Wireless fault tolerances decision using artificial intelligence technique

Wireless techniques utilized in industrial applications face significant challenges in preventing noise, collision, and data fusion, particularly when wireless sensors are used to identify and classify fault in real time for protection. This study will focus on the design of integrated wireless fault diagnosis system, which is protecting the induction motor (IM) from the vibration via decrease the speed. The filtering, signal processing, and Artificial Intelligent (AI) techniques are applied to improve the reliability and flexibility to prevent vibration increases on the IM. Wireless sensors of speed and vibration and card decision are designed based on the wireless application via the C++ related to the microcontroller, also, MATLAB coding was utilized to design the signal processing and the AI steps. The system was successful to identify the misalignment fault and dropping the speed when vibrations rising for preventing the damage may be happen on the IM. The vibration value reduced via the system producing response signal proportional with fault values based on modify the main speed signal to dropping the speed of IM

“Eat Smart to Play Hard”: a social martketing campaign to prevent obesity in Hispanic populations.

Presented at: 2016 University of South Florida Social Marketing Conference; June 17-18, 2016; Clearwater, FL.https://digitalrepository.unm.edu/prc-posters-presentations/1020/thumbnail.jp

Health impact assessment and federal trails policy: equity in public land access.

Presented at: New Mexico Public Health Association and New Mexico Center for the Advancement of Research, Engagement & Science on Health Disparities National Health Disparities 2014 Joint Conference; April 1-2, 2014; Albuquerque, NM.https://digitalrepository.unm.edu/prc-posters-presentations/1053/thumbnail.jp

Does she think she’s supported? Maternal perceptions of their experiences in the neonatal intensive care unit

Parents’ involvement in the care of their infants in the neonatal intensive care unit (NICU) is critically important, leading many NICUs to implement policies and practices of family-centered care (FCC). Analyzing narrative interviews, we examined whether mothers of premature infants who participated in an intervention to help reduce anxiety, stress, and depression felt that their NICU experience reflected four key nursing behaviors previously identified as being necessary to achieving FCC. Fifty-six narratives derived from semi-structured interviews with the mothers were analyzed qualitatively and quantitatively to examine whether the women experienced emotional support, parent empowerment, welcoming environment, and parent education, as well as whether differences in reported experiences were related to sociodemographic factors or maternal coping styles. Overall, the mothers reported more negative than positive experiences with respect to the four behaviors, and those who had negative interactions with the hospital staff felt a sense of disenfranchisement and failure as mothers. Sociodemographic factors and coping styles were significantly associated with the mothers’ perceptions of their experiences, although these relationships were not consistent. Achieving actual FCC in the NICU may require parent-informed evidence-based changes in NICU personnel training and infrastructure

A Yersinia Effector with Enhanced Inhibitory Activity on the NF-κB Pathway Activates the NLRP3/ASC/Caspase-1 Inflammasome in Macrophages

A type III secretion system (T3SS) in pathogenic Yersinia species functions to translocate Yop effectors, which modulate cytokine production and regulate cell death in macrophages. Distinct pathways of T3SS-dependent cell death and caspase-1 activation occur in Yersinia-infected macrophages. One pathway of cell death and caspase-1 activation in macrophages requires the effector YopJ. YopJ is an acetyltransferase that inactivates MAPK kinases and IKKβ to cause TLR4-dependent apoptosis in naïve macrophages. A YopJ isoform in Y. pestis KIM (YopJKIM) has two amino acid substitutions, F177L and K206E, not present in YopJ proteins of Y. pseudotuberculosis and Y. pestis CO92. As compared to other YopJ isoforms, YopJKIM causes increased apoptosis, caspase-1 activation, and secretion of IL-1β in Yersinia-infected macrophages. The molecular basis for increased apoptosis and activation of caspase-1 by YopJKIM in Yersinia-infected macrophages was studied. Site directed mutagenesis showed that the F177L and K206E substitutions in YopJKIM were important for enhanced apoptosis, caspase-1 activation, and IL-1β secretion. As compared to YopJCO92, YopJKIM displayed an enhanced capacity to inhibit phosphorylation of IκB-α in macrophages and to bind IKKβ in vitro. YopJKIM also showed a moderately increased ability to inhibit phosphorylation of MAPKs. Increased caspase-1 cleavage and IL-1β secretion occurred in IKKβ-deficient macrophages infected with Y. pestis expressing YopJCO92, confirming that the NF-κB pathway can negatively regulate inflammasome activation. K+ efflux, NLRP3 and ASC were important for secretion of IL-1β in response to Y. pestis KIM infection as shown using macrophages lacking inflammasome components or by the addition of exogenous KCl. These data show that caspase-1 is activated in naïve macrophages in response to infection with a pathogen that inhibits IKKβ and MAPK kinases and induces TLR4-dependent apoptosis. This pro-inflammatory form of apoptosis may represent an early innate immune response to highly virulent pathogens such as Y. pestis KIM that have evolved an enhanced ability to inhibit host signaling pathways

Sequence-Based Prediction of Type III Secreted Proteins

The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen–host interactions

Identify and classify vibration fault based on artificial intelligence techniques

Steam turbines (ST) need to be protected from damaging faults in the event it ends up in a danger zone. Some examples of faults include vibration, thrust, and eccentricity. Vibration fault represents one of the challenges to designers, as it could cause massive damages and its fault signal is rather complex. Researches in the field intend to prevent or diagnose vibration faults early in order to reduce the cost of maintenance and improve the reliability of machine production. This work aims to diagnose and classify vibration faults by utilized many schemes of Artificial Intelligence (AI) technique and signal processing, such as Fuzzy logic-Sugeno FIS (FLS), Back Propagation Neural Network (BPNN) hybrid with FL-Sugeno (NFS), and BPNN hybrid with FL-Mamdani FIS (NFM). The signal of the fault and the design of the FL and NN were done using MATLB. The results will be compared based on its ability to feed the output signal to the control system without disturbing system behavior. The results showed that the NFS scheme is able to generate linear and stable signals that could be fed to modify the main demand of the ST protection system. This work concluded that the hybrid of more than one AI technique will improve the reliability of protection system and generate smooth signals that are proportional to the fault level, which can then be used to control the speed and generated power in order to prevent the increase of vibration faults