Texas A&M Engineering Experiment Station

In the United States 1, engineering education as an area of interest for curriculum development and pedagogical innovation emerged about 1890 to 1910 and its transition to a

PURPOSE: To compile a list of genes that have been reported to be affected by external ionizing radiation (IR) and to assess their performance as candidate biomarkers for individual human radiation dosimetry. METHODS: Eligible studies were identified through extensive searches of the online databases from 1978 to 2017. Original English-language publications of microarray studies assessing radiation-induced changes in gene expression levels in human blood after external IR were included. Genes identified in at least half of the selected studies were retained for bio-statistical analysis in order to evaluate their diagnostic ability. RESULTS: 24 studies met the criteria and were included in this study. Radiation-induced expression of 10,170 unique genes was identified and the 31 genes that have been identified in at least 50% of studies (12/24 studies) were selected for diagnostic power analysis. Twenty-seven genes showed a significant Spearman's correlation with radiation dose. Individually, TNFSF4, FDXR, MYC, ZMAT3 and GADD45A provided the best discrimination of radiation dose < 2 Gy and dose ≥ 2 Gy according to according to their maximized Youden's index (0.67, 0.55, 0.55, 0.55 and 0.53 respectively). Moreover, 12 combinations of three genes display an area under the Receiver Operating Curve (ROC) curve (AUC) = 1 reinforcing the concept of biomarker combinations instead of looking for an ideal and unique biomarker. CONCLUSION: Gene expression is a promising approach for radiation dosimetry assessment. A list of robust candidate biomarkers has been identified from analysis of the studies published to date, confirming for example the potential of well-known genes such as FDXR and TNFSF4 or highlighting other promising gene such as ZMAT3. However, heterogeneity in protocols and analysis methods will require additional studies to confirm these results.

The advent of shale gas and the increasing spread between the supply and demand curves for propylene present an opportunity for adopting alternative pathways to produce propylene. This study aims to investigate a sustainable process design approach to on-purpose propylene production. A hierarchical approach to sustainable process design is proposed and implemented in a case study with propane dehydrogenation as the process under consideration. A base case design was developed, and process integration and intensification techniques were applied to reduce dependence on external utilities and to lower the overall capital investment. Waste heat recovery and offgas recycle were additional options used to intensify the overall energy consumption of the process. Emissions from the process were calculated from the Environmental Protection Agency’s guidelines. Economic and environmental metrics were then used to study the impact of integration and intensification techniques. Up to 70% reductions in CO2 emissions were achieved as a result of this approach to sustainable design. The Sustainability Weighted Return on Investment metric was evaluated for all cases. Multiobjective decision making for the optimum design was facilitated by the sustainability metrics augmented with the traditional economic criteria.

We measured the change in specific heat of nitrate salt–alumina nanoparticle nanofluids at low nanoparticle concentration (less than 2% by mass) to understand how adding small amounts of nanoparticles affected this property. Alumina nanoparticles were dispersed in a eutectic of sodium nitrate and potassium nitrate (60:40 mole fraction) to create nanofluids using a two-step method. Neutron activation analysis was used to measure the actual mass fraction of the alumina nanoparticles in the nanofluids. The nominal mass fraction was always larger than the actual mass fraction, with differences up to 41%. The specific heat was measured using a modulated differential scanning calorimeter (MDSC). The results showed that there exists a parabolic relation between specific heat and mass fraction of alumina nanoparticles (maximum 30.6% enhancement at 0.78% actual mass fraction of alumina nanoparticles). The measurement uncertainty for the specific heat values was less than 4%. The stability of the specific heat values of the nanofluids was also examined; we found the nanoparticle concentration with the highest specific heat value shifted from 0.78% to 0.3% when the same samples were tested after one and two months.

Abstract Engine performance curves were obtained for crude, degummed, and degummed‐dewaxed sunflower oils and for crude, degummed, and alkali refined cottonseed oils using a single‐cylinder, precombustion chamber design diesel engine. Crude oils gave very poor performance and are considered unsuitable for use as alternative diesel fuels. Performance curves for processed sunflower and cotton‐seed oils were slightly better than for diesel fuel, but increased carbon deposits and lubricating oil fouling were noted. Although processed oils may be acceptable fuels for short‐term use, they are not recommended as alternative diesel fuels at this time.

Dry reforming of methane (DRM) is an important technology that utilizes CO2 to convert methane to a mixture of H2 and CO (syngas). Commercial applicability of DRM has been challenged by the high energy requirement, susceptibility to coke formation, and low-quality syngas (syngas ratio, H2/CO ∼ 1). On the other hand, DRM provides an attractive pathway to the cost-effective sequestration of CO2 via transformation to value-added chemicals and fuels. DRM may be used in conjunction with other reforming technologies to produce the needed quality of syngas and to exploit synergism in energy release and demand. In this work, an optimization-based approach is used to compare the carbon footprint of conventional reforming technologies with other processes involving DRM to produce syngas of different H2/CO ratios. Technical, economic, and environmental metrics are used to assess the various options. Additionally, the model accounts for the carbon footprint associated with the reforming process, catalyst regeneration, and other energy requirements. The results of the optimization formulation show that the CO2 fixation using DRM is highly dependent on the desired syngas ratio. Net CO2 fixation occurs only at low syngas ratios of 1 and below. The results also indicate that producing syngas through a parallel reforming network involving existing technologies (steam methane reforming and partial oxidation) with DRM does not result in overall CO2 emissions reduction. Finally, two novel process concepts have been studied—CO removal from DRM syngas (DRM + COSORB) and H2 addition from an external source. Both these cases, while producing high H2/CO ratio syngas, have potential in terms of CO2 emissions reduction and competitive operating costs but will have certain limitations. The DRM + COSORB (captured CO sold as feedstock) process was found to be the best among all options studied in terms of overall reduction of CO2 emissions and operating costs.

Tensor completion has become an effective computational tool in many real-world data-driven applications. Beyond traditional static setting, with the increasing popularity of high velocity streaming data, it requires efficient online processing without reconstructing the whole model from scratch. Existing work on streaming tensor completion is usually built upon the assumption that tensors only grow in one mode. Unfortunately, the assumption does not hold in many real-world situations in which tensors may grow in multiple modes, i.e., multi-aspect streaming tensors. Efficiently modeling and completing these incremental tensors without sacrificing its effectiveness remains a challenging task due to the uncertainty of tensor mode changes and complex data structure of multi-aspect streaming tensors. To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework (MAST) based on CANDECOMP/PARAFAC (CP) decomposition to track the subspace of general incremental tensors for completion. In addition, we investigate a special situation where time is one mode of the tensors, and leverage its extra structure information to improve the general framework towards higher effectiveness. Experimental results on four datasets collected from various real-world applications demonstrate the effectiveness and efficiency of the proposed framework.

The effect of surface roughness on the thermal microwave emission of soils has been studied with the Kirchoff approach. A model is presented that includes both the coherent and incoherent reflectivities of the rough surface. It is demonstrated that both the coherent and incoherent terms must be included in order for theoretical computations to provide good agreement with the experimental data, especially for wet soil, where surface roughness causes a dramatic increase in brightness temperature. In addition, the model including both the coherent and incoherent terms allows one to use the physically measured surface height deviations of the rough surface in the model. Previous models have required that an effective height, not equal to the actual surface height measurements, be used in order for theoretical computations to match measurements.

