shaped microstructure, which overcomes the trade-off with the help of the TPS structure. The optimal design exhibits a strong adhesive strength of 87.8 kPa and a negligible detachment strength of <0.07 kPa with a low trigger shear stress of 10.7 kPa on smooth glass surfaces. The large tip-to-stem ratio (50 to 20 mu m) enhances the suction effect, allowing the microstructure to maintain its adhesive performance even in wet conditions. Pick-and-place manipulation tasks of a single and an array of ultralight parts from micrometer to millimeter scales are performed to demonstrate the capability of handling fragile and tiny parts. Moreover, it demonstrates the ability to transfer parts across water and air interfaces. This proposed microstructure offers a facile solution for manipulating microscale fragile parts in dry and wet conditions., C.P., J.-K.K., and Y.W. contributed equally to this work. This work was funded by the Max Planck Society. C.P. thank the Hong Kong PhD Fellowship Scheme under Grant PF18-21218 for support. J.-K.K. and Y.W. thank the Alexander von Humboldt Foundation for the fellowship support. Open access funding enabled and organized by Projekt DEAL.

The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine. Prior studies on microneedles proposed designs based on the optimization of physical parameters through trial-and-error method. While these methods showed adequate results, it is possible to enhance the performance of a microneedle using a large dataset of parameters and their respective performance using advanced data analysis methods. Machine Learning (ML) offers the ability to mimic human learning behavior to expedite decision-making processes in biotechnology. In this study, the finite element analysis and ML models are combined to determine the optimal physical parameters for a microneedle design to maximize the amount of collected biological fluid. The fluid behavior in a microneedle patch is modeled using COMSOL Multiphysics (R), and the model is simulated with a set of initial physical and geometrical parameters in MATLAB (R) using LiveLink (TM). The mathematical model is used as the input to MATLAB's Bayesian Optimization function (bayesopt) and optimized for the maximum volumetric flow rate with pre-defined number of iterations. Within the parameter bounds, maximum volumetric flow rate is determined to be 21.16 mL min-1, which is 60% higher with respect to a system, where geometrical parameters are chosen randomly on average. This study introduces an online method for designing microneedles, where user can define the upper and lower bounds of the parameters to obtain an optimal design. The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Marie Sk?odowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers, and Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership award for financial support of this research. This work was partially supported by Science Academy's Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBİP), and Bilim Kahramanlari Dernegi The Young Scientist Award.

The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Skłodowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers, and Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership Award (120N019) for financial support of this research. This work was partially supported by Science Academy’s Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBİP), and Bilim Kahramanlari Dernegi The Young Scientist Award. This study was conducted using the service and infrastructure of Koç University Translational Medicine Research Center (KUTTAM).

Light-driven actuators have great potential in different types of applications but is still challenging to apply them in flying devices owing to their slow response, small deflection and force output. Here, the authors report a rotary flying photoactuator with fast rotation and rapid response. Light-driven actuators have great potential in different types of applications. However, it is still challenging to apply them in flying devices owing to their slow response, small deflection and force output and low frequency response. Herein, inspired by the structure of vine maple seeds, we report a helicopter-like rotary flying photoactuator (in response to 0.6 W/cm(2) near-infrared (NIR) light) with ultrafast rotation (similar to 7200 revolutions per minute) and rapid response (similar to 650 ms). This photoactuator is operated based on a fundamentally different mechanism that depends on the synergistic interactions between the photothermal graphene and the hygroscopic agar/silk fibroin components, the subsequent aerodynamically favorable airscrew formation, the jet propulsion, and the aerodynamics-based flying. The soft helicopter-like photoactuator exhibits controlled flight and steering behaviors, making it promising for applications in soft robotics and other miniature devices., This work was supported by National Key Research and Development Program of China (Grant No. 2018YFE0306105) and National Natural Science Foundation of China (Grant No. 22173068). This work is supported by Suzhou Key Laboratory of Functional Nano & Soft Materials, Collaborative Innovation Center of Suzhou Nano Science & Technology, the 111 Project, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices. It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Fund for Excellent Creative Research Teams of Jiangsu Higher Education Institutions and Suzhou Key Laboratory of Surface and Interface Intelligent Matter (SZS2022011). Also, it was supported by the National Science Foundation for Distinguished Young Scholars (51925301) (F.S.), Max Planck Society (M.S., W.H.), and the Fundamental Research Funds for the Central Universities (QNTD20; XK1902) (F.S.), Wanren Plan (wrjh201903) (F.S.), and Open Project of State Key Laboratory (sklssm2023) (F.S.).

The pro-inflammatory cytokine interleukin-1 (IL-1) drives the pathogenesis of several inflammatory diseases. Recent studies have revealed that 2-indolinones can modulate cytokine responses. Therefore, we screened several 2-indolinone derivatives in preliminary studies to develop agents with anti-IL-1 activity. First, the putative efficacies and binding interactions of 2-indolinones were evaluated by docking studies. Second, previously synthesized 5-fluoro/(trifluoromethoxy)-1H-indole-2,3-dione 3-(4-phenylthiosemicarbazones) (compounds 47-69) which had the highest inhibitory effect in the screening were evaluated for inhibitory effects on the IL-1 receptor (IL-1R). Compounds 52 (IC50 = 0.09 mu M) and 65 (IC50 = 0.07 mu M) were selected as lead compounds for the subsequent synthesis of new derivatives. The novel 5-fluoro/(trifluoromethoxy)-1H-indole-2,3-dione 3-(4-phenylthiosemicarbazones) (compounds 70-116) were designed, synthesized, and in vitro studies were completed. The compounds 76, 78, 81, 91, 100, 105, and 107 tested showed nontoxic inhibitory effects on IL-1R-dependent responses in the range of 0.01-0.06 mu M and stronger than the lead compounds 52 and 65. In vitro and in silico findings showed that compounds 78 (IC50 = 0.01 mu M) and 81 (IC50 = 0.02 mu M) had the strongest IL-1R inhibitory effects and the most favorable drug-like properties. Molecular modeling studies of the compounds 78 and 81 were carried out to determine the possible binding interactions at the active site of the IL-1R. Novel 5-fluoro/(trifluoromethoxy)-2-indolinone derivatives were designed and synthesized based on in silico and preliminary in vitro test results from lead compounds. All compounds tested displayed nontoxic IL-1 receptor inhibitory effects at IC50 values in the range of 10 nM to 13 mu M, and seven compounds showed inhibitory responses stronger than the lead compounds at 0.01-0.06 mu M.image, Scientific and Technological Research Council of Turkey (TÜBİTAK)

The process of information carrying molecules (IcM) dispersing from a molecular transmitter (MTx) to a molecular receiver (MRx) and the process of these molecules binding to the MRx are two major components of the Molecular Communications (MC). There are many approaches to model these two processes separately. While these models are very efficient when used alone, they suffer from compatibility issues. In this work, we reconcile the models for these two processes in a unified framework. Our proposed framework is influenced by the wave-particle duality of light. We model the dispersion of molecules using the wave model and the interactions using the particle model. We demonstrate our algorithm specifically for the case of synaptic molecular communication (SMC) for a cuboid synaptic cleft, however it can be adapted to any MC system as long as the MTx and MRx exhibit only insignificant positional changes relative to each other., This work was supported by the AXA Research Fund (AXA Chair for Internet of Everything at Koç University).

Many real-world computations involve sparse data structures in the form of sparse matrices. A common strategy for optimizing sparse matrix operations is to reorder a matrix to improve data locality. However, it's not always clear whether reordering will provide benefits over the unordered matrix, as its effectiveness depends on several factors, such as structural features of the matrix, the reordering algorithm and the hardware that is used. This paper aims to establish the relationship between matrix reordering algorithms and the performance of sparse matrix operations. We thoroughly evaluate six different matrix reordering algorithms on 490 matrices across eight multicore architectures, focusing on the commonly used sparse matrix-vector multiplication (SpMV) kernel. We find that reordering based on graph partitioning provides better SpMV performance than the alternatives for a large majority of matrices, and that the resulting performance is explained through a combination of data locality and load balancing concerns., Scientific and Technological Research Council of Turkey (TÜBİTAK); European Union (EU); SparCity Project; European High-Performance Computing Joint Undertaking; Fundação para a Ciência e a Tecnologia (FCT); Research Council of Norway

Bioprinting as an extension of 3D printing offers capabilities for printing tissues and organs for application in biomedical engineering. Conducting bioprinting in space, where the gravity is zero, can enable new frontiers in tissue engineering. Fabrication of soft tissues, which usually collapse under their own weight, can be accelerated in microgravity conditions as the external forces are eliminated. Furthermore, human colonization in space can be supported by providing critical needs of life and ecosystems by 3D bioprinting without relying on cargos from Earth, e.g., by development and long-term employment of living engineered filters (such as sea sponges-known as critical for initiating and maintaining an ecosystem). This review covers bioprinting methods in microgravity along with providing an analysis on the process of shipping bioprinters to space and presenting a perspective on the prospects of zero-gravity bioprinting., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Celebi Transforming Systems Through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanları Derneği The Young Scientist Award

