# Texas State University

• San Marcos, Texas, United States
Recent publications
This study proposes a multi-use energy storage system (ESS) framework to participate in both price-based and incentive-based demand response programs with reinforcement learning (RL) on the demand side. We focused on industrial customers, to provide them the opportunity to obtain additional profits through market participation in addition to managing their load. Since industrial customers pay their electricity bills according to the time of use tariff structure, they can benefit if they can shift their electricity usage from high-price hours to low-price hours. Furthermore, they are able obtain additional incentives by fulfilling a dispatch signal from the system operator by using the ESS. To model the multi-use ESS by industrial users, we used the RL framework to make customer decisions. The RL approach uses a control action policy by interacting with an environment with no prior knowledge. For this, we formulated the ESS operation as a Markov decision process so that the environmental information obtained by the customers provides RL, which takes optimal actions for the current environment considering customer benefits. We developed several RL agents to identify an acceptable control agent. We utilized the actual industrial load profile in South Korea to train the RL agents. The experimental results demonstrated that the proposed framework can make near-optimal decisions for using ESS in multiple ways.
Background Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.
The sustainability of cementitious composites is currently analyzed by static methods, with little consideration of changes over time. In terms of Global Warming Potential (GWP), increasing the CO2 concentration in the atmosphere may negatively affect the planet. Therefore, estimating the imbalance between CO2 emitted and CO2 captured by these materials may be critical when assessing their environmental performance. This is the first study that proposes a new dynamic GWP method to analyze the sustainability of materials with the ability to uptake CO2 during their service life. For that, three main concrete mixtures are assessed: (i) a reference concrete with 100% Ordinary Portland cement (OPC); (ii) a concrete with 25% Fly Ash Class C (FA) + 75% OPC; and (iii) a concrete with 40% Ground Granulated Blast Furnace Slag (GGBFS) + 60% OPC. The investigation quantifies the GWP associated over a 100-year timeframe of a concrete mixture made at year 0 and based on four different CO2 uptake rates (reference, nano-modified A and B, and CO2 curing). Results demonstrate that while CO2-cured concretes exhibit the lowest GWP at year 0, nano-modified A concretes possess the lowest GWP associated at year 100 due to the acceleration of the CO2 uptake during their lifetime. Results also reveal that the GWP of nano-modified and CO2-cured concretes will be overestimated if the effects over time are not considered. Therefore, the dynamic methodology presented in this study should be employed when quantifying their GWP in cementitious composites. Moreover, this dynamic analysis could also be applied to other impact categories or even the complete holistic life cycle assessment.
Smart manufacturing is arriving [1]. It promises a future of highly responsive manufacturing operations with advanced sensing, reasoning, and decision-making capabilities towards mass personalization [2]. Statistical AI, e.g., machine learning technologies, has shown great potential in making manufacturing smart [3]. However, Statistical AI’s approximative, agnostic, and context- and task-specific nature has limited its implementation in real-world manufacturing contexts, which demand guaranteed product quality, robust system performance, and ubiquitous transparency. Semantic AI – the combination of Statistical AI and Symbolic AI technologies, could be the answer to the in-depth adoption of AI technologies in industry. Semantic AI enables interpretable manufacturing decisions with augmented intelligence via integrating the merits of statistical learning and semantic reasoning. This timely special issue contains ten articles demonstrating state-of-the-art achievements on Semantic AI, focusing on a variety of technologies, i.e., knowledge graph, semantic web, knowledge discovery, meta-heuristic algorithms, reinforcement learning, and deep learning, with novel applications in machining process automation, assembly troubleshooting, system simulation, production scheduling, 4D printing and robot automation.
3D printing of concrete is commonly viewed as a promising way to manufacture novel structural sections with numerous benefits. This process, however, relies upon a specific set of timely parameters that can have significant impact on the long-term performance and durability of the printed sections, especially since they are potentially expected to be exposed to the outside environments. In that respect, this article provides a critical review of the durability properties of 3D printed concrete (3DPC) sections, including the effect of printing parameters, mixture proportions, and key materials on shrinkage behavior, porosity and pore connectivity, freeze thawing, fire, chemical, and acid resistance. Based on this review, it is found that the thermo-durability properties of 3D printed concrete sections are highly sensitive to the shrinkage potential and printing time interval, which can significantly alter the porosity and pore connectivity of the printed concrete, especially at interlayer bonding regions. Suggestions for improving the durability of 3D printed concrete sections exposed to the various environments are also provided in the final part of this review.
Social entrepreneurship has a deservedly well-regarded reputation in the literature. Given the constraints and problems of modern society we are looking for social entrepreneurs to solve problems that government will not or cannot solve more now than ever. However, there is also a darker side to social entrepreneurs. In effect, social entrepreneurs become so involved in fixing problems that they justify behaviors that could be viewed as unethical. In essence, they do the “wrong thing for the right reason.” Using moral disengagement theory, we develop a typology to discuss issues related to social entrepreneurship.
Despite rising numbers of unaccompanied child migrants in the Americas, very limited research directly engages with youth as they journey north to seek protection in the United States. In this article, we examine young Central American migrant experiences of bordering, focusing on policing and shelter management. Part of a wider binational, interdisciplinary, and multi-scalar research project along the Mexico-U.S. border, which began on the heels of Programa Frontera Sur, we draw on interviews and a participatory workshop with migrant youth, and complementary interviews with migration officials and shelter workers. Through the uniquely insightful accounts of children themselves, we show how care work in shelters and direct control via policing emerge as powerful and connected techniques of bordering. In these spaces of connected securitization and humanitarian management, children negotiate highly violent, emotional, and extra-legal interactions with officials. These include extortion, apprehension, aggression, confinement and deception, but also disciplinary forms of care and protection. Our findings deepen and complicate extant work on the humanitarian care/control nexus via our focus on, and direct research with, youth from Central America in Mexico. Their narratives make clear that state policies such as Programa Frontera Sur expand the geographies of bordering and bring practices of migrant care and control into deeper relation. This bordering blocks children's access to legal protections like asylum; leaves them more exposed to exploitation and rights abuses; and encourages greater risk-taking in migration journeys.
The rock-cutting phenomenon can be considered a challenging problem from a numerical modeling point of view due to the complexity of the physics that comes from the interaction between the rock and the cutter. The present research was aimed at the presentation of a numerical simulation of the rock-cutting process based on the finite element method coupled with smoothed-particle hydrodynamics that was able to provide reasonable estimations of cutting forces for both shallow and deep cuts. Experimental scratch tests on the Vosges sandstone were utilized as modeling targets because all indispensable characteristics of rock cutting were encompassed by these tests. Five well-known material models, namely the soil and foam model (MAT_005), geologic cap model (MAT_025), concrete damage model (MAT_072R3), Johnson and Holmquist concrete model (MAT_111), and continuous surface cap model (MAT_159) in LS-DYNA, were calibrated for Vosges sandstone via the experimental triaxial and hydrostatic compression tests. The calibration process for the determination of the material parameters for each model was discussed in detail. Besides, the accuracy of each model was evaluated in predicting stress–strain behaviors of the rock both in compression and tension under different confining pressures. It was concluded that for Vosges sandstone, the model based on the calibrated parameters of MAT_72R3 is capable of proposing the most robust and reasonable predictions. Moreover, the calibration method can be widely used by occasional users in engineering applications of different types of geomaterials such as concrete, stone, and soil for convenient calibration of constitutive material models.
