Stack Architecture Research Articles

Hadoop Oriented Smart Cities Architecture.

A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.

RIE dynamics for extreme wafer thinning applications

Over the past few years, extreme wafer thinning has acquired more interest due to its importance in 3D stacked system architecture. This technique facilitates multi-wafer stacking for via last advanced packaging. From a cost and wafer integrity point of view, it has been demonstrated that the best process flow combines grinding with fast Si removal using Reactive Ion Etching (RIE). For this integration scheme, final thickness, and global flatness are key for subsequent steps. The wafer thinning performances are driven by several steps and can lead to lot, wafer to wafer and within wafer variations especially at the extreme edge. The first part of this study is to demonstrate stable wafer thinning with good control of the remaining Si (up to 5μm) during the RIE process. This uses an innovative in-situ endpoint system (Near Infra-Red reflectometry) where the Si thickness is monitored whilst etching. The second part will focus on adjustment of the etch profile to compensate for incoming non-uniformity. This has been investigated from three different perspectives: Hardware modification where the ceramic ring surrounding the wafer is modified, process modification to change the etch front through changing the gas flow and plasma shape and changing the edge trim to introduce additional loading at the edge.

Büyükten Küçüğe Oto-kodlayıcılar ile dişlerin konumlandırılması*

Localization of teeth is a prerequisite task for most of the computerized methods for dental images such as medical diagnosis and human identification. Classical deep learning architectures like convolutional neural networks and auto-encoders seem to work well for tooth detection, however, it is non-trivial because of the large image size. In this study, a coarse-to-fine stacked auto-encoder architecture is presented for detection of teeth in dental panoramic images. The proposed architecture involves cascaded stacked auto-encoders where sizes of the input patches increase with the successive steps. Only the detected candidate tooth patches are fed into the successive layers, thus the irrelevant patches are eliminated. The proposed architecture decreases the cost of detection process while providing precise localization. The method is tested and validated on a dataset containing 206 dental panoramic images and the results are promising.

Infrared Organic Light-Emitting Diodes with Carbon Nanotube Emitters.

While organic light-emitting diodes (OLEDs) covering all colors of the visible spectrum are widespread, suitable organic emitter materials in the near-infrared (nIR) beyond 800 nm are still lacking. Here, the first OLED based on single-walled carbon nanotubes (SWCNTs) as the emitter is demonstrated. By using a multilayer stacked architecture with matching charge blocking and charge-transport layers, narrow-band electroluminescence at wavelengths between 1000 and 1200 nm is achieved, with spectral features characteristic of excitonic and trionic emission of the employed (6,5) SWCNTs. Here, the OLED performance is investigated in detail and it is found that local conduction hot-spots lead to pronounced trion emission. Analysis of the emissive dipole orientation shows a strong horizontal alignment of the SWCNTs with an average inclination angle of 12.9° with respect to the plane, leading to an exceptionally high outcoupling efficiency of 49%. The SWCNT-based OLEDs represent a highly attractive platform for emission across the entire nIR.

Cognitive Architecture for a Connected World

We propose a unified architecture for next generation cognitive, low cost, mobile internet. The end user platform is able to scale as per the application and network requirements. It takes computing out of the data center and into end user platform. Internet enables open standards, accessible computing and applications programmability on a commodity platform. The architecture is a super-set to present day infrastructure web computing. The Java virtual machine (JVM) derives from the stack architecture. Applications can be developed and deployed on a multitude of host platforms. O(1) -> O(N). Computing and the internet today are more accessible and available to the larger community. Machine learning has made extensive advances with the availability of modern computing. It is used widely in NLP, Computer Vision, Deep learning and AI. A prototype device for mobile could contain N compute and N MB of memory.

Enviroment-Friendly Synthesis of Nanocrystalline Nickel Oxide and Its Antibacterial Properties

This study reports an environment-friendly synthetic strategy to process nickel oxide nanocrystals. A mesoporous nickel oxide nanostructure was synthesized using an environmentally benign biomimetic method. We used a natural rambutan peel waste resource as a raw material to ligate nickel ions to form nickel-ellagate complexes. The direct decomposition of the obtained complexes at 700 oC, 900 oC and 1100 oC in a static air atmosphere resulted in mesoporous nickel oxide nanostructures. The formation of columnar mesoporous NiO with a concentric stacked doughnuts architecture was purely dependent on the suitable direct decomposition temperature at 1100 oC when the synthesis was carried out. The prepared NiO nanocrystals were coated on cotton fabric and their antibacterial activity was also analyzed. The NiO nanoparticle-treated cotton fabric exhibited good antibacterial and wash durability performance.

Deciphering the helicity switching mechanism: a case study of the rigid three-tiered stacked architecture.

Understanding the switching mechanism of helical molecular cages is critical in regulating their functions of asymmetric catalysis and enantioseparation. The helical inversion of a three-tiered stacked architecture was investigated by employing molecular dynamics simulations combined with free-energy calculations. A two-dimensional free-energy landscape characterizing the spinning processes of the top and bottom tiers around the z axis was determined using the extended adaptive biasing force method. The free-energy barrier in the least free-energy pathway was estimated to be 17.6 kcal mol-1, in excellent agreement with experimental measurements. Further analysis revealed that the barrier was caused by geometric deformation, weakening of π-π stacking between aromatic rings, and the re-orientation of polarized amine moieties. The present contribution takes a step toward understanding the dynamic helicity-based functions related to asymmetric reactions and optical resolution.

Resistance Training with Co-ingestion of Anti-inflammatory Drugs Attenuates Mitochondrial Function.

Aim: The current study aimed to examine the effects of resistance exercise with concomitant consumption of high vs. low daily doses of non-steroidal anti-inflammatory drugs (NSAIDs) on mitochondrial oxidative phosphorylation in skeletal muscle. As a secondary aim, we compared the effects of eccentric overload with conventional training.Methods: Twenty participants were randomized to either a group taking high doses (3 × 400 mg/day) of ibuprofen (IBU; 27 ± 5 year; n = 11) or a group ingesting a low dose (1 × 75 mg/day) of acetylsalicylic acid (ASA; 26 ± 4 year; n = 9) during 8 weeks of supervised knee extensor resistance training. Each of the subject's legs were randomized to complete the training program using either a flywheel (FW) device emphasizing eccentric overload, or a traditional weight stack machine (WS). Maximal mitochondrial oxidative phosphorylation (CI+IIP) from permeabilized skeletal muscle bundles was assessed using high-resolution respirometry. Citrate synthase (CS) activity was assessed using spectrophotometric techniques and mitochondrial protein content using western blotting.Results: After training, CI+IIP decreased (P < 0.05) in both IBU (23%) and ASA (29%) with no difference across medical treatments. Although CI+IIP decreased in both legs, the decrease was greater (interaction p = 0.015) in WS (33%, p = 0.001) compared with FW (19%, p = 0.078). CS activity increased (p = 0.027) with resistance training, with no interactions with medical treatment or training modality. Protein expression of ULK1 increased with training in both groups (p < 0.001). The increase in quadriceps muscle volume was not correlated with changes in CI+IIP (R = 0.16).Conclusion: These results suggest that 8 weeks of resistance training with co-ingestion of anti-inflammatory drugs reduces mitochondrial function but increases mitochondrial content. The observed changes were not affected by higher doses of NSAIDs consumption, suggesting that the resistance training intervention was the prime mediator of the decreased mitochondrial phosphorylation. Finally, we noted that flywheel resistance training, emphasizing eccentric overload, rescued some of the reduction in mitochondrial function seen with conventional resistance training.

Interconnects on elastomers: optimizing for stretchability, speed and layout area

The design of interconnects on elastomers is of interest in flexible electronics. The geometric design of interconnects must take into account the electrical and mechanical aspects, along with layout area. This work discusses the impact of the geometry of stretchable interconnects on various user specifications, namely layout area, stretchability given by mechanical stiffness coefficient and speed given by electrical impedance. Analytical models have been derived and an optimization methodology is developed to help pick the best design for the given application. The analytical models are corroborated with simulations and experiments. Interconnect designs have been fabricated on a copper-viscoelastic adhesive-elastomer stack architecture, using laser toner-transfer process where the viscoelastic adhesive prevents delamination and in which the process flow is in line with conventional printed circuit board fabrication techniques.

Nappe stack of Piemonte–Ligurian units south of Aosta Valley: New evidence from Urtier Valley (Western Alps)

New results of structural and lithostratigraphic investigations performed in the Urtier Valley, located south of Aosta Valley at the northern flank of the Gran Paradiso Massif, shed new light on the nappe stack architecture of the Western Alps. Two distinct Piemonte–Ligurian units were recognized in the area: (a) the blueschist facies Broillot unit, with a well‐preserved lithostratigraphic succession that includes ophiolites and their metasedimentary cover and (2) the eclogitic Bardonney unit with clasts and/or slices of ophiolites embedded in a carbonate/quarzitic and/or metabasic matrix. Three major deformation stages have been recognized (D1, D2, and D3). D1 phase occurred during the peak metamorphic conditions (i.e., eclogite or blueschist facies). D2 and D3 phases are related to multistage exhumation processes, from early stacking to late refolding of former nappe contacts. Despite the intensity of deformation and metamorphism, the original lithostratigraphic succession of the Broillot unit allowed us to use a lithostratigraphic approach to reconstruct the structures in the study area. For the first time, differences in the nappe stack exposed south and north of the Aosta Valley are reported. In fact, contrary to what is observed in the north, the eclogitic units exposed in the Urtier Valley lay above and not below the blueschist units. A new model for the tectonic evolution, which ranges from stages of subduction to large‐scale nappe refolding, is proposed. It is able to explain the difference in the architecture of the north and south of the Aosta Valley.

High-Performance Single-Active-Layer Memristor Based on an Ultrananocrystalline Oxygen-Deficient TiOx Film.

The theoretical and practical realization of memristive devices has been hailed as the next step for nonvolatile memories, low-power remote sensing, and adaptive intelligent prototypes for neuromorphic and biological systems. However, the active materials of currently available memristors need to undergo an often destructive high-bias electroforming process in order to activate resistive switching. This limits their device performance in switching speed, endurance/retention, and power consumption upon high-density integration, due to excessive Joule heating. By employing a nanocrystalline oxygen-deficient TiOx switching matrix to localize the electric field at discrete locations, it is possible to resolve the Joule heating problem by reducing the need for electroforming at high bias. With a Pt/TiOx/Pt stacking architecture, our device follows an electric field driven, vacancy-modulated interface-type switching that is sensitive to the junction size. By scaling down the junction size, the SET voltage and output current can be reduced, and a SET voltage as low as +0.59 V can be obtained for a 5 × 5 μm2 junction size. Along with its potentially fast switching (over 105 cycles with a 100 μs voltage pulse) and high retention (over 105 s) performance, memristors based on these disordered oxygen-deficient TiOx films promise viable building blocks for next-generation nonvolatile memories and other logic circuit systems.

A Proof of Turing Completeness in Bitcoin Script

The concept of a Turing machine has been well defined. It would be sufficient to show that Bitcoin uses a dual stack architecture that acts as a dual counter machine. Such systems have already been demonstrated as being Turing complete. We demonstrate that Bitcoin script is a minimal family of which λ and R are members. Further using the compositional product rule and the iteration rule we demonstrate that Bitcoin scripting is Turing complete with the limitations imposed on any realworld computer. This limitation is that there cannot be an infinite tape. Iterations can be simulated using an “unrolled” loop function with allocation to the “Alt” stack. As the product rule states that if A, B are machines, then A.B is also a machine. The iteration rule shows that if A is a machine then (A) is also a machine. Further the minimum power of A under which the observed square of the final configuration is blank. The consequence of these rules is that for every partial recursive function of in variables we can show that it can be evaluated by machine of the proposed family.

QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network

Wireless sensor nodes consist of a collection of sensor nodes with constrained resources in terms of processing power and battery energy. Wireless sensors networks are used increasingly in many industrial and consumer applications. Sensors detect events and send via multi hop routing to the sink node for processing the event. The routing path is established through proactive or reactive routing protocols. To improve the performance of the Wireless Sensor Networks, multi stack architecture is addressed. But the multi stack architecture has many problems with respect to life time, routing loop and QOS. In this work we propose a solution to address all these three problems of life time, routing loop and QOS in case of multi stack architecture.

A minimal dose approach to resistance training for the older adult; the prophylactic for aging

A plethora of research has supported the numerous health benefits of resistance training as we age, including positive relationships between muscular strength, muscle mass and reduced all-cause mortality. As such, resistance training has been referred to as medicine. However, participation and adherence remains low, with time constraints and perceived difficulty often cited as barriers to resistance training. With this in mind, we aimed to summarise the benefits which might be obtained as a product of a minimal dose approach. In this sense, participation in resistance training might serve as a prophylactic to delay or prevent the onset of biological aging. A short review of studies reporting considerable health benefits resulting from low volume resistance training participation is presented, specifically considering the training time, frequency, intensity of effort, and exercises performed. Research supports the considerable physiological and psychological health benefits from resistance training and suggests that these can be obtained using a minimal dose approach (e.g. ≤60min, 2d-wk−1), using uncomplicated equipment/methods (e.g. weight stack machines). Our hope is that discussion of these specific recommendations, and provision of an example minimal dose workout, will promote resistance training participation by persons who might otherwise have not engaged. We also encourage medical professionals to use this information to prescribe resistance exercise like a drug whilst having an awareness of the health benefits and uncomplicated methods.

A DWM-Based Stack Architecture Implementation for Energy Harvesting Systems

Energy harvesting systems tend to use non-volatile processors to conduct computation under intermittent power supplies. While previous implementations of non-volatile processors are based on register architectures, stack architecture, known for its simplicity and small footprint, seems to be a better fit for energy harvesting systems. In this work, Domain Wall Memory (DWM) is used to implement ZPU, the world’s smallest working CPU. Not only does DWM offer ultra-high density and SRAM-comparable access latency, but the sequential access structure of DWM also makes it well suited for a stack whose accesses display high temporal locality. As the performance and energy of DWM are determined by the number of shift operations performed to access the stack, this paper further reduces shift operations through novel data placement and micro-code transformation optimizations. The impact of compiler optimization techniques on the number of shift operations is also investigated so as to select the most effective optimizations for DWM-based stack machine. Experimental studies confirm the effectiveness of the proposed DWM-based stack architectures in improving the performance and energy-efficiency of energy harvesting systems.

Self‐Assembly Atomic Stacking Transport Layer of 2D Layered Titania for Perovskite Solar Cells with Extended UV Stability

AbstractA novel atomic stacking transporting layer (ASTL) based on 2D atomic sheets of titania (Ti1−δO2) is demonstrated in organic–inorganic lead halide perovskite solar cells. The atomically thin ASTL of 2D titania, which is fabricated using a solution‐processed self‐assembly atomic layer‐by‐layer deposition technique, exhibits the unique features of high UV transparency and negligible (or very low) oxygen vacancies, making it a promising electron transporting material in the development of stable and high‐performance perovskite solar cells. In particular, the solution‐processable atomically thin ASTL of 2D titania atomic sheets shows superior inhibition of UV degradation of perovskite solar cell devices, compared to the conventional high‐temperature sintered TiO2 counterpart, which usually causes the notorious instability of devices under UV irradiation. The discovery opens up a new dimension to utilize the 2D layered materials with a great variety of homostructrual or heterostructural atomic stacking architectures to be integrated with the fabrication of large‐area photovoltaic or optoelectronic devices based on the solution processes.

Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion

This study proposes a fuzzy-based FMEA (failure mode and effect analysis) for a hybrid molten carbonate fuel cell and gas turbine system for liquefied hydrogen tankers. An FMEA-based regulatory framework is adopted to analyze the non-conventional propulsion system and to understand the risk picture of the system. Since the participants of the FMEA rely on their subjective and qualitative experiences, the conventional FMEA used for identifying failures that affect system performance inevitably involves inherent uncertainties. A fuzzy-based FMEA is introduced to express such uncertainties appropriately and to provide flexible access to a risk picture for a new system using fuzzy modeling. The hybrid system has 35 components and has 70 potential failure modes, respectively. Significant failure modes occur in the fuel cell stack and rotary machine. The fuzzy risk priority number is used to validate the crisp risk priority number in the FMEA.

NEURAL NETWORK BASED DEEP LEARNING AND ENSEMBLE TECHNIQUES FOR DATA CLASSIFICATION

n this paper, four neural network architectures are proposed for data classification. The four neural networks are constructed based on deep learning and ensemble architectures. Supervised and unsupervised learning paradigms are adopted. Stack of Supervised Neural Network (SSNN), Stack of Unsupervised Neural Network (SUNN), Ensemble of Supervised Neural Network (ESNN), and Ensemble of Unsupervised Neural Network (ESNN) are the proposed neural network classifiers. Australian credit approval data set from the University of California, Irvin is used to evaluate the classifiers. Supervised neural networks have produced more accurate classification results than unsupervised networks. Stack architectures are comparatively better than ensemble architectures. It is found from this research that the combination of supervised learning method and stack architecture leads to better performance.

Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy

The stacked extreme learning machine (S-ELM) is an advanced framework of deep learning. It passes the ‘reduced’ outputs of the previous layer to the current layer, instead of directly propagating the previous outputs to the next layer in traditional deep learning. The S-ELM could address some large and complex data problems with a high accuracy and a relatively low requirement for memory. However, there is still room for improvement of the time complexity as well as robustness while using S-ELM. In this article, we propose an enhanced S-ELM by replacing the original principle component analysis (PCA) technique used in this algorithm with the correntropy-optimized temporal PCA (CTPCA), which is robust for outliers rejection and significantly improves the training speed. Then, the CTPCA-based S-ELM performs better than S-ELM in both accuracy and learning speed, when dealing with dataset disturbed by outliers. Furthermore, after integrating the extreme learning machine (ELM) sparse autoencoder (AE) method into the CTPCA-based S-ELM, the learning accuracy is further improved while spending a little more training time. Meanwhile, the sparser and more compact feature information are available by using the ELM sparse AE with more computational efforts. The simulation results on some benchmark datasets verify the effectiveness of our proposed methods.

A Novel Method for Assessing Muscle Power During the Standing Cable Wood Chop Exercise.

Zemková, E, Cepková, A, Uvaček, M, and Šooš, L. A novel method for assessing muscle power during the standing cable wood chop exercise. J Strength Cond Res 31(8): 2246-2254, 2017-The study estimates the repeatability and sensitivity of a novel method for assessing (a) the maximal power during the standing cable wood chop exercise with different weights and (b) the endurance of the core muscles. A group of 23 fit men performed (a) maximal effort single repetitions of the standing cable wood chop exercise with weights increasing stepwise up to 1 repetition maximum (1RM) and (b) a set of 20 repetitions at a previously established weight at which maximal power was achieved. Results showed that mean power during the standing cable wood chop exercise is a reliable parameter, with intraclass correlation coefficient values above 0.90 for all weights tested. It was also shown to be a sensitive parameter able to discriminate within-group differences in the maximal power and endurance of core muscles. Substantial individual differences were found in mean power, especially at higher weights, and in the maximal power achieved at about 75% of 1RM (462.2 ± 57.4 W, n = 11), 67% of 1RM (327.2 ± 49.7 W, n = 7), and 83% of 1RM (524.0 ± 63.2 W, n = 5). At these weights, there were also significant differences between the initial and the final repetitions of the wood chop exercise (13.9%, p = 0.025; 10.2%, p = 0.036; and 13.8%, p = 0.028, respectively). These findings indicate that evaluation of the maximal power and endurance of the core muscles during the standing cable wood chop exercise on a weight stack machine is a reliable method and sensitive to differences among physically active individuals.