The impacts of cattle access points on deposited sediment levels in headwater streams in Ireland

Cattle access to streams has been linked globally with degradation of stream water quality, driven largely by bank erosion and resultant instream, fine sediment deposition. The majority of evidence on such effects is however based in arid and semiarid regions of the United States and Australia, with few studies relating to cool temperate climates such as Northwest Europe. In this study, “Quorer” resuspendable sediment samples were taken from riffle geomorphic units upstream (control) and at two points downstream (pressure and recovery) of cattle access points in headwater streams in agricultural catchments in Ireland to assess levels of deposited stream sediment. Samples were taken in April/May (2016) prior to the grazing season and in October (2016) at the end of the grazing season. Sites in good‐high ecological status catchments and less than good ecological status catchments were included in the study. Higher levels of sediment were found downstream of cattle access points in both good‐high status and less than good status catchments; however, the impacts of access points were spatially confined to, in most cases, the area immediately downstream of the point of access. There was a strong correlation between deposited sediment mass and organic matter (OM) mass, with levels of OM increasing linearly with deposited sediment mass. Levels of measured sediment were negatively correlated with riparian habitat health (measured using a qualitative habitat assessment). The results of this study highlight the need for measures to prevent cattle access to headwater streams where access points can be many in order to manage local habitat quality and downstream water quality issues.     

Improved energy storage density and energy efficiency of Samarium modified PMNT electroceramic

We report the improved energy storage density and efficiency after 2.5% of Samarium substitution in ferroelectric Pb[(Mg_1/3Nb_2/3)_0.80Ti_0.20]O₃ (PMNT) electroceramic. The microstructure and surface morphology were analyzed and correlated with various functional properties. The energy storage density, leakage current density, ferroelectric and dielectric properties were investigated thoroughly, indicating that Samarium's substitution significantly modified the microstructure, the dielectric strength, breakdown electric field, and turned ferroelectric PMNT to relaxor ferroelectrics. Due to the relaxor nature, the gap between remanent polarization and maximum polarization increases with the substitution of Samarium in PMNT matrix, which further increases the recoverable energy storage density and energy efficiency. A nearly 100% increase in recoverable energy density and efficiency was obtained at an electric field strength of 35 kV/cm at room temperature (～296 K). The electroceramic shows maximum energy density near the ferroelectric phase transition temperature (325 K–345 K) region and provides a moderate energy storage density for possible applications in power microelectronics.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Apparent power-law distributions in animal movements can arise from intraspecific interactions

Lévy flights have gained prominence for analysis of animal movement. In a Lévy flight, step-lengths are drawn from a heavy-tailed distribution such as a power law (PL), and a large number of empirical demonstrations have been published. Others, however, have suggested that animal movement is ill fit by PL distributions or contend a state-switching process better explains apparent Lévy flight movement patterns. We used a mix of direct behavioural observations and GPS tracking to understand step-length patterns in females of two related butterflies. We initially found movement in one species (Euphydryas editha taylori) was best fit by a bounded PL, evidence of a Lévy flight, while the other (Euphydryas phaeton) was best fit by an exponential distribution. Subsequent analyses introduced additional candidate models and used behavioural observations to sort steps based on intraspecific interactions (interactions were rare in E. phaeton but common in E. e. taylori). These analyses showed a mixed-exponential is favoured over the bounded PL for E. e. taylori and that when step-lengths were sorted into states based on the influence of harassing conspecific males, both states were best fit by simple exponential distributions. The direct behavioural observations allowed us to infer the underlying behavioural mechanism is a state-switching process driven by intraspecific interactions rather than a Lévy flight.     

Extending SSD Lifespan with Comprehensive Non-Volatile Memory-Based Write Buffers

Journal of Computer Science and Technology - New non-volatile memory (NVM) technologies are expected to replace main memory DRAM (dynamic random access memory) in the near future. NAND flash...     

Coexistence of three ferroelectric phases and enhanced piezoelectric properties in BaTiO3–CaHfO3 lead-free ceramics

Perovskite ferroelectrics possess the fascinating piezoelectric properties near a morphotropic phase boundary, attributing to a low energy barrier that the results in structural instability and easy polarization rotation. In this work, a new lead-free system of (1-x)BaTiO₃-xCaHfO₃ was designed, and characterized by a coexistence of ferroelectric rhombohedral-orthorhombic-tetragonal (R-O-T) phases. With the increase amount of CaHfO₃ (x), a stable coexistence region of three ferroelectric phases (R-O-T) exists at 0.06 ≤ x ≤ 0.08. Both large piezoelectric coefficient (d₃₃～400 pC/N), inverse piezoelectric coefficient (d₃₃^*～547 pm/V) and planar electromechanical coupling factor (k_p～58.2%) can be achieved for the composition with x = 0.08 near the coexistence of three ferroelectric phases. Our results show that the materials with the composition located at a region where the three ferroelectric R-O-T phases coexist would have the lowest energy barrier and thus greatly promote the polarization rotation, resulting in a strong piezoelectric response.     

Compiler compatible 5.66 Mb/mm2 8T 1R1W register file in 14 nm FinFET technology

1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm² is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm² array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall V_min of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances.