Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829

Identification of enterprise carbon assets based on a business perspective

From a new perspective, this paper clarifies the internal and external factors affecting the carbon assets on the basis of induction of the connotation. It takes the enterprise business as the source of carbon assets, and makes an automotive group as an example, and establishes a network of its passenger car business activities based on the topological structure. This paper provides a method for identifying carbon assets from the relationship of business activities, and explains the formation mechanism of different assets from which it refines its network, and puts forward a reference to re-identify enterprise carbon assets from the perspective of development.

Identification of protein targets of nevirapine reactive metabolites using click chemistry and mass spectrometry-based differential proteomics

Abstract : Adverse drug reactions (ADRs) are undesirable effects caused after administration of a single dose or prolonged administration of drug or result from the combination of two or more drugs. Idiosyncratic drug reaction (IDR) is an adverse reaction that does not occur in most patients treated with a drug and does not involve the therapeutic effect of the drug. IDRs are unpredictable and often life-threatening. Idiosyncratic reaction is dependent on drug chemical characteristics or individual immunological response. IDRs are a major problem for drug development because they are usually not detected during clinical trials. In this study we focused on IDRs of Nevirapine (NVP), which is a non-nucleoside reverse transcriptase inhibitor used for the treatment of Human Immunodeficiency Virus (HIV) infections. The use of NVP is limited by a relatively high incidence of skin rash. NVP also causes a rash in female Brown Norway (BN) rats, which we use as animal model for this study. Our hypothesis is that idiosyncratic skin reactions associated with NVP treatment are due to post-translational modifications of proteins (e.g., glutathionylation) detectable by MS. The main objective of this study was to identify the proteins that are targeted by a reactive metabolite of Nevirapine in the skin. The specific objectives derived from the general objective were as follow: 1) To implement the click chemistry approach to detect proteins modified by a reactive NVP-Alkyne (NVP-ALK) metabolite. The purpose of using NVP-ALK was to couple it with Biotin using cycloaddition Click Chemistry reaction. 2) To detect protein modification using Western blotting and Mass Spectrometry techniques, which is important to understand the mechanism of NVP induced toxicity. 3) To identify the proteins using MASCOT search engine for protein identification, by comparing obtained spectrum from Mass Spectrometry with theoretical spectrum to find a matching peptide sequence. 4) To test if the drug or drug metabolites can cause harmful effects, as the induction of oxidative stress in cells (via protein glutathionylation). Oxidative stress causes cell damage that mediates signals, which likely induces the immune response. The results showed that Nevirapine is metabolized to a reactive metabolite, which causes protein modification. The extracted protein from the treated BN rats matched 10% of keratin, which implies that keratin was the protein targeted by the NVP-ALK.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

Design of unique composites based on aromatic thermosetting copolyesters

Aromatic thermosetting copolyester (ATSP) has promise in high-temperature applications. It can be employed as a bulk polymer, as a coating and as a matrix for carbon fiber composites (ATSP/C composites). This work focuses on the applications of high performance ATSP/C composites. The morphology of the ATSP matrix in the presence of carbon fiber was studied. The effect of liquid crystalline character of starting oligomers used to prepare ATSP on the final crystal structure of the ATSP/C composite was evaluated. Matrices obtained by crosslinking of both liquid crystalline oligomers (ATSP2) and non-liquid crystalline oligomers (ATSP1) tend to crystallize in presence of carbon fibers. The crystallite size of ATSP2 is 4 times that of ATSP1. Composites made from ATSP2 yield tougher matrices compared to those made from ATSP1. Thus toughened matrices could be achieved without incorporating any additives by just changing the morphology of the final polymer. The flammability characteristics of ATSP were also studied. The limiting oxygen index (LOI) of bulk ATSP was found to be 40% whereas that of ATSP/C composites is estimated to be 85%. Thus, ATSP shows potential to be used as a flame resistant material, and also as an aerospace reentry shield. Mechanical properties of the ATSP/C composite were characterized. ATSP was observed to bond strongly with reinforcing carbon fibers. The tensile strength, modulus and shear modulus were comparable to those of conventionally used high temperature epoxy resins. ATSP shows a unique capability for healing of interlaminar cracks on application of heat and pressure, via the Interchain Transesterification Reaction (ITR). ITR can also be used for reduction in void volume and healing of microcracks. Thus, ATSP resin systems provide a unique intrinsic repair mechanism compared to any other thermosetting systems in use today. Preliminary studies on measurement of residual stresses for ATSP/C composites indicate that the stresses induced are much lower than that in epoxy/C composites. Thermal fatigue testing suggests that ATSP shows better resistance to microcracking compared to epoxy resins.

Text segmentation using a cache memory

International audience

A Belief rule-based environmental responsibility assessment system for small and medium-sized enterprises

Various environmental management systems, standards and tools are being created to assist companies to become more environmental friendly. However, not all the enterprises have adopted environmental policies in the same scale and range. Additionally, there is no existing guide to help them determine their level of environmental responsibility and subsequently, provide support to enable them to move forward towards environmental responsibility excellence. This research proposes the use of a Belief Rule-Based approach to assess an enterprise’s level commitment to environmental issues. The Environmental Responsibility BRB assessment system has been developed for this research. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses (using the Belief Rule-Based approach) will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB systems consist of two parts: Knowledge Base and Inference Engine. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining, where an inference starts iteratively searching for a pattern-match of the input and if-then clause. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.

Graph-based structural analysis of planar mechanisms.

Kinematic structure of planar mechanisms addresses the study of attributes determined exclusively by the joining pattern among the links forming a mechanism. The system group classification is central to the kinematic structure and consists of determining a sequence of kinematically and statically independent-simple chains which represent a modular basis for the kinematics and force analysis of the mechanism. This article presents a novel graph-based algorithm for structural analysis of planar mechanisms with closed-loop kinematic structure which determines a sequence of modules (Assur groups) representing the topology of the mechanism. The computational complexity analysis and proof of correctness of the implemented algorithm are provided. A case study is presented to illustrate the results of the devised method.

INVESTIGATION OF MAGNESIUM ION CHARGE STORAGE MECHANISM IN MANGANESE DIOXIDE

Magnesium (Mg) battery is considered as a promising candidate for the next generation battery technology that could potentially replace the current lithium (Li)-ion batteries due to the following factors. Magnesium possesses a higher volumetric capacity than commercialized Li-ion battery anode materials. Additionally, the low cost and high abundance of Mg compared to Li makes Mg batteries even more attractive. Moreover, unlike metallic Li anodes which have a tendency to develop a dendritic structure on the surface upon the cycling of the battery, Mg metal is known to be free from such a hazardous phenomenon. Due to these merits of Mg as an anode, the topic of rechargea¬ble Mg batteries has attracted considerable attention among researchers in the last few decades. However, the aforementioned advantages of Mg batteries have not been fully utilized due to the serious kinetic limitation of Mg2+ diffusion process in many hosting compounds which is believed to be due to a strong electrostatic interaction between divalent Mg2+ ions and hosting matrix. This serious kinetic hindrance is directly related to the lack of cathode materials for Mg battery that provide comparable electrochemical performances to that of Li-based system. Manganese oxide (MnO2) is one of the most well studied electrode materials due to its excellent electrochemical properties, including high Li+ ion capacity and relatively high operating voltage (i.e., ~ 4 V vs. Li/Li+ for LiMn2O4 and ~ 3.2 V vs. Mg/Mg2+). However, unlike the good electrochemical properties of MnO2 realized in Li-based systems, rather poor electrochemical performances have been reported in Mg based systems, particularly with low capacity and poor cycling performances. While the origin of the observed poor performances is believed to be due to the aforementioned strong ionic interaction between the Mg2+ ions and MnO2 lattice resulting in a limited diffusion of Mg2+ ions in MnO2, very little has been explored regarding the charge storage mechanism of MnO2 with divalent Mg2+ ions. This dissertation investigates the charge storage mechanism of MnO2, focusing on the insertion behaviors of divalent Mg2+ ions and exploring the origins of the limited Mg2+ insertion behavior in MnO2. It is found that the limited Mg2+ capacity in MnO2 can be significantly improved by introducing water molecules in the Mg electrolyte system, where the water molecules effectively mitigated the kinetic hindrance of Mg2+ insertion process. The combination of nanostructured MnO2 electrode and water effect provides a synergic effect demonstrating further enhanced Mg2+ insertion capability. Furthermore, it is demonstrated in this study that pre-cycling MnO2 electrodes in water-containing electrolyte activates MnO2 electrode, after which improved Mg2+ capacity is maintained in dry Mg electrolyte. Based on a series of XPS analysis, a conversion mechanism is proposed where magnesiated MnO2 undergoes a conversion reaction to Mg(OH)2 and MnOx and Mn(OH)y species in the presence of water molecules. This conversion process is believed to be the driving force that generates the improved Mg2+ capacity in MnO2 along with the water molecule’s charge screening effect. Finally, it is discussed that upon a consecutive cycling of MnO2 in the water-containing Mg electrolyte, structural water is generated within the MnO2 lattice, which is thought to be the origin of the observed activation phenomenon. The results provided in this dissertation highlight that the divalency of Mg2+ ions result in very different electrochemical behaviors than those of the well-studied monovalent Li+ ions towards MnO2.