Research on molecular crystals exhibiting light-driven actuation has made remarkable progress through the development of various molecules and the identification of driving mechanisms. However, crystals developed to date have been driven mainly by ultraviolet (UV) or blue light irradiation, and driving by red or near-infrared (NIR) light has not been attempted yet. Herein, a broad-wavelength light-driven molecular crystals that exhibit high-speed bending by photothermal effect is developed. Titanium carbide (Ti3C2Tx) MXene nanosheets are integrated into salicylideneaniline crystals to extend the wavelength range that causes photothermally driven bending to UV, visible, and NIR light. In addition, unlike the thin pristine molecular crystals that show slow photoisomerization-induced bending only under UV light, the MXene layer enables the molecular crystals to be actuated rapidly regardless of their thickness over a wide range of wavelengths. The hybridization of molecular crystals with MXene, which exhibits strong biocompatibility as well as NIR light-driven photothermal effect, allows for the bending of the hybrid crystals inside agar phantoms mimicking biological tissue. Last, it is confirmed that MXene hybridization can be extended to common molecular crystals including various salicylideneaniline and anisole derivatives., D.W.K. and Y.H. contributed equally to this work. This research was financially supported by National Research Foundation of Korea (NRF-2022R1A6A3A03063349) for D.W.K, JSPS Grant-in-Aid for Scientific Research B (17H03107) for H.K., JSPS Research Fellowship for Young Scientists (21J20125) for Y.H., JSPS Overseas Challenge Program for Young Researchers for Y.H., and the Grant-in-Aid for Young Scientists (Early Bird) at Waseda Research Institute for Science and Engineering for Y.H. Y.H. and S.H. thank the Graduate Program for Power Energy Professionals in Waseda University. Y.H. thanks to Mayuka Iida for her help in experiments. This work was funded by the Max Planck Society. Open access funding enabled and organized by Projekt DEAL.

Extracellula r matri x (ECM)-derived hydrogels are in demand for use in lung tissue engineering to mimic the native microenvironment of cells in vitro. Decellularization of native tissues has been pursued for preser v i n g organotypic ECM while eliminating cellular content and reconstitution into scaffolds which allows re-cellularization for modeling homeostasis, regeneration, or diseases. Achieving mechanical stabi l i t y and understanding the effects of the decellularization process on mechanical parameters of the reconstituted ECM hydrogels present a challenge in the field. Stiffness and viscoelasticity are important characteristics of tissue mechanics that regulate crucial cellular processes and their in vitro representation in engineered models is a current aspiration. The effect of decellulariza-tion on viscoelastic properties of resulting ECM hydrogels has not yet been addressed. The aim of this study was to establish bovine lung tissue decellularization for the first time via pursuing four different protocols and characterization of reconstituted decellularized lung ECM hydrogels for biochemical and mechanical properties. Our data reveal that bovine lungs provide a reproducible alternative to human lungs for disease modeling with optimal retention of ECM components upon decellularization. We demonstrate that the decellularization method significa n t l y affects ECM content, stiffness, and viscoelastic properties of resulting hydrogels. Lastly, we examined the impact of these aspects on viabi l i t y , morphology, and growth of lung cancer cells, healthy bronchial epithelial cells, and patient-derived lung organoids., This work was funded by the International Fellowship for Outstanding Researchers Program of Scientific and Technological Research Council of Turkey (TÜBİTAK) (grant no. 118C238) and Marie Sklodowska-Curie Individual Fellowship (MiTuMi, grant no. 101032602).