Frost Heaving Stress (FHS) is one of the main causes of freeze-thaw (FT) damage in porous cement concrete. This study customized a device for the measurement of FHS in the laboratory. Firstly, rodding, vibration, and static compaction methods were compared for the preparation of porous cement concrete in terms of the air void characteristics and air void distribution. Based on the proposed measurement device, the influence of curing time, air void, saturation degree, and freeze-thaw cycles on the FHS evolution were discussed, respectively. Besides, the release characteristics of FHS in the thawing process were also characterized. The results indicated that the evolution of FHS in the freezing process can be divided into three stages that accounted for the thermal contraction, phase transformation of water, and the end of phase transformation. The FHS of porous cement concrete can be reduced through the extension of curing or reduction of air void content. In general, the increase of saturation degree induced the growth of FHS. With respect to the release characteristics of FHS, the FHS would not completely dissipate and the remaining FHS would accumulate as the F-T cycle increased.
The concept spark joy has a long history in Japan but only a brief one in the U.S. This study involved interviews with 25 Japanese and 25 U.S. nationals to capture their knowledge of and interpretation for the popular concept of spark joy. We also looked for what objects brought out the emotion and the occurrence of particular meanings given to and characteristics of objects that spark joy in our participants. To spark joy generally referenced a positive emotion, though it was more specific for, lyrical, and ingrained in daily life for those from Japan, and it was tied more often to past memories for those from the U.S. For both groups, but particularly for those from the U.S., objects that sparked joy were likely to be seen as indispensable and, to a lesser extent, irreplaceable, reflecting an attachment to such objects. The objects that sparked joy typically had relational and/or self-expression meanings. Overall, the semiotic value of objects that spark joy has two sides: a combination of positive feeling and connection to self and/or other; given the owner’s belief that the objects are indispensable, however, they may also fear their loss.
ARTICLE scitation.org/journal/adv Dynamic viscosity of strontium ferrite-nylon composite below the melting temperature ABSTRACT Hard-magnetic 3D-printer filaments made of 40 wt. % SrO(Fe 2 O 3) 6 /PA12 composites made using a twin-screw extruder are being studied to be used for Magnetic Field Assisted Additive Manufacturing (MFAAM). The time dependence of the magnetic properties above the softening temperature of the PA12 matrix but below the melting point was studied using a biaxial Vibrating Sample Magnetometer (VSM). Specifically, the rotation of the magnetic particles in the softened polymer matrix after the application of a rotation field was extracted from the time dependent biaxial VSM signals. Above 132 ○ C, the strontium ferrite particles can rotate in the nylon matrix. The measured time constant decreases with temperature and magnitude of the rotation field. Model calculations indicate that, for 40 wt. % SrO(Fe 2 O 3) 6 /PA12 at rotation fields of 500 Oe, the effect of the demagnetizing field on the angle between the magnetic moment and the particle's easy axis is negligible, allowing one to determine the dynamic "melt" viscosity from the measured transients. The dynamic viscosity decreases from 2 × 10 5 Pa s at 132 ○ C to 3.1 × 10 4 Pa s at 175 ○ C with a sharp kink observed near 140 ○ C that correlates with a relaxation from the α ′ c phase for PA12 observed by others. A yield shear stress was observed for small rotation fields at low temperatures resulting in non-perfect alignment of the magnetic particles. The implications of the measurement results for MFAAM are discussed.
The codegree of an irreducible character $\chi$ of a finite group G is $|G : \ker \chi |/\chi (1)$ . We show that the Ree group ${}^2G_2(q)$ , where $q = 3^{2f+1}$ , is determined up to isomorphism by its set of codegrees.
Objective: This study examined acculturation with positive alcohol expectancies (PAE) and alcohol use intentions among college-bound Latinas using a bidimensional (ie U.S. acculturation/enculturation) and bidomain (ie behaviors/values) acculturation framework. Participants: A total of 298 Latina young adults between 18 and 20 years old were included in this analysis. Methods: Data were collected the summer before participants began college for the first time. We used an online survey to assess acculturation, PAE, and alcohol use expectancies. Results: Path analyses showed that U.S. acculturation values were related to more PAE and alcohol use intentions. U.S. acculturation behaviors were related to more alcohol use intentions, and the pathway was moderated by PAE. There was also an interaction between U.S. acculturation and enculturation behaviors predicting alcohol use intentions. Conclusion: This study sheds light on how acculturating Latina young women think about alcohol use prior to beginning college, which is an opportune window for targeted prevention programs.
