A flexible nickel phthalocyanine resistive random access memory with multi-level data storage capability

Metal phthalocyanine is considered one of the most promising candidates for the design and fabrication of flexible resistive random access memory(RRAM)devices due to its intrinsic flexibility and excel-lent functionality.However,performance degradation and the lack of multi-level capability,which can directly expand the storage capacity in one memory cell without sacrificing additional layout area,are the primary obstacles to the use of metal phthalocyanine RRAMs in information storage.Here,a flex-ible RRAM with pristine nickel phthalocyanine(NiPc)as the resistive layer is reported for multi-level data storage.Due to its high trap-concentration,the charge transport behavior of the device agrees well with classical space charge limited conduction controlled by traps,leading to an excellent performance,including a high on-off current ratio of 107,a long-term retention of 106 s,a reproducible endurance over 6000 cycles,long-term flexibility at a bending strain of 0.6％,a write speed of 50 ns under sequential bias pulses and the capability of multi-level data storage with reliable retention and uniformity.     

Exceptional photocatalytic activity for g-C3N4 activated by H2O2 and integrated with Bi2S3 and Fe3O4 nanoparticles for removal of organic and inorganic pollutants

Herein, hydrogen peroxide activated graphitic carbon nitride (agCN) was combined with Fe₃O₄ and Bi₂S₃ to fabricate agCN/Fe₃O₄/Bi₂S₃ nanocomposites via facile refluxing method, as visible-light-induced photocatalysts for photodegradations of anionic and cationic dyes such as MO, RhB, MB, and photoreduction of Cr(VI). The fabricated samples were explored by XRD, EDX, XPS, TGA, SEM, TEM, HRTEM, VSM, PL, FT-IR, BET, and UV-vis DRS. Photocatalytic activity of the nanocomposite with 20% of Bi₂S₃ was 16.6, 40.4, 19.5, and 12.5 times more than that of the pristine gCN in removal of RhB, MB, MO, and Cr(VI), respectively. A plausible photocatalytic mechanism on the agCN/Fe₃O₄/Bi₂S₃ nanocomposites was proposed by construction of n-n heterojunction between gCN and Bi₂S₃. Also, stability of the magnetic hybrid was characterized through cyclic photocatalytic tests.     

Regular model checking for LTL(MSO)

Regular model checking is a form of symbolic model checking for parameterized and infinite-state systems whose states can be represented as words of arbitrary length over a finite alphabet, in which regular sets of words are used to represent sets of states. We present LTL(MSO), a combination of the logics monadic second-order logic (MSO) and LTL as a natural logic for expressing the temporal properties to be verified in regular model checking. In other words, LTL(MSO) is a natural specification language for both the system and the property under consideration. LTL(MSO) is a two-dimensional modal logic, where MSO is used for specifying properties of system states and transitions, and LTL is used for specifying temporal properties. In addition, the first-order quantification in MSO can be used to express properties parameterized on a position or process. We give a technique for model checking LTL(MSO), which is adapted from the automata-theoretic approach: a formula is translated to a buchi regular transition system with a regular set of accepting states, and regular model checking techniques are used to search for models. We have implemented the technique, and show its application to a number of parameterized algorithms from the literature.     

Semi-parametric Estimation for Selecting Optimal Threshold of Extreme Rainfall Events

The two primary approaches of extreme events analysis are annual maximum series (AMS), which fits Generalized Extreme Value (GEV) distribution to the yearly peaks of events in the observation period, and partial duration series (PDS), which fits Generalized Pareto (GP) distribution to the peaks of events that exceed a given threshold. The PDS is able to reduce sampling uncertainty and is more useful in dealing with extreme values and asymmetries in the tails, but the optimal threshold is required. The objective of this study is to compare and determine the best method for selecting the optimal threshold of PDS using the hourly, 12-h and 24-h aggregated data of rainfall time series in Peninsular Malaysia. The choice of the threshold, or the number of largest order statistics, can be estimated by the parameters of extreme events. In this study, thirteen semi-parametric estimators are considered and applied to estimate the shape parameter or extreme value index (EVI). A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. Based on the smallest MSE, the majority of stations and data durations favor the Adapted Hill estimator, followed by the QQ, Hill and Moment Ratio 1 estimators. Therefore, this study proves that the application of different estimators on real data may result in different optimal values of threshold and the choice of the best method is very much data-dependent.     