Recent materials, microfabrication, and biotechnology improvements have introduced numerous exciting bioelectronic devices based on piezoelectric materials. There is an intriguing evolution from conventional unrecyclable materials to biodegradable, green, and biocompatible functional materials. As a fundamental electromechanical coupling material in numerous applications, novel piezoelectric materials with a feature of degradability and desired electrical and mechanical properties are being developed for future wearable and implantable bioelectronics. These bioelectronics can be easily integrated with biological systems for applications, including sensing physiological signals, diagnosing medical problems, opening the blood-brain barrier, and stimulating healing or tissue growth. Therefore, the generation of piezoelectricity from natural and synthetic bioresorbable polymers has drawn great attention in the research field. Herein, the significant and recent advancements in biodegradable piezoelectric materials, including natural and synthetic polymers, their principles, advanced applications, and challenges for medical uses, are reviewed thoroughly. The degradation methods of these piezoelectric materials through in vitro and in vivo studies are also investigated. These improvements in biodegradable piezoelectric materials and microsystems could enable new applications in the biomedical field. In the end, potential research opportunities regarding the practical applications are pointed out that might be significant for new materials research., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232; TÜBİTAK 2244; TÜBİTAK 3501; European Union (EU); Horizon 2020; Marie Sk?odowska-Curie Individual Fellowship; Brain Watch

Recent progress in flexible sensors and piezoelectric materials has enabled the development of continuous monitoring systems for human physiological signals as wearable and implantable medical devices. However, their non-degradable characteristics also lead to the generation of a significant amount of non-decomposable electronic waste (e-waste) and necessitate a secondary surgery for implant removal. Herein, a flexible and biodegradable piezoelectric material for wearable and implantable devices that addresses the problem of secondary surgery and e-waste while providing a high-performance platform for continuous and seamless monitoring of human physiological signals and tactile stimuli is provided. The novel composition of bioresorbable poly(l-lactide) and glycine leads to flexible piezoelectric devices for non-invasive measurement of artery pulse signals in near-surface arteries and slight movement of the muscle, including the trachea, esophagus, and movements of joints. The complete degradability of piezoelectric film in phosphate-buffered saline at 37 degrees C is also shown. The developed pressure sensor exhibits high sensitivity of 13.2 mV kPa(-1) with a response time of 10 ms and shows good mechanical stability. This piezoelectric material has comparable performance to commonly used non-degradable piezoelectric materials for measuring physiological signals. It can also be used in temporary implantable medical devices for monitoring due to its degradable nature., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232; European Union (EU); Horizon 2020; European Research Council (ERC)

Microparticle manipulation and trapping play pivotal roles in biotechnology. To achieve effective manipulation within fluidic flow conditions and confined spaces, it is necessary to consider the physical properties of microparticles and the types of trapping forces applied. While acoustic waves have shown potential for manipulating microparticles, the existing setups involve complex actuation mechanisms and unstable microbubbles. Consequently, the need persists for an easily deployable acoustic actuation setup with stable microparticles. Here, we propose the use of hollow borosilicate microparticles possessing a rigid thin shell, which can be efficiently trapped and manipulated using a single-lens focused ultrasound (FUS) transducer under physiologically relevant flow conditions. These hollow microparticles offer stability and advantageous acoustic properties. They can be scaled up and mass-produced, making them suitable for systemic delivery. Our research demonstrates the successful trapping dynamics of FUS within circular tubings of varying diameters, validating the effectiveness of the method under realistic flow rates and ultrasound amplitudes. We also showcase the ability to remove hollow microparticles by steering the FUS transducer against the flow. Furthermore, we present potential biomedical applications, such as active cell tagging and navigation in bifurcated channels as well as ultrasound imaging in mouse cadaver liver tissue., Open access funded by Max Planck Society. P.W. thanks ETH and Max Planck Center for Learning Systems for funding.

Utah Center for Water Resources Research at Utah State University newsletter.

A physical model of the simplest three-dimensional tensegrity unit was built at human-scale out of aluminum. Self-stress implementation and static loading tests were performed on this model. At each step, accurate measurements were obtained for all nodal positions and element forces. For the prestressing phase, elongations were imposed, via mechanical devices, in different combinations of elements, called prestress scenarios. Experimental results are compared to the theoretical self-stress state obtained by singular value decomposition of the equilibrium matrix and to numerical simulations using the dynamic relaxation method. It is shown that internal forces follow the same linear trend for all prestress scenarios even if the geometry is significantly impacted. Compressive tests were conducted by hanging masses from the top nodes. It is shown that there exists a unique load-displacement relation, that follows the “infinitesimal” mechanism direction for a “finite” distance which depends on the self-stress level. The paper provides a detailed overview of the simplex’s structural behavior using both experimental and numerical results while discussing the limitations of the analysis methods explored.

Traditional techniques of observing cracking within reinforced structures can be invasive, leading to an increased risk of added corrosion to structures already undergoing corrosive processes. The research presented in this document improves upon a nondestructive method for detecting early crack formation in reinforced concrete. This method includes using acoustic signaling to add a layer of salt water between the sensor and analyzed sample. Following the collection of surface and rebar echo responses, an adapted version of the novel Biot-Stoll method is used to model sound propagation for poro-elastic mediums. Testing of model parameters and variables has improved the root mean square error (RMSE) by up to 63.7% when studying the full signal, and up to 62.6% for the rebar echo locations. These improvements signify better curve fitting between simulated and measured responses, which lead to increased accuracy in the model parameter outputs., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Sickle Cell Disease (SCD) is a genetic disease that affects approximately 100,000 people in the USA and millions worldwide. The disease is defined by a mutation in hemoglobin, the red blood cell’s oxygen carrying component. Under hypoxic (low oxygen) conditions, the mutated hemoglobin (known as HbS) polymerizes into rigid fibers that stretch the cell into a sickle shape. These rigid cells can occlude blood vessels and cause an individual immense pain. Currently, no point-of-care devices exist in the market for assisting those with SCD. Using microfluidics with custom designed portable impedance measuring hardware we can achieve label-free in vitro analyses of SCD rheology. This dissertation presents two impedance-based devices for finger-prick volume blood testing, including a microflow cytometer for SCD diagnostics and a vaso-occlusion tester for monitoring blood flow activities. First, the microflow cytometer is validated by measuring the electrical impedance of individual cells flowing through a narrow microfluidic channel. Cellular impedance is interpreted by changes in subcellular components due to oxygen association-dissociation of hemoglobin, using an equivalent circuit model and Multiphysics simulation. Impedance values of sickle cells exhibit remarkable deviations from normal blood cells. Such deviation is quantified by a conformity score, which allows for measurement of SCD heterogeneity, and potentially disease severity. Findings from this study demonstrate the potential for SCD screening via electrical impedance. Second, a vaso-occlusion tester is validated by measuring the impedance response of blood flow within a microfluidic mimic of capillary bed., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Intermodal facilities, including port operations, play a significant role in the economic framework of the United States by making substantial contributions to the country's GDP, but face challenges managing increased freight volumes. However, increased transportation time within port facilities leads to higher costs, emissions, and impacts on efficiency and sustainability. This thesis aims to develop a concept of operations (ConOps) for improving the efficiency of heavy truck movement outside ports, with goals of reducing congestion, considering greenhouse gas (GHG) emissions, and addressing issues faced by the truck drivers. The study proposes integrating technological solutions to streamline heavy truck traffic at intermodal port facilities, including scheduled truck arrivals and departures, truck stop and rest areas near ports, real-time traffic information, implementation of dedicated truck lanes, and autonomous truck platooning. The focus is improving communication, efficiency, and safety for trucking companies, operations managers, and truck drivers. Using microsimulation modeling in PTV VISSIM (2023), a traffic impact study is also conducted, focusing on a case study near the Port of Miami. A base scenario is developed to represent current traffic conditions, and additional scenarios are implemented to evaluate different strategies, such as dedicated and exclusive truck lanes, freeway lane restrictions, and autonomous truck platooning. Simulation findings emphasize the positive impact of these strategies on travel times and delays, and forecast scenarios account for increased truck volumes. Dedicated truck lanes and truck platooning demonstrate promising results in reducing congestion and improving overall traffic flow. This research supports decision-making for government officials and logistics service providers in sustainable and efficient intermodal freight planning. The study also suggests opportunities for future extensions, including emerging technolo, 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Recent successes of Deep Learning-powered AI are largely due to the trio of: algorithms, GPU computing, and big data. Data could take the shape of hospital records, satellite images, or the text in this paragraph. Deep Learning algorithms typically need massive collections of data before they can make reliable predictions. This limitation inspired investigation into a class of techniques referred to as Data Augmentation. Data Augmentation was originally developed as a set of label-preserving transformations used in order to simulate large datasets from small ones. For example, imagine developing a classifier that categorizes images as either a “cat” or a “dog”. After initial collection and labeling, there may only be 500 of these images, which are not enough data points to train a Deep Learning model. By transforming these images with Data Augmentations such as rotations and brightness modifications, more labeled images are available for model training and classification! In addition to applications for learning from limited labeled data, Data Augmentation can also be used for generalization testing. For example, we can augment the test set to set the visual style of images to “winter” and see how that impacts the performance of a stop sign detector. The dissertation begins with an overview of Deep Learning methods such as neural network architectures, gradient descent optimization, and generalization testing. Following an initial description of this technology, the dissertation explains overfitting. Overfitting is the crux of Deep Learning methods in which improvements to the training set do not lead to improvements on the testing set. To the rescue are Data Augmentation techniques, of which the Dissertation presents an overview of the augmentations used for both image and text data, as well as the promising potential of generative data augmentation with models such as ChatGPT. The dissertation then describes three major experimental works revolving around CIFAR-10, 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