Isopropanol is a widely used solvent and chemical. The growing demand for isopropanol, the declining supply of typical feedstocks for manufacturing isopropanol, and the increasing prices necessitate the search for alternative, cost-effective, and sustainable pathways for the production of isopropanol. The objective of this work is to synthesize, screen, design, and assess alternate pathways to produce isopropanol. First, biomass- and fossil-based raw materials are considered along with plausible reaction pathways. A process synthesis and integration approach is used to create and prune the alternatives based on branching, matching, and prescreening. For the promising candidates, process simulation, design, and technoeconomic assessment are carried out to compare the options. Under the studied conditions, the results show that propane dehydrogenation followed by direct hydration is the most promising pathway based on profitability (while accounting for price volatility) as well as technical and environmental benefits.

In this research, Al-Fe-Cr quasicrystal (QC) reinforced Al-based metal matrix composites were in-situ manufactured by using selective laser melting (SLM) from the powder mixture. The parametrical optimization based on our previous work was performed with focus on laser scanning speed . From the optimized parameters, an almost dense (99.7%) free-crack sample was fabricated with an ultra-fine microstructure. A phase transition from decagonal QC Al 65 Cu 25 Fe 10 Cr 5 to icosahedral QC Al 91 Fe 4 Cr 5 could be observed as laser scanning speed decreases. Differential scanning calorimetry curves show that the QC phase is quiet stable until 500 °C. And then, the effects of annealing temperature on the microstructural and mechanical properties were determined. The results indicate that the recrystallization and growth behavior of α-Al grains could be prevented by QC particle during annealing. Furthermore, the growth of QC particle, which tends to form a porous structure , leads an improvement of Young modulus and decline of ductility. • Pore and crack free Al/quasicrystal composite was fabricated by SLM from powder mixture. • Melting, reaction and solidification behavior of quasicrystal was investigated. • Quasicrystal morphology changes from pentagon to porous reticulate after annealing. • Tensile strength and ductility decreases with the increment of annealing temperature.

While outlier detection has been intensively studied in many applications, interpretation is becoming increasingly important to help people trust and evaluate the developed detection models through providing intrinsic reasons why the given outliers are identified. It is a nontrivial task for interpreting the abnormality of outliers due to the distinct characteristics of different detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, contexts where outliers locate, as well as the relation between outliers and the contexts, are usually overlooked in existing interpretation frameworks. To tackle the issues, in this paper, we propose a Contextual Outlier INterpretation (COIN) framework to explain the abnormality of outliers spotted by detectors. The interpretability of an outlier is achieved through three aspects, i.e., outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework.

This review examines the recent advancements in transparent electrodes and their crucial role in multimodal sensing technologies. Transparent electrodes, notable for their optical transparency and electrical conductivity, are revolutionizing sensors by enabling the simultaneous detection of diverse physical, chemical, and biological signals. Materials like graphene, carbon nanotubes, and conductive polymers, which offer a balance between optical transparency, electrical conductivity, and mechanical flexibility, are at the forefront of this development. These electrodes are integral in various applications, from healthcare to solar cell technologies, enhancing sensor performance in complex environments. The paper addresses challenges in applying these electrodes, such as the need for mechanical flexibility, high optoelectronic performance, and biocompatibility. It explores new materials and innovative techniques to overcome these hurdles, aiming to broaden the capabilities of multimodal sensing devices. The review provides a comparative analysis of different transparent electrode materials, discussing their applications and the ongoing development of novel electrode systems for multimodal sensing. This exploration offers insights into future advancements in transparent electrodes, highlighting their transformative potential in bioelectronics and multimodal sensing technologies.

Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due to the heterogeneous types of information and the scale of real-world data. Meanwhile, it has been observed that local anomalies, which may align with global condition, are hard to be detected by existing algorithms with interpretability. Motivated by the observations, in this paper, we propose to study the problem of effective and efficient local anomaly detection in attributed networks. In particular, we design a collective way for modeling heterogeneous network and attribute information, and develop a novel and efficient distributed optimization algorithm to handle large-scale data. In the experiments, we compare the proposed framework with the state-of-the-art methods on both real and synthetic datasets, and demonstrate its effectiveness and efficiency through quantitative evaluation and case studies.

Abstract Shape memory polymers (SMPs) are smart materials that enable to transform back to their original shape from the deformed state when subjected to external stimuli. They have shown great potential used as sensors and actuators in diverse applications. However, current research on SMPs primarily focuses on the utilization of a single source of stimuli (e.g., electricity, magnetism, light, etc.), which heavily restricts their potential in complex circumstances. In this study, a novel approach is developed to fabricate multi‐layer electrically/magnetically dual‐driven shape memory composites (ML‐EMSMCs) based on a magnetic field‐assisted digital light processing (MF‐DLP) 4D printing technique. The fabricated ML‐EMSMCs contain alternating high electric conductive layers (up to 5.37 × 10 −3 S cm −1 ) and magnetic responsive layers (10.7 emu g −1 ), enabling Joule heat‐based and high‐frequency magnetic field induction‐based stimuli. Furthermore, the ML‐EMSMCs exhibited excellent shape memory behavior, good formability, and magnetic properties. The developed 4D printing techniques allows for the alignment of magnetic particles with a unidirectional magnetic field, significantly improving their shape recovery speed. The developed electrically/magnetically dual‐driven and photocurable SMP composites shed light on the development of actuators and sensors with multiple functionalities.

Extreme events cause significant damage and disruption to the manufacturing sector, associated supply chains, and adjacent communities. These disastrous shocks may include natural disasters (e.g., hurricanes, floods, earthquakes), pandemics, catastrophic economic collapses (e.g., price crash of oil and gas), and terrorist and cyber attacks. Although much research has addressed the resilience of infrastructure, very little work has targeted the resilience of the manufacturing processes. Even less work has addressed the topic of process design approaches to create disaster-resilient industrial processes. The objective of this paper is to provide perspectives on the use of process integration for developing disaster-resilient designs of industrial plants with focus on the process industries (e.g., chemical, petrochemical, oil, gas, specialty chemicals, pharmaceuticals, biorefining). Focus is given to three categories of extreme events: natural disasters, pandemics, and economic collapses. Although several principles have been proposed for resilience engineering of infrastructure, industrial processes have unique features that warrant a tailored discussion. To streamline the discussion, we have identified 12 principal strategies for creating disaster-resilient designs: Fail-safe by design, (2) Redundancy, (3) Reconfigurability, (4) Modularity/Mobility/Distributability, (5) Repurposability, (6) Flexibility, (7) Controllability, (8) Reliability, (9) Recoverability/restorability, (10) Rapidity, (11) Robustness, and (12) Resourcefulness. These strategies are generally applicable to the process industries because they transcend the specific type of the manufacturing facility. Examples of industrial applications are given to explain the resilience strategies and discuss the research challenges and potential use of process integration in the systematic development of design concepts and tools for resilient design. The paper concludes with a list of future directions and promising research opportunities.

Bis(2-ethylhexyl) phthalate (DEHP) is an endocrine disruptor commonly present in plastic products, such as PVC tubes and water bottles. In this work, a surface enhanced Raman spectroscopy (SERS) based aptasensor was developed and utilized for rapid, easy, sensitive, and specific detection of trace DEHP. A DEHP aptamer was immobilized on magnetic particles. Raman reporter molecule conjugated silver nanoparticles were clustered and coated with silica to provide a stable SERS signal. The SERS silica particle was then functionalized with 1,2,4-benzenetricarboxylic acid 1,2-bis(2-ethylhexyl) ester to increase its affinity to the DEHP aptamer. In the presence of a sample with DEHP, the high-affinity SERS silica particle competes with the DEHP molecule to bind with the aptamer on the magnetic particle. By measuring the signal of free SERS silica particles in the supernatant after magnetic separation, the concentration of DEHP in the sample was quantitatively determined. The developed DEHP aptasensor had a detection range from 0.008 to 182 nM and a limit of detection (LOD) of 8 pM. The aptasensor also showed high selectivity when exposed to interferents with analogous structures. The aptasensor was successfully tested for the detection of DEHP spiked in tap water, bottled water, and a carbonate beverage. The developed SERS-based aptasensor provides a rapid, sensitive, and easy-to-use method for the quantitative detection of DEHP in environmental and food analysis.

A Calcium Looping Process (CLP) is an emerging approach for Carbon Capture and Utilization (CCU). It is essentially a CO2 capture process that utilizes calcium oxide (CaO) as a sorbent for the removal of CO2. A concentrated stream of CO2 (∼96%) that is suitable for storage and reuse is produced in this process. The objective of this work is to use mass and energy integration to couple CLP with industrial facilities and power plants in order to enhance industrial symbiosis and reduce cost via the chemical conversion of CO2 into value-added products. Special attention is given to plants that generate large amount of CO2 and/or provide excess heat that can be used in driving CLP. A case study is solved to assess the integration of CLP with candidate processes including power plants, cement production, gas-to-liquid (GTL) facility, and chemical plants for the production of ammonia, urea, polymer, methanol and acetic acid. The solution to the case study shows the merits integrating CLP with processing facilities.

Shape Memory Alloys (SMA) are a unique type of material characterized by two properties that ordinary metals do not exhibit: Shape Memory Effect (SME) and pseudoelasticity. SMAs can be actuated mechanically and/or thermally, and these properties have already been exploited in a wide variety of engineering applications. The appearance of SMAs in space applications, however, is more recent. This paper presents the motivations leading to interest for SMAs in space applications, as well as an overview of their use from tested mechanisms to ones still in development. As will be shown, many SMA space applications are single use and thermally activated. Although heating is never a problem, cooling SMA actuators in a reasonable amount of time still has to be achieved. A thermoelectric cooling system that allows for thermal control will be presented. This active cooling can allow better thermal actuation of SMA mechanisms using the two way SME. The last section of the paper describes their suitability for passive vibration isolation during launch, with a simple design using SMA hollow tubes at the interface between the payload and the spacecraft.

Abstract Substantial increases in shale gas production due to advances in hydraulic fracturing have created tremendous monetization and sustainable development opportunities, one of which is liquified natural gas (LNG). The International Maritime Organization (IMO) has targeted reducing greenhouse gas (GHG) emissions from shipping by 50% by 2050. Conventional shipping fuels currently used are heavy fuel oil (HFO) and marine gas/diesel oil (MGO/MDO). There has been growing interest in using LNG as a shipping fuel because of its competitive cost, availability, and the presence of bunkering infrastructure. A thorough literature review of LNG life cycle GHG emissions shows variation depending on the following factors: shale gas extraction, pretreatment, pipeline transportation distance, liquefaction plant capacity/technology, and ship propulsion system. Compared to conventional fuels, LNG can reduce life cycle emissions up to 18%. Incorporating renewables‐based power generation in liquefaction can reduce emissions by a further 5%–10% (renewable‐assisted LNG). The reduction potential and economic effects of this modification on LNG cost are examined and it is shown that low wind‐based electricity prices can make renewable‐assisted LNG competitive. A comprehensive understanding of the factors impacting LNG emissions help identify the current and future potential of LNG in reducing shipping industry emissions and providing a short‐term transitional fuel until it is supplanted with decarbonized fuels. This paper also uses water‐energy nexus to examine the impact of responsible water management on the carbon footprint of LNG.

Abstract The use of magnetic resonance imaging (MRI) for monitoring multiphase displacement experiments for quantitative characterization of fluid saturation is demonstrated. Displacements are conducted with one fluid phase in a porous medium being immiscibly displaced by another. Our objective is to accurately measure porosity and saturation distributions corresponding to one spatial dimension. Measures for the accuracy and resolution, with which the properties are identified, are developed.