Applying Minimum Night Flow to Estimate Water Loss Using Statistical Modeling: A Case Study in Kinta Valley, Malaysia

Minimum night flow (MNF) is a common method used to evaluate water loss in a water network. In 2010, the average percentage of non-revenue water for the state of Perak in Malaysia was 29.4 %, a figure which resulted in major financial, supply, and pressure losses, as well as excessive energy consumption. In this study, a statistical analysis of the water distribution network and a modeling of MNF were carried out to estimate water loss in Kinta Valley, Perak. Flow and pressure for 361 zones were monitored for 24 h using PrimeWorks software (version: 1.5.57.0). Thirty study zones were randomly selected from 361 zones. MNF was screened within the time band of 1:00 am to 5:00 am. A total of 20 factors for physical, hydraulic, and operational variables were selected and correlated with MNF (L/s). Multiple linear regression was used as a statistical technique to determine factors that contributed to MNF (L/s). Consequently, pipe length (m) and pipe age (year) were the main contributors to MNF (L/s). The statistical model was finalized with R-Sq 0.706 and then improved to R-Sq 0.779. Results of the study revealed that 84.9 % of MNF frequencies for the 30 study areas were found at the time band 2:15 am to 4:15 am; therefore, the mean MNF for each zone in 2010 was determined to be between 1:00 am and 5:00 am. Statistical analyses showed that number of connections, total length of pipe, weighted mean of age of pipe, and type of pipe (100 mm asbestos cement) contributed to MNF. Moreover, approximately 97.5 % of registered repairs were conducted on pipes with small diameters of less than or equal to 50 mm. Pipes within this size range are usually used as service pipes and service connections.     

A study of rock bolting failure modes

Rock bolting has advanced rapidly during the past 4 decades due to a better understanding of load transfer mechanisms and advances made in the bolt system technology. Bolts are used as permanent and temporary support systems in tunnelling and mining operations. A review of has indicated that three systems of reinforcement devices have evolved as part of rock bolt and ground anchor while the rock is not generally thought of as being a component of the reinforcement system. A classification of rock bolting reinforcement systems is presented, followed by the fundamental theory of the load transfer mechanism. The failure mode of two phases of rock bolting system is formularised. The failure modes of cable bolting are discussed using a bond strength model as well as an iterative method. Finally, the interfacial shear stress model for ribbed bar is introduced and a closed form solution is obtained using a tri-line stress strain relationship.     

Large excavations and their effect on displacement of land boundaries

A study to estimate land surface movement caused by large surface excavations in sedimentary strata is presented. In stratified or jointed strata the stress relief driven movement adjacent to large excavations can be significantly larger than expected. High lateral stresses measured in Australia and other places around the world indicate that the ratio of horizontal to vertical stress has been particularly high at shallow depths. The in situ strata is in compression and during excavation, stress is relieved towards the opening causing strata movement. Large excavations such as, open cut mines or highway cuttings, can initiate an extensive horizontal slide of surface layers towards the excavation. These ground movements can be damaging to surface structures such as water storage dams and large buildings. Based on stress measurements at shallow depths in Australian coal mines the study presented here calculates and models the extent of potential ground movement along the bedding surface adjacent to large excavations and provides a new prediction tool of land movement at the excavation boundary that can benefit the geotechnical practitioners in the mining industry.     

Data mining applications in hydrocarbon exploration

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term “intelligent” is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.     

Verifying Programs with Unreliable Channels

We consider the verification of a particular class of infinite-state systems, namely systems consisting of finite-state processes that communicate via unbounded lossy FIFO channels. This class is able to model, e.g., link protocols such as the Alternating Bit Protocol and HDLC. For this class of systems, we show that several interesting verification problems are decidable by giving algorithms for verifying (1) thereachability problem—is a finite set of global states reachable from some other global state of the system ? (2)safety properties over tracesformulated as regular sets of allowed finite traces, and (3)eventuality properties—do all computations of a system eventually reach a given set of states? We have used the algorithms to verify some idealized sliding-window protocols with reasonable time and space resources. Our results should be contrasted with the well-known fact that these problems are undecidable for systems with unboundedperfectFIFO channels.     

Discrete cuckoo search algorithm for the travelling salesman problem

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics.