Nanostructured Alloys for High-Temperature Applications: A Study on Performance and Longevity

Nanostructured alloys have become a revolutionary material for high-temperature applications due to their exceptional mechanical properties, thermal stability, and resistance to environmental degradation. The present work focuses on the performance and durability of these materials with respect to extreme conditions in advanced engineering systems. Nano-oxides, for instance, Y2Ti2O7 pyrochlore have been found to play an essential role in optimizing creep resistance and tensile strength and also showing improved irradiation tolerance, thus finding applications in the fusion and fission reactors. Though significant amounts of progress in understanding the routes of composition processing to optimize properties have been accomplished, fabrication remains a difficult step in achieving reproducible performance because of defect introduction. This study also assesses the economic and practical feasibility of using nanostructured alloys in critical high-temperature environments, providing insight into their long-term reliability and potential for transformative industrial applications

Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.National Institutes of Health (U.S.) (NIH NIGMS grant R01GM086881)National Science Foundation (U.S.) (NSF Award #1001092)National Science Foundation (U.S.) (NSF Graduate Research Fellowship Program)Swiss National Science Foundation (SystemsX.ch grant

Effect of arsenic-phosphorus interaction on arsenic-induced oxidative stress in chickpea plants

Arsenic-induced oxidative stress in chickpea was investigated under glasshouse conditions in response to application of arsenic and phosphorus. Three levels of arsenic (0, 30 and 60 mg kg−1) and four levels of P (50, 100, 200, and 400 mg kg−1) were applied to soil-grown plants. Increasing levels of both arsenic and P significantly increased arsenic concentrations in the plants. Shoot growth was reduced with increased arsenic supply regardless of applied P levels. Applied arsenic induced oxidative stress in the plants, and the concentrations of H2O2 and lipid peroxidation were increased. Activity of superoxide dismutase (SOD) and concentrations of non-enzymatic antioxidants decreased in these plants, but activities of catalase (CAT) and ascorbate peroxidase (APX) were significantly increased under arsenic phytotoxicity. Increased supply of P decreased activities of CAT and APX, and decreased concentrations of non-enzymatic antioxidants, but the high-P plants had lowered lipid peroxidation. It can be concluded that P increased uptake of arsenic from the soil, probably by making it more available, but although plant growth was inhibited by arsenic the P may have partially protected the membranes from arsenic-induced oxidative stress

Capturing regional differences in flood vulnerability improves flood loss estimation

Flood vulnerability is quantified by loss models which are developed using either empirical or synthetic approaches. In reality, processes influencing flood risk are stochastic and loss predictions bear significant uncertainty, especially due to differences in vulnerability across exposed objects and regions. However, many state-of-the-art flood loss models are deterministic, i.e., they do not account for data and model uncertainty. The Bayesian Data-Driven Synthetic (BDDS) model was one of the first approaches that used empirical data to reduce the prediction errors at object-level and enhance the reliability of synthetic flood loss models. However, the BDDS model does not account for regional differences in vulnerability which may result in over-/under-estimation of losses in some regions. In order to overcome this limitation, this study introduces a hierarchical parameterization of the BDDS model which enhances synthetic flood loss model predictions by quantifying regional differences in vulnerability. The hierarchical parameterization makes optimal use of the process information contained in the overall data set for the various regional applications, so that it is particularly suitable for cases in which only a small amount of empirical data is available. The implementation and performance of the hierarchical parametrization is demonstrated with the Multi-Colored Manual (MCM) loss functions and empirical damage dataset from the UK consisting of residential buildings from the regions Appleby, Carlisle, Kendal and Cockermouth that suffered losses during the 2015 flood event. The developed model improves prediction accuracy of flood loss compared to MCM by reducing the absolute error and bias by at least 23 and 90%, respectively. The model reliability in terms of hit rate (i.e., the probability that the observed value lies in the 90% high density interval of predictions) is 88% for residential buildings from the same regions used for calibration and 73% for residential buildings from new regions. The approach is of high practical relevance for all regions where only limited amounts of empirical flood loss data is available