Paleoclimate research in the Maya region of Mesoamerica provides compelling evidence of drought during key periods of cultural transition in Maya society. These include the transition from the Preclassic to the Classic, and from Classic to the Postclassic. Previous research emphasized a causal relationship between drought and cultural change, or so-called "collapse" in the Maya region. Recent advances in the range and precision of climate-sensitive proxies and the development of new archives have enabled quantitative reconstructions of past hydroclimate, as well as providing evidence of high impact, short-duration events, such as tropical cyclones. Simultaneously, archaeological research has unearthed widespread evidence of technologies used by the Maya to exert control over water resources in urban, rural, and agricultural settings. Evidence suggests that many of these water features were in use for multiple generations, possibly centuries, and many were constructed during the Terminal Preclassic and Terminal Classic periods. We suggest that, given the availability of new archaeological and paleoclimate records, these data can be combined to identify the full complexity of Maya adaptation to hydroclimate variability to emphasize adaptation and resilience to both water scarcity and overabundance (e.g., flooding). Such syntheses, which can offer lessons for present-day efforts to grapple with regional climate change, will benefit from additional studies in data-poor zones of the Maya region, as well as public archiving of paleoclimate and archaeological data.
To identify the current research involving interprofessional collaboration between registered nurses (RNs) and speech language pathologists (SLPs) in healthcare and educational settings. As the complexity of healthcare increases, the need for active interprofessional collaboration between RNs and SLPs grows. A review of the literature revealed no systematic reviews currently exist about interprofessional collaborative studies between RNs and SLPs. Researchers conducted a scoping review using PRISMA guidelines. Online databases were used to identify qualitative and quantitative research studies written in English and conducted between 2011 and 2020. Databases included Academic Search Ultimate, ASHA Wire, CINAHL, Cochrane Database of Systematic Reviews, ERIC, MEDLINE, PubMed, PsycINFO, and SEMANTIC SCHOLAR. The studies needed to focus on the interprofessional collaboration between RNs and SLPs or students in these professions. Of the 128 sources, only six studies met scoping review criteria. The primary focus of three studies was an evaluation of interprofessional education activities between nursing, speech language pathology, and other health profession students. One study explored interprofessional education in clinical practice between RNs and SLPs. Two studies explored interprofessional collaboration in the clinical setting. More research is needed that investigates interprofessional collaboration and practice of RNs and SLPs in the healthcare setting. This review identified the need for RNs and SLPs to work effectively as interprofessional teams are important in improving patient outcomes.
The lower Pecos River located in the southwest USA, is a naturally saline river system that has been significantly altered in relatively recent years. Climate change, coupled with anthropogenic disturbances such as dam construction have led to portions of the river becoming more susceptible to increased salinization and declines in water quality. These alterations have been documented to be detrimental to multiple freshwater communities; however, there is a lack of knowledge on how these alterations influence long-lived species in the river, such as freshwater turtles, where the effects can appear over dramatically different temporal scales. The Rio Grande Cooter (Pseudemys gorzugi) is a species of concern known to occur in the Pecos River. To understand the current distribution and habitat requirements for P. gorzugi in the Pecos River, we used a single-season, single-species occupancy modeling framework to estimate occurrence while accounting for the sampling process. Day of year, water surface area, and water visibility had the greatest influence on the ability to detect the species given a sampling unit is occupied. Conductivity (a measure of salinity) had the greatest influence on the occupancy probability for the species, where sites with higher conductivity coincided with lower occupancy probabilities. This study indicates that increased salinization on the lower Pecos River is a cause for concern regarding freshwater turtle populations within the Chihuahuan Desert.
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• Department of Computer Science
• Department of Physics
• Director Methodology, Measurement & Statistical Analysis