This research studied the effects of mooring line pretension, spring safe working load, and spring response curve on peak loads and platform surge. The maximum tension load from the optimized assembly was applied to a modelled section of 8-strand multiplait rope to study stress concentrations. The analyses yielded a mooring line pretensioned at 1250 kN with a 4500 kN safe working load degressive spring was optimal. Fatigue damage from 12-hour duration of 50-year storm conditions was 8.04 × 10−6. Infinite life is predicted at annual average conditions. The peak tension from 50-year storm conditions of 3671 kN and annual average conditions of 1388 kN was applied to the section model, yielding a maximum stress of 3.70 × 108 Pa and 2.01 × 108 Pa, respectively, from friction and longitudinal compression of the rope’s cross section. The maximum stress from the static structural analysis was 33.5% of polyester’s ultimate strength, based on the maximum stress failure criterion., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Online advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to display advertisements (ads) along with search results, posts, or page content. Online advertising is mainly based on dynamically selecting ads through a real-time bidding (or auction) mechanism. Predicting user responses like clicking ads in e-commerce platforms and internet-based advertising systems, as the first measurable user response, is an essential step for many digital advertising and recommendation systems to capture the user’s propensity to follow up actions, such as purchasing a product or subscribing to a service. To maximize revenue and user satisfaction, online advertising platforms must predict the expected user behavior of each displayed advertisement and maximize the user’s expectations of clicking [28]. Based on this observed feedback, these systems are tailored to user preferences to decide the order in that ads or any promoted content should be served to them. This objective provides an incentive to develop new research by using ideas derived from different domains like machine learning and data mining combined with models for information retrieval and mathematical optimization. They introduce different machine learning and data mining methods that employ deep learning-based predictive models to learn the representation of input features with the aim of user response prediction. Feature representation learning is known as a fundamental task on how to input information is going to be represented in machine learning models. A good feature representation learni, 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

In this thesis, we will explore different kinds of metamaterial or architectural structural problems, including structures composed of heterogeneous media with bi periodic sub-structures, discrete structures with sub-elements or continuous structures with discrete attached sub-elements. The thesis is composed of seven parts. After having introduced the specificities of metamaterial mechanics, the second chapter is devoted to the vibration of discrete beam problems called Hencky bar-chain model in a stochastic framework. It is shown that the lattice beam behaves as a nonlocal continuous beam problem, both in the deterministic and the non-deterministic analyses. The third chapter considers the vibration of continuous beams with the introduction of shear effects and attached periodically oscillators. A discussion on beam modelling, for example Timoshenko beam models or truncated Timoshenko beam models is included. It is shown that the bandgap phenomenon observed for metamaterial beams can be accurately captured by a truncated Timoshenko beam model which means the last term in the Timoshenko equation is not that important., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution. Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models. Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Vision is a critical sense for many species, with the perception of motion being a fundamental aspect. This aspect often provides richer information than static images for understanding the environment. Motion recognition is a relatively simple computation compared to shape recognition. Many creatures can discriminate moving objects quite well while having virtually no capacity for recognizing stationary objects. Traditional methods for collision-free navigation require the reconstruction of a 3D model of the environment before planning an action. These methods face numerous limitations as they are computationally expensive and struggle to scale in unstructured and dynamic environments with a multitude of moving objects. This thesis proposes a more scalable and efficient alternative approach without 3D reconstruction. We focus on visual motion cues, specifically ’visual looming’, the relative expansion of objects on an image sensor. This concept allows for the perception of collision threats and facilitates collision-free navigation in any environment, structured or unstructured, regardless of the vehicle’s movement or the number of moving objects present., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Today’s mainstream vehicles are partially automated via an advanced driver assistance feature (ADAS) known as Adaptive Cruise Control (ACC). ACC uses data from on-board sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations, ICE vehicles equipped with ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. On the other hand, ACC equipped EVs can potentially mitigate this effect for having ready torque and quicker acceleration. However, this has not been investigated for cases when lane changers enter from the adjacent lane. ACC could respond differently under these conditions, and this car following behavior is often referred as receiving lane change car following. Carefully planned field experiments on lane change car following demonstrate that lane changes and the subsequent receiving lane change car following from ICE vehicles equipped with ACC increases the gap unless the lane changer and the target lane traffic have identical or similar speeds for internal combustion engine (ICE) vehicles and ACC in the EVs doesn’t increase the gap after lane change increasing capacity for merging compared to ICE vehicles. For ICE, this trend also correlates with the selected ACC gap, with larger gap selection resulting in longer gap following the lane change maneuver and the receiving lane change car following in response. Larger gap setting shows better results after lane change for EVs., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