Authentication and Centralized Control of Electrical Devices Using Zigbee Protocol

In buildings, lighting accounts for more total energy cost, reducing this energy consumption is a major goal of this project. Energy reduction comes from turning off lights when they are not needed, optimizing light levels to suit worker needs. Through the use of modern enterprise-class wireless networking technology, the difficulty of control wiring is eliminated. In this project, the analysis, design and implementation of an intelligent office room whose two main components are realized using two emergent wireless technologies, namely, wireless sensor networks (ZigBee) and Radio-frequency identification (RFID) tags. The combination of these two technologies produces a powerful and versatile solution that can offer automated access control to an office room as well as the monitoring of entry or exit of an employee and also to perform automated job as described in the profile

Evaluation of relative roles of LH and FSH in regulation of differentiation of Leydig cells using an ethane 1,2-dimethylsulfonate-treated adult rat model

The relative role of LH and FSH in regulation of differentiation of Leydig cells was assessed using an ethane 1,2-dimethylsulfonate (EDS)-treated rat model in which endogenous LH or FSH was neutralized from day 3 to day 22 following EDS treatment. Serum testosterone and the in vitro response of the purified Leydig cells to human chorionic gonadotropin (hCG) was monitored. In addition RNA was isolated from the Leydig cells to monitor the steady-state mRNA levels by RT-PCR for 17α-hydroxylase, side chain cleavage enzyme, steroidogenic acute regulatory protein (StAR), LH receptor, estrogen receptor (ER-α) and cyclophilin (internal control). Serum testosterone was undetected and the isolated Leydig cells secreted negligible amount of testosterone on stimulation with hCG in the group of rats that were treated with LH antiserum following EDS treatment. RT-PCR analysis revealed the absence of message for cholesterol side chain cleavage enzyme and 17α-hydroxylase although ER-alpha and LH receptor mRNA could be detected, indicating the presence of undifferentiated precursor Leydig cells. In contrast, the effects following deprival of endogenous FSH were not as drastic as seen following LH neutralization. Deprival of endogenous FSH in EDS-treated rats led to a significant decrease in serum testosterone and in vitro response to hCG by the Leydig cells. Also, there was a significant decrease in the steady-state mRNA levels of 17α-hydroxylase, cholesterol side chain cleavage enzyme, LH receptor and StAR as assessed by a semiquantitative RT-PCR. These results establish that while LH is obligatory for the functional differentiation of Leydig cells, repopulation of precursor Leydig cells is independent of LH, and also unequivocally establish an important role for FSH in regulation of Leydig cell function

Initiation of the expression of peroxisome proliferator - activated receptor gamma (PPAR gamma) in the rat ovary and the role of FSH

PPARgamma is highly expressed in granulosa cells by 23 days post-partum (pp) and is down-regulated in response to the LH surge. We tested the hypothesis that high levels of FSH during the neonatal period trigger the expression of PPARgamma. To determine when PPARgamma expression is initiated, ovaries were collected from neonatal rats. Messenger RNA for PPARgamma was undetectable on day 1, low from days 5-14, and increased by day 19 pp (p < 0.05). PPARgamma was detected in select granulosa cells in primary/early secondary follicles. Messenger RNA for the FSH receptor was detected as early as day 1 and remained steady throughout day 19 pp. The FSH receptor was detected by immunoblot analysis in ovaries collected 1, 2, and 5-9 days pp. In a subsequent experiment, neonatal rats were treated with acyline (GnRH antagonist) which significantly reduced FSH (p < 0.05) but not levels of mRNA for PPARgamma. The role of FSH in the induction of PPARgamma expression was further assessed in ovarian tissue from FORKO mice. Both mRNA and protein for PPARgamma were identified in ovarian tissue from FORKO mice. In summary, the FSH/FSH receptor system is present in granulosa cells prior to the onset of expression of PPARgamma. Reducing FSH during the neonatal period, or the ability to respond to FSH, did not decrease expression of mRNA for PPARgamma. These data indicate that FSH is not a primary factor initiating the expression of PPARgamma and that other agents play a role in activating its expression in the ovary