I. Quantal effects in biochemical cooperativity and a proposed mechanism for the differentiation of calcium signaling in synaptic plasticity. II. Evolutionary algorithms for the optimization of methods in computational chemistry

In Part 1 of this thesis, we propose that biochemical cooperativity is a fundamentally non-ideal process. We show quantal effects underlying biochemical cooperativity and highlight apparent ergodic breaking at small volumes. The apparent ergodic breaking manifests itself in a divergence of deterministic and stochastic models. We further predict that this divergence of deterministic and stochastic results is a failure of the deterministic methods rather than an issue of stochastic simulations.

Ergodic breaking at small volumes may allow these molecular complexes to function as switches to a greater degree than has previously been shown. We propose that this ergodic breaking is a phenomenon that the synapse might exploit to differentiate Ca$^{2+}$ signaling that would lead to either the strengthening or weakening of a synapse. Techniques such as lattice-based statistics and rule-based modeling are tools that allow us to directly confront this non-ideality. A natural next step to understanding the chemical physics that underlies these processes is to consider \textit{in silico} specifically atomistic simulation methods that might augment our modeling efforts.

In the second part of this thesis, we use evolutionary algorithms to optimize \textit{in silico} methods that might be used to describe biochemical processes at the subcellular and molecular levels. While we have applied evolutionary algorithms to several methods, this thesis will focus on the optimization of charge equilibration methods. Accurate charges are essential to understanding the electrostatic interactions that are involved in ligand binding, as frequently discussed in the first part of this thesis.

Structural and Biochemical Characterization of Peroxiredoxin Q beta from Xylella fastidiosa CATALYTIC MECHANISM and HIGH REACTIVITY

The phytopathogenic bacterium Xylella fastidiosa is the etiological agent of various plant diseases. To survive under oxidative stress imposed by the host, microorganisms express antioxidant proteins, including cysteine-based peroxidases named peroxiredoxins. This work is a comprehensive analysis of the catalysis performed by PrxQ from X. fastidiosa (XfPrxQ) that belongs to a peroxiredoxin class still poorly characterized and previously considered as moderately reactive toward hydroperoxides. Contrary to these assumptions, our competitive kinetics studies have shown that the second-order rate constants of the peroxidase reactions of XfPrxQ with hydrogen peroxide and peroxynitrite are in the order of 107 and 106 M(-1) s(-1), respectively, which are as fast as the most efficient peroxidases. The XfPrxQ disulfides were only slightly reducible by dithiothreitol; therefore, the identification of a thioredoxin system as the probable biological reductant of XfPrxQ was a relevant finding. We also showed by site-specific mutagenesis and mass spectrometry that an intramolecular disulfide bond between Cys-47 and Cys-83 is generated during the catalytic cycle. Furthermore, we elucidated the crystal structure of XfPrxQ C47S in which Ser-47 and Cys-83 lie similar to 12.3 angstrom apart. Therefore, significant conformational changes are required for disulfide bond formation. In fact, circular dichroism data indicated that there was a significant redox-dependent unfolding of alpha-helices, which is probably triggered by the peroxidatic cysteine oxidation. Finally, we proposed a model that takes data from this work as well data as from the literature into account.

Antitumor effect and mechanism of action of polysaccharides extracted from Polygonum perfoliatum L whole plant in human lung carcinoma A549 cell line

Purpose: To optimize the extraction conditions of polysaccharides from Polygonum perfoliatum L. (PSDP) and to evaluate their anti-tumor activities on A549 cell line. Methods: Extraction of PSDP was optimized using Box-Behnken design (BBD). Three factors of response surface methodology (RSM) including extraction time, ratio of water to raw material and number of extractions were employed to optimize the yield of PSDP. The cytotoxic effect of PSDP on human lung carcinoma A549 cell line was evaluated in vivo, while its effects on expressions of caspase3, caspase-9, Bcl-2 and Bax were determined by western blot assay. Result: BBD was significant and applicable to PSDP extraction. Based on the contour plots, response surface plots and variance analysis, it predicted that the optimum conditions for PSDP extraction were: 1.58 h (extraction time); 30.18 mL/g (ratio of water to raw material); and 2.02 (number of extractions). PSDP had significant inhibitory effect on the growth of A549 cells in a concentration- and timedependent manner (p < 0.05). After treatment with PSDP, caspase-3, caspase-9 and Bax were significantly up-regulated (p < 0.05), whereas Bcl-2 was down-regulated, all concentration-dependently. Conclusion: RSM analysis is an appropriate method to optimize PSDP extraction. The results also indicate that PSDP has significant anti-tumor effect against A549 cells, most likely via inducing mitochondria-mediated apoptosis.

Insights into the Fallback Path of Best-Effort Hardware Transactional Memory Systems

Current industry proposals for Hardware Transactional Memory (HTM) focus on best-effort solutions (BE-HTM) where hardware limits are imposed on transactions. These designs may show a significant performance degradation due to high contention scenarios and different hardware and operating system limitations that abort transactions, e.g. cache overflows, hardware and software exceptions, etc. To deal with these events and to ensure forward progress, BE-HTM systems usually provide a software fallback path to execute a lock-based version of the code. In this paper, we propose a hardware implementation of an irrevocability mechanism as an alternative to the software fallback path to gain insight into the hardware improvements that could enhance the execution of such a fallback. Our mechanism anticipates the abort that causes the transaction serialization, and stalls other transactions in the system so that transactional work loss is mini- mized. In addition, we evaluate the main software fallback path approaches and propose the use of ticket locks that hold precise information of the number of transactions waiting to enter the fallback. Thus, the separation of transactional and fallback execution can be achieved in a precise manner. The evaluation is carried out using the Simics/GEMS simulator and the complete range of STAMP transactional suite benchmarks. We obtain significant performance benefits of around twice the speedup and an abort reduction of 50% over the software fallback path for a number of benchmarks.

A BACKING DEVICE BASED ON AN EMBEDDED STIFFENER AND RETRACTABLE INSERTION TOOL FOR THIN-FILM COCHLEAR ARRAYS

Intracochlear trauma from surgical insertion of bulky electrode arrays and inadequate pitch perception are areas of concern with current hand-assembled commercial cochlear implants. Parylene thin-film arrays with higher electrode densities and lower profiles are a potential solution, but lack rigidity and hence depend on manually fabricated permanently attached polyethylene terephthalate (PET) tubing based bulky backing devices. As a solution, we investigated a new backing device with two sub-systems. The first sub-system is a thin poly(lactic acid) (PLA) stiffener that will be embedded in the parylene array. The second sub-system is an attaching and detaching mechanism, utilizing a poly(N-vinylpyrrolidone)-block-poly(d,l-lactide) (PVP-b-PDLLA) copolymer-based biodegradable and water soluble adhesive, that will help to retract the PET insertion tool after implantation. As a proof-of-concept of sub-system one, a microfabrication process for patterning PLA stiffeners embedded in parylene has been developed. Conventional hotembossing, mechanical micromachining, and standard cleanroom processes were integrated for patterning fully released and discrete stiffeners coated with parylene. The released embedded stiffeners were thermoformed to demonstrate that imparting perimodiolar shapes to stiffener-embedded arrays will be possible. The developed process when integrated with the array fabrication process will allow fabrication of stiffener-embedded arrays in a single process. As a proof-of-concept of sub-system two, the feasibility of the attaching and detaching mechanism was demonstrated by adhering 1x and 1.5x scale PET tube-based insertion tools and PLA stiffeners embedded in parylene using the copolymer adhesive. The attached devices survived qualitative adhesion tests, thermoforming, and flexing. The viability of the detaching mechanism was tested by aging the assemblies in-vitro in phosphate buffer solution. The average detachment times, 2.6 minutes and 10 minutes for 1x and 1.5x scale devices respectively, were found to be clinically relevant with respect to the reported array insertion times during surgical implantation. Eventually, the stiffener-embedded arrays would not need to be permanently attached to current insertion tools which are left behind after implantation and congest the cochlear scala tympani chamber. Finally, a simulation-based approach for accelerated failure analysis of PLA stiffeners and characterization of PVP-b-PDLLA copolymer adhesive has been explored. The residual functional life of embedded PLA stiffeners exposed to body-fluid and thereby subjected to degradation and erosion has been estimated by simulating PLA stiffeners with different parylene coating failure types and different PLA types for a given parylene coating failure type. For characterizing the PVP-b-PDLLA copolymer adhesive, several formulations of the copolymer adhesive were simulated and compared based on the insertion tool detachment times that were predicted from the dissolution, degradation, and erosion behavior of the simulated adhesive formulations. Results indicate that the simulation-based approaches could be used to reduce the total number of time consuming and expensive in-vitro tests that must be conducted.

High-sensitivity refractive index sensor based on large-angle tilted fiber grating with carbon nanotube deposition

This paper presents a highly sensitive ambient refractive index (RI) sensor based on 81° tilted fiber grating (81°-TFG) structure UV-inscribed in standard telecom fiber (62.5μm cladding radius) with carbon nanotube (CNT) overlay deposition. The sensing mechanism is based on the ability of CNT to induce change in transmitted optical power and the high sensitivity of 81°-TFG to ambient refractive index. The thin CNT film with high refractive index enhances the cladding modes of the TFG, resulting in the significant interaction between the propagating light and the surrounding medium. Consequently, the surrounding RI change will induce not only the resonant wavelength shift but also the power intensity change of the attenuation band in the transmission spectrum. Result shows that the change in transmitted optical power produces a corresponding linear reduction in intensity with increment in RI values. The sample shows high sensitivities of ∼207.38nm/RIU, ∼241.79nm/RIU at RI range 1.344-1.374 and ∼113.09nm/RIU, ∼144.40nm/RIU at RI range 1.374-1.392 (for X-pol and Y-pol respectively). It also shows power intensity sensitivity of ∼ 65.728dBm/RIU and ∼ 45.898 (for X-pol and Y-pol respectively). The low thermal sensitivity property of the 81°-TFG offers reduction in thermal cross-sensitivity and enhances specificity of the sensor.