In today’s world, data is generated at an unprecedented rate, and a significant portion of it is unstructured text data. The recent advancements in Natural Language Processing have enabled computers to understand and interpret human language. Data mining techniques were once unable to use text data due to the high dimensionality of text processing models. This limitation was overcome with the ability to represent data as text. This thesis aims to compare the predictive performance of structured versus unstructured text data in two different applications. The first application is in the field of real estate. We compare the performance of tabular real-estate data and unstructured text descriptions of homes to predict the house price. The second application is in translating Electronic Health Records (EHR) tabular data to text data for survival classification of COVID-19 patients. Lastly, we present a range of strategies and perspectives for future research., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

In this research, we use calcite and celestite inorganic model systems to better understand biological crystallization in the presence of organic biomolecules. Our goal is to understand what happens when biomolecules occlude into crystals and how that affects the structural organization. Specifically, we focus on the role the respective biomolecule chemistry plays in regulating the incorporation into a crystal. To visualize and characterize the biomolecule/mineral role in crystallization, a variety of techniques were used to image and analyze the respective model systems. The synthesized single crystals were characterized by light microscopy (LM). Scanning electron microscopy (SEM) and field-emission SEM (FE-SEM) were used to examine the morphology of the crystals. Structural and topographical analyses were carried out using atomic force microscopy (AFM). Fourier transform infrared spectroscopy (FTIR) and confocal Raman microscopy were both used to characterize functional groups, where Raman spectroscopic mappings provided the region-specific chemical composition of the crystal., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Artificial reefs are coastal structures built to improve marine life and prevent beach erosion. During earlier days artificial reefs were constructed for recreational fishing using discarded scraps and waste materials. Later on, ships were scuttled for constructing artificial reefs. Artificial reefs dissipate the energy of the wave by making the wave break over the reef. The artificial reefs used for coastal protection are usually in submerged condition as this condition does not affect the aesthetic beauty of the beach. Wave transmission decides the efficiency of submerged-detached artificial reef in protecting the beach from the incoming waves. The efficiency of submerged detached coastal protection structures in protecting the beach is usually measured in terms of wave transmission coefficient. The experimental investigation in the present study is carried out for submerged two-dimensional impermeable and permeable reefs for three water depths. The crest width of the reefs considered for the experimental studies are 60 cm and 20 cm. The permeable artificial reefs are made up of oyster shells in Nylon bags and biodegradable bags. The water levels considered for the study are 35 cm, 34 cm, and 33 cm. The effect of pore space between the oyster shells, crest width, water depth and wave parameters on the wave transmission coefficient for submerged impermeable and permeable artificial reefs are studied experimentally. The wave transmission coefficient is calculated for submerged impermeable and permeable reefs for different water levels and crest widths. Based on the results of the present experimental studies, it is logical to conclude that both submerged impermeable and permeable artificial reefs contribute to a significant extent to the attenuation of the incident wave., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

The major objective of this dissertation was to create a framework which is used for medical image diagnosis. In this diagnosis, we brought classification and diagnosing of diseases through an Artificial Intelligence based framework, including COVID, Pneumonia, and Melanoma cancer through medical images. The algorithm ran on multiple datasets. A model was developed which detected the medical images through changing hyper-parameters. The aim of this work was to apply the new transfer learning framework DenseNet-201 for the diagnosis of the diseases and compare the results with the other deep learning models. The novelty in the proposed work was modifying the Dense Net 201 Algorithm, changing hyper parameters (source weights, Batch Size, Epochs, Architecture (number of neurons in hidden layer), learning rate and optimizer) to quantify the results. The novelty also included the training of the model by quantifying weights and in order to get more accuracy. During the data selection process, the data were cleaned, removing all the outliers. Data augmentation was used for the novel architecture to overcome overfitting and hence not producing false absurd results the computational performance was also observed. The proposed model results were also compared with the existing deep learning models and the algorithm was also tested on multiple datasets., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Reinforced concrete (RC) is the building block of modern architecture and industry. The failure of which is costly and dangerous. Typically made with carbon steel rebars, corrosion resistant alloys provide an alternative method of delaying failure. Stainless steels, while more expensive than carbon steels, provide excellent corrosion resistance, but less is known about the long term monitoring of corrosion activity for stainless steel than for carbon steel. This study looks at samples prepared between 2005 and 2009 using 304SS, 316SS, and 2304SS rebars, as well as SMI and Stelax stainless steel clad carbon steel reinforcements embedded in three different concrete mixes. These selected samples are split into two exposure environments, inside humidity chambers within the laboratory and outdoor exposure. Measurements reported here were made monthly over the course of 250 plus days using the Galvanostatic Pulse method, Electrochemical Impedance Spectroscopy, and a Gecor 8 device. These methods were used to determine corrosion current, isolated corrosion current density, and solution resistance. Corrosion current density values calculated from measurements by the Galvanostatic Pulse and Electrochemical Impedance Spectroscopy method are too small to indicate corrosion, based on value ranges provided by Andrade. However, Gecor 8 corrosion current density values indicate low levels or moderate levels of corrosion for all samples compared to the Andrade’s value ranges. The area used by the Gecor is unknown, so it’s possible this is driving up the measured values., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies due to its sizeable latent heat during phase change. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms, a primary source of enhanced heat dissipation. This thesis provides a comprehensive examination of the mechanisms involved in the growth of bubbles during nucleate boiling. By conducting a combination of experiments and numerical analyses, the goal is to enhance our understanding of bubble growth phenomena and their impact on heat transfer. Initially, the experimental work focuses on comparing the heat transfer performance and parameters related to bubble dynamics between regular and modified fin structures. The findings demonstrate that the modified fin structure, which featured artificial nucleation sites, exhibits superior heat transfer characteristics. This improvement is attributed to changes in the bubble departure diameter, bubble departure frequency, and growth time. Subsequently, an artificial neural network is developed to accurately predict the bubble departure diameter based on the wall superheat and subcooling level. This predictive model provides valuable insights into bubble behavior originating from artificial nucleation sites., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Today’s mainstream vehicles are partially automated via an Advanced Driver Assistance Feature (ADAS) known as Adaptive Cruise Control (ACC). ACC relies on data from onboard sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations for automated vehicles, ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. Fortunately, when ACC is paired with fully electric vehicles (EVs), EV’s unique powertrain characteristics such as instantaneous torque and aggressive regenerative braking could allow ACC to adopt shorter headways and accelerate more swiftly to maintain shorter headways during queue discharge, therefore reverse the negative impact on capacity. This has been verified in a series of car following field experiments. Field experiments demonstrate that EVs with ACC can achieve a capacity as high as 3333 veh/hr/lane when cruising in steady state conditions at typical freeway speeds (60 mph and 55 mph) and arterial speeds (45 mph and 35 mph). Furthermore, speed fluctuations and disturbances that may come from queues forming at or near the bottleneck do not reduce the capacity, unlike ACC-equipped internal combustion engine (ICE) vehicles, making ACC-equipped EVs outperform ICE vehicles with ACC, as well as human drivers., 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

As the global population is increasing, the generation of various waste materials (fats, oils and grease, fruit waste etc.) is increasing, which when landfilled, takes up valuable landfill space. Anaerobic digestion techniques have been developed that potentially convert these waste materials into energy and fertilizer, thus reducing landfill demand. It has been hypothesized that addition of high strength organic waste to conventional wastewater sludge can enhance the generation of onsite biogas at wastewater treatment plants, to meet the energy requirements of the plant partially or fully. To determine the anaerobic biodegradability of fats, oils and grease and fruit waste residuals, lab scale ultimate digestibility tests were conducted for a period of 63 days under mesophilic conditions. High strength organic wastes, thickened waste activated sludge and inoculum were mixed at 9 different ratios, and the mixtures were incubated in 500 mL serum bottles. After 63 days, the highest methane yield of 280 mL/gVS and 243 mL/gVS were obtained with mixtures containing 10% FOG with 10% red apples and 10% FOG only respectively whereas the methane yield of inoculum was only 8 mL/gVS. Preliminary cost analyses were conducted using the laboratory derived data, 2023, Includes bibliography., Degree granted: Thesis (MS)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection