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Merging technique is widely adopted by I/O schedulers to maximize system I/O throughput. However, I/O merging could increase the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged performance variations. Even with better system throughput, higher worst-case latency experienced by some requests could block the SSD storage system, which violates the QoS (Quality of Service) requirement. In order to improve QoS performance while providing higher I/O throughput, this paper proposes a reinforcement learning based I/O merging approach. Through learning the characteristic of various I/O patterns, the proposed approach makes merging decisions adaptively based on different I/O workloads. Evaluation results show that the proposed scheme is capable of reducing the standard deviation of I/O latency by 19.1 percent on average, worst-case latency by 7.3-60.9 percent at the 99.9th percentile compared with the latest I/O merging scheme, while maximizing system throughput.

This study develops an input-output linear programming (IO-LP) model to identify a cost-effective strategy to reduce the economy-wide carbon dioxide (CO2) emissions in China from 2020 to 2050 through a shift in the electricity generation mix. In particular, the fixed capital formation of electricity technologies (FCFE) is endogenized so that the capital-related CO2 emissions of various generation technologies can be captured in the model. The modeling results show that low-carbon electricity, e.g., hydro, nuclear, wind, and solar, is associated with lower operation-related CO2 emissions but higher capital-related CO2 emissions compared to coal-fired electricity. A scenario analysis further reveals that a shift in the electricity generation mix could reduce the accumulated economy-wide CO2 emissions in China by 20% compared to the business-as-usual (BAU) level and could help peak China's CO2 emissions by 2030. The emission reduction is mainly due to a drop in operation-related CO2 emissions of electricity, contributing to a decrease in accumulated economy-wide emissions by 21.4%. The infrastructure expansion of electricity, on the other hand, causes a rise in the accumulated emissions by 1.4%. The proposed model serves as an effective tool to identify the optimal technology choice in the electricity system with the consideration of both direct and indirect CO2 emissions in the economy.

In production theory, industrial units do business in such a way that they use minimum amount of resources to produce maximum amount of products. So, inefficient units decrease their inputs level and increase their outputs level to meet the efficient frontier. By changing inputs and outputs, achieving an optimal scale size (OSS) in industrial units is one of the most important attempts and has attracted considerable attention among researchers. In this paper, an optimal scale size in input–output allocative DEA model is defined to each production firm in which the costs of inputs and the revenues of outputs are considered. We first rearrange the average-revenue efficiency measure that combines scale and output allocative efficiencies. Next, we simultaneously consider both of inputs and outputs in a new average-cost/revenue efficiency measure (ACRE). It has been shown that the proposed ACRE measure is the ratio of the profitability efficiency to ray average productivity. A numerical heuristic procedure is proposed to calculate a relatively good approximation of the new OSS in a convex and continuous technology set. To illustrate the real applicability of the proposed approach, we use a real case on 39 electricity distribution companies.

The established guidance laws in literature are generally designed to capture the target while considering all the states of the interceptor model to be measurable. This paper demonstrates the use of an input output observer for the unmeasured state of the missile-target model with semi-global exponential results. A well-established guidance law called pure proportional navigation law is incorporated in this paper to demonstrate the application of the proposed observer. The missile’s angle of attack is the unmeasured state, which also appears non-linearly in the remaining two kinematic equations of the missile. The occurrence of the unmeasured state in the nonlinear form makes it more challenging to theoretically prove its convergence. The Lyapunov analysis is used to show the convergence of the observed state. Different scenarios consisting of the tail chase and head-on engagements with non-maneuvering and maneuvering targets are considered in the simulations to establish the effectiveness of the proposed guidance strategy.

In recent years, deep learning has achieved great success in the field of image processing. In the single image super-resolution (SISR) task, the convolutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. In this paper, we propose a single image super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling layers compared with the existing SR methods. Specifically, the key element of our model is the Adaptive Residual Block (ARB), which replaces the commonly used constant factor with an adaptive residual factor. The experiments prove the effectiveness of our ADR-SR model, which can not only reconstruct images with better visual effects, but also get better objective performances.

Reduction of an [Formula: see text] nonsymmetric matrix to Hessenberg form which takes [Formula: see text] flops and [Formula: see text] I/Os is a major performance bottleneck in the computing of its eigenvalues. Usually to improve the performance, this Hessenberg reduction is computed in two steps: the first one reduces the matrix to a banded Hessenberg form, and the second one further reduces it to Hessenberg form by incorporating more matrix-matrix operations in the computation. We analyse the two steps of the Hessenberg reduction problem on the external memory model (of Aggarwal and Vitter) for their I/O complexities. We propose and analyse a tile based algorithm for the first step of the reduction and show that it takes [Formula: see text] I/Os. For the reduction of a banded Hessenberg matrix of bandwidth [Formula: see text] to Hessenberg form, we propose an algorithm, which uses tight packing of bulges, and requires only [Formula: see text] I/Os. Combining the results of the two steps of the reduction, we show that the Hessenberg reduction can be performed in [Formula: see text] I/Os, when [Formula: see text] is sufficiently large. To the best of our knowledge, the proposed algorithm is the first I/O optimal algorithm for Hessenberg reduction; the worst case I/O complexity matches the known lower bound of the problem.

Functional test sequences can detect defects that are not detected by scan-based tests, but overall, achieve a lower gate-level fault coverage than scan-based tests. Design-for-testability (DFT) approaches for functional test sequences cause the sequences to deviate from functional operation conditions in arbitrary ways over the entire design. This paper introduces the concept of invisible-DFT as a DFT approach for functional test sequences, where the effects of activating the DFT logic are confined to selected logic blocks. This paper develops an invisible-scan approach. Considering a single logic block, the procedure described in this paper inserts scan shift cycles into a functional test sequence while maintaining the same primary input and output sequences for the logic block. This makes the activation of the DFT logic invisible to other logic blocks. The procedure allows a limited number of primary output vectors to be corrected for this purpose. Experimental results are presented to demonstrate the increase in fault coverage that can be achieved by invisible-scan.

Feedforward neural network prediction is the most commonly used method in time series prediction. In view of the low prediction accuracy of the conventional BPNN model when the time series data contain a certain linear relationship, this paper describes a neural network approach for time series prediction, that is BPNN–DIOC (back-propagation neural network with direct input-to-output connections). Eight different datasets were used to verify the validity of BPNN–DIOC model in time series prediction. In this paper, the BPNN was extended to four variants based on the presence or absence of output layer bias and input-to-output connections firstly, and the prediction accuracy of eight datasets are analyzed by statistic method secondly. Finally, the experimental results demonstrate that the BPNN–DIOC has better prediction accuracy compared to the conventional BPNN while the output layer bias has no significant effect. Therefore, the input-to-output connections can significantly improve the prediction ability of time series.

A method is proposed for reducing noise in learning data based on fuzzy inference methods called α-GEMII (α-level-set and generalized-mean-based inference with the proof of two-sided symmetry of consequences) and α-GEMINAS (α-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points). It is particularly effective for reducing noise in randomly sampled data given by singleton input–output pairs for fuzzy rule optimization. In the proposed method, α-GEMII and α-GEMINAS are performed with singleton input–output rules and facts defined by fuzzy sets (non-singletons). The rules are initially set by directly using the input–output pairs of the learning data. They are arranged with the facts and consequences deduced by α-GEMII and α-GEMINAS. This process reduces noise to some extent and transforms the randomly sampled data into regularly sampled data for iteratively reducing noise at a later stage. The width of the regular sampling interval can be determined with tolerance so as to satisfy application-specific requirements. Then, the singleton input–output rules are updated with consequences obtained in iteratively performing α-GEMINAS for noise reduction. The noise reduction in each iteration is a deterministic process, and thus the proposed method is expected to improve the noise robustness in fuzzy rule optimization, relying less on trial-and-error-based progress. Simulation results demonstrate that noise is properly reduced in each iteration and the deviation in the learning data is suppressed considerably.

The big data on the value-added tax (VAT), which links upstream production and downstream consumption, can serve as a key basis for a structured macroeconomic analysis. This paper discusses the con...

This study presents a novel online, accurate and simple model-based maximum torque per ampere (MTPA) strategy for input–output feedback linearisation control of induction motor (IM) drives. Despite conventional MTPA control principles which are under the assumption of iron loss neglecting, the proposed strategy takes iron loss effect into account. This study highlights the iron loss influence on MTPA scheme. Firstly, the cross-coupling effects in the IM model and torque non-linearity due to the iron loss in torque–speed characteristics of the IM are discussed. The criterion for the MTPA scheme is then introduced and investigated by gradient approach so that when the gradient vectors of the torque and stator current magnitude are parallel, the MTPA strategy is satisfied. Finally, to confirm the validity of the proposed control scheme, experiments are carried out.

Due to the growing importance of sustainable socioeconomic development at a national level, this paper makes an attempt to identify and assess the relationships between agriculture and the environment in European Union countries in light of input–output tables. To meet that objective, data was retrieved from input-output tables and European environmental accounts. The analysis covered material input and output to/from agriculture, pollutant emissions and biomass production and consumption. European Union countries are witnessing a growing share of sphere I agribusiness (mainly including services, the fuel and energy sector and the chemical industry) in material input to agriculture. While this reflects production modernization efforts, it involves a greater environmental burden. EU countries are reporting a decline in direct material input and the domestic consumption of agricultural biomass per euro of GDP. These changes are indicative of less intensive use of environmental resources (dematerialization of the economy), which is consistent with the assumptions of sustainable development. To summarize, it may be concluded that countries at higher levels of agricultural development are meeting the environmental objectives of sustainable development to a greater extent than countries at lower levels of socioeconomic development.

Distributed control offers practical trade-off between globally (plant-wide) valid, computationally expensive centralized and locally effective decentralized architectures. While the development of distributed control theory (algorithms, stability, convergence properties, etc.) has made a significant progress in the last decade, decomposition of a system into an optimal distributed structure (classification of controller variables) has received considerably less attention. This paper focuses on achieving such a decomposition for integrated process systems with the objective of maximizing the strength of input–output impact and thus reducing the strength of inter-controller interactions. A graph-theoretic framework is used to abstract input-state-output dependence of a dynamical system and control-relevant interactions are quantified using weighted digraphs. It is shown that community detection in this weighted digraph is equivalent to distributed structure corresponding to strong input–output impact (quantified through the corresponding gain). Two relevant example systems are considered to illustrate the proposed framework and it is shown that the optimal distributed architectures adhere to the underlying structure of strong and weak inter-variable interactions.

This paper deals with the input–output finite-time stabilization problem for Markovian jump systems (MJSs) with incompletely known transition rates. An observer-based output feedback controller is constructed to study the input–output finite-time stability (IO-FTS) problem. By using the mode-dependent Lyapunov–krasovskii functional method, a sufficient criterion checking the IO-FTS problem is provided. Then, an observer and a corresponding state feedback controller for the individual subsystem are respectively designed to solve the input–output finite-time stabilization problem for the systems. Finally, a numerical example on the mass-spring system model is investigated to bring out the advantages of the control scheme proposed in this paper.

For the small-gain theorem derived by Zames in 1966, the later studies after a few decades elaborated on its derivation through defining system causality, which was not assumed by Zames. In connection with the treatment of causality, however, these studies made some unnecessary assumptions on the subsystems in feedback connection and failed to handle general systems described by an input–output relation rather than mapping (which we call input-intolerant/-output-unsolitary systems). On the other hand, although the treatment by Zames can handle such subsystems, it instead turns out to lead to larger values for the induced norms of subsystems compared with the later treatment. This paper is concerned with developing an extended form of the small-gain theorem through the same induced norms as in the later studies while dealing with general input–output causal subsystems. Since causality of subsystems plays a key role in such development, our research direction strongly motivates us to study how causality should be defined for general input–output systems. Thus, much of the arguments in this paper is devoted to such a study, which provides us with profound and thorough understandings on causality of different restricted classes of general input–output systems. Mutual relationships among adequate causality definitions for different classes are also clarified, which should be important in its own right. After deriving an extended form of the small-gain theorem, an example illustrates the importance of dealing with such general subsystems, as well as usefulness of the extension.

The Leontief Input-Output model is a valuable tool for economics. Many economic research works are devoted to analysis of Input-Output table [1]-[5]. In this paper, we consider a separation of Copper production subsector from Mining of metal ores sector which is one of the 55 economic sectors of Input-Output Table of Mongolia. We examine impact of this subsector on other economic sectors using Input-Output table for 2016 and Physical Measurement Method of Cost Accounting [6]. We compute input coefficients of new extended matrix as well as inverse coefficients.

Beijing is a megalopolis with a serious water shortage that has been further exacerbated by an unreasonable industrial water structure. This article uses an input-output method to calculate the water use coefficients in each industrial sector in Beijing and analyses the water use characteristics of the various industrial sectors. Then, an industry association index that represents the influence and sensitivity of sectors is combined with the water use characteristics to readjust the industrial structure with the objectives of water conservation and sustainable economic development. The results indicate that the agricultural water use coefficient is the highest and that the coefficients are generally higher for sectors in secondary industry than for those in tertiary industry. In addition, all coefficients display a downward trend. The water use multipliers vary widely among sectors. In secondary industry during the study period, the number of high water use sectors remained stable, the number of potentially high water use sectors increased and the number of general water use sectors decreased. A comprehensive analysis of the water use characteristics and industrial structure correlations could provide a reference for the optimal allocation of water resources.

The concept of input-output system is stated in the geometric language of infinite jets. It is proved that finite-dimensional input-output mappings are Backlund transformations. This assertion is generalized to infinite-dimensional input-output systems. Observability conditions are derived as a consequence of the geometric interpretation introduced.

This paper has investigated the input-output finite time stability (IO-FTS) for a class of networked control systems (NCSs) with network-induced delay. To reduce the frequents of packets transmission, a novel memory event-triggered scheme (METS) has been proposed. Different from existing event-triggered schemes, the proposed METS can make use of certain released packets to generate new event. By this way, the event generator can do more precise decision and better control performance can be expected. By using a Lyapunov functional method, sufficient condition for the IO-FTS of NCSs has been derived. Then a co-design method is proposed to obtain the memory feedback gains and parameters of the METS. Finally, a simulation example is carried out and the effectiveness of the designed METS is validated. The IO-FTS of NCSs with solved memory feedback gains is also confirmed.

This paper proposes a novel input-output parametrization of the set of internally stabilizing output-feedback controllers for linear time invariant (LTI) systems. Our underlying idea is to directly treat the closed-loop transfer matrices from disturbances to input and output signals as design parameters and exploit their affine relationships. This input-output perspective is particularly effective when a doubly-coprime factorization is difficult to compute, or an initial stabilizing controller is challenging to find; most previous work requires one of these pre-computation steps. Instead, our approach can bypass such pre-computations, in the sense that a stabilizing controller is computed by directly solving a linear program (LP). Furthermore, we show that the proposed input-output parametrization allows for computing norm-optimal controllers subject to quadratically invariant (QI) constraints using convex programming., IEEE Control Systems Letters, 3 (4), ISSN:2475-1456

In team problems is it better for the principal to reward players based on their individual efforts or should they be rewarded based on collective performance or even a combination of the two? The answer will depend on the importance of two familiar challenges in team works – coordination and free riding. With perfectly complementary efforts, free-riding incentives are absent, so the principal prefers output monitoring over input monitoring but sometimes both may be dominated by mixed-wage incentives. When efforts are perfect substitutes in production, either of the two polar mechanisms may dominate the other but sometimes mixed-wages may dominate both. For more general technologies only output and input monitoring mechanisms are compared. It is shown that when the team production technology is supermodular, coordination becomes the primary concern and monitoring agents through collective output is mostly the better protocol. On the other hand, if the technology is submodular, output monitoring encourages free riding and so input monitoring may be a more attractive alternative.

Sustainable water management is challenging because of the wide range of agents who need water and the different kinds of use, in a context of limited water availability. The availability of water for use, at a given point in time and space, depends on numerous physical and climatic variables, as well as upstream uses and downstream commitments. Therefore, any analysis of water use and management must inevitably be made in the context of such variability. This paper develops an integrated, multiregional, hydro-economic modeling framework to analyze the spatial and temporal dependencies between economic agents in the different regions and areas of a river basin. We combine hydro-economic modeling (partial economic equilibrium) and a multiregional input–output model (general equilibrium) to take advantage of both methodologies. Spatial variability is considered in the input–output models, but variability in both time and space is also considered by the hydro-economic model. Hydro-economic models are used to quantify direct impacts, but not indirect impacts in some specific sectors of the economy, while the input–output model reveals the relationships between all sectors and regions, and facilitates the assessment of total impacts (direct plus indirect) of a range of scenarios. While the methodology described in this paper is applicable to any river basin, the case study considered is the Ebro River Basin, in Spain. To show the potential of the modeling framework, two scenarios are simulated to assess the impacts on water use, value added, and jobs across scales. The results of these scenarios show that decreases in water availability have negative impacts on socio-economic variables (value added and employment). The trade-off between water availability and socio-economic variables depends on the temporal and spatial variability of the resource, and affects each location in the basin in a different way, demonstrating the importance of the methodology developed.

CO2 emissions from China accounted for 27 per cent of global emisions in 2019. More than one third of China's CO2 emissions come from the thermal electricity and heating sector. Unfortunately, this area has received limited academic attention. This research aims to find the key drivers of CO2 emissions in the thermal electricity and heating sector, as well as investigating how energy policies affect those drivers. We use data from 2007 to 2018 to decompose the drivers of CO2 emissions into four types, namely: energy structure; energy intensity; input-output structure; and the demand for electricity and heating. We find that the demand for electricity and heating is the main driver of the increase in CO2 emissions, and energy intensity has a slight effect on increasing carbon emissions. Improving the input-output structure can significantly help to reduce CO2 emissions, but optimising the energy structure only has a limited influence. This study complements the existing literature and finds that the continuous upgrading of power generation technology is less effective at reducing emissions and needs to be accompanied by the market reform of thermal power prices. Second, this study extends the research on CO2 emissions and enriches the application of the IO-SDA method. In terms of policy implications, we suggest that energy policies should be more flexible and adaptive to the varying socio-economic conditions in different cities and provinces in China. Accelerating the market-oriented reforms with regard to electricity pricing is also important if the benefits of technology upgrading and innovation are to be realised.

The construction industry is one of the major contributors that emits carbon into the environment. When considering the carbon emission in the local context, even though there are Input-Output Tables (IOTs) that applies to all types of industries in Sri Lanka, there seems to be limited focuses on IOTs specifically relating to the construction activities which is vital for the need due to its outstanding contribution to the carbon footprint of Sri Lanka. Hence, this study aims to calculate the carbon footprint construction activities in Sri Lanka using IOT with a bottom-up approach. The required data were extracted from published documents of Survey of Construction Industry by Census and Statistics, and The Inventory of Carbon and Energy (ICE) database of UK. Carbon footprint of construction activities were calculated using IOT with a bottom-up approach. The analysis revealed that the road and railway sector have the highest contribution of 48% to the monetary value of the construction industry meanwhile it accounts for 44% of the highest carbon emission to the atmosphere in the year 2020. Subsequently, activities related to non-residential residential, and utilities contribute to 20%, 15%, and 12% of total carbon emission respectively. In terms of construction materials, cement-based activities (59%) were the highest contributor while rubble and metal-based (23%), and iron and steel-based activities (17%) were next level contributors to the carbon emission due to construction. The analysis concluded that the as the initiatives, public sector projects including road and railway sector and utilities should integrate more sustainable construction practices as they are responsible for more than 50% of carbon emission.

ObjectivesElectric stimulation is used to treat a number of neurologic disorders such as epilepsy and depression. However, delivering the required current to far-field neural targets is often ineffective because of current spread through low-impedance pathways. Here, the specific aims are to develop an empirical measure for current passing through the human head and to optimize stimulation strategies for targeting deeper structures, including the auditory nerve, by utilizing the cochlear implant (CI).Materials and methodsOutward input/output (I/O) functions were obtained by CI stimulation and recording scalp potentials in five CI subjects. Conversely, inward I/O functions were obtained by noninvasive transcranial electric stimulation (tES) and recording intracochlear potentials using the onboard recording capability of the CI.ResultsI/O measures indicate substantial current spread, with a maximum of 2.2% gain recorded at the inner ear target during tES (mastoid-to-mastoid electrode configuration). Similarly, CI stimulation produced a maximum of 1.1% gain at the scalp electrode nearest the CI return electrode. Gain varied with electrode montage according to a point source model that accounted for distances between the stimulating and recording electrodes. Within the same electrode montages, current gain patterns varied across subjects suggesting the importance of tissue properties, geometry, and electrode positioning.ConclusionThese results provide a novel objective measure of electric stimulation in the human head, which can help to optimize stimulation parameters that improve neural excitation of deep structures by reducing the influence of current spread.Conflict of interestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Most components of High Performance Computing systems, either hardware or software, come with many tunable parameters and their parametrization can have a significant impact on their performance. For optimal performance, the most adapted parametrization for each application running on the cluster must be determined and used. However, this parametrization is difficult to find because of the complexity of the relationship between each component and the lack of insight on the system’s behavior.In this thesis, we remove the complex task of tuning the system manually or through theoretical models, by exploring auto-tuning methods relying on black-box optimization to find the system’s optimal parameters. We provide a comprehensive comparison of three different black-box optimization heuristics (simulated annealing, genetic algorithms and bayesian optimization) to tune two very different I/O accelerators over several benchmarks.To improve the resilience of these heuristics to the noise caused by the shared nature of HPC systems, we suggest a new algorithm based on resampling methods for stochastic optimization, and show a significative improvement compared to the state-of-the-art. The different described methods are made available in a generic open-source software called SHAMan, entirely developed during this thesis. Additionally, we suggest a possible integration of our work for automatic parametrization of incoming applications, by using a matching pipeline on users’ submission and automatically parametrizing the system's at each new application's submission. Finally, we prove the relevance of black-box optimization to test yet another class of tunable component: the collective operations of Message Passing Interface (MPI).; La plupart des composants des systèmes à Haute Performance, qu'ils soient matériels ou logiciels, sont hautement configurable, et leurs paramètres ont un impact fort sur la performance du système. Pour maximiser la performance, il faut donc trouver les paramètres les plus appropriés pour chacune des applications exécutées sur le système de calcul, mais trouver cette paramétrisation est une tâche compliquée, de par la complexité des interactions entre les éléments du système et la variété des applications que celui-ci exécute.Dans cette thèse, nous suggérons d'utiliser des méthodes d'auto-optimisation dîtes de boîte noire, plutôt que de régler le système manuellement ou à l'aide de modèles théoriques. Nous réalisons ainsi la comparaison de trois heuristiques d'optimisation (recuit simulé, algorithmes génétiques, et optimisation bayésienne) pour auto-paramétriser deux accélérateurs d'Entrées/Sorties (E/S) pour plusieurs applications. Pour améliorer la résilience de ces algorithmes aux interférences mesurées, nous proposons un nouvel algorithme de ré-échantillonage pour l'optimisation stochastique et permettant d'obtenir une amélioration significative par rapport à l'état de l'art. Les différentes méthodes proposées sont intégrées dans un logiciel mis à disposition de la communauté dénommé SHAMan, entièrement développé durant la thèse. Aditionnellement, nous proposons un mécanisme permettant la paramétrisation automatique des applications entrantes à l'aide d'un moteur de reconnaissance de métadonnées utilisant des méthodes de dédoublonnage. Enfin, nous élargissons notre travail à d'autres composants que les accélérateurs E/S en optimisant les opérations collectives du Message Passing Interface (MPI).

In asynchronous quasi delay-insensitive (QDI) circuits, temporal masking is a serious concern because of their event-driven behavior, which makes them prone to environmental effects: Data acceptance windows, e.g. are defined by transitions (token/acknowledgement) alone, without temporal bounds, therefore a glitch occurring anytime throughout such a window cannot be distinguished from an expected, correct transition in a straightforward manner and hence threatens data integrity. Therefore, shortening that window is one proposed way in the literature to enhance temporal masking in QDI designs.We examine a variant of the Weak-Conditioned Half Buffer (WCHB) called Interlocking WCHB (which wisely shortens the transition window) because of its glitch filtering properties and a low cost implementation as compared to other variants. We propose modifications that enhance its dealing with illegal token words specifically when waiting for acknowledgment signal transitions in the so-called bubble limited operation mode. A very strict triple-check input filter with a glitch filter preventing the buffer from capturing an illegal state is used, which also enhances the deadlocking rate of the circuitry.

Proper integration of different inputs targeting the dendritic tree of CA3 pyramidal cells (CA3PCs) is critical for associative learning and recall. Dendritic Ca2+ spikes have been proposed to perform associative computations in other PC types, by detecting conjunctive activation of different afferent input pathways, initiating afterdepolarization (ADP) and triggering burst firing. Implementation of such operations fundamentally depends on the actual biophysical properties of dendritic Ca2+ spikes; yet little is known about these properties in dendrites of CA3PCs. Using dendritic patch-clamp recordings and two-photon Ca2+ imaging in acute slices from male rats we report that, unlike CA1PCs, distal apical trunk dendrites of CA3PCs exhibit distinct forms of dendritic Ca2+ spikes. Besides ADP-type global Ca2+ spikes, a majority of dendrites expresses a novel, fast Ca2+ spike type that is initiated locally without backpropagating action potentials, can recruit additional Na+ currents, and is compartmentalized to the activated dendritic subtree. Occurrence of the different Ca2+ spike types correlates with dendritic structure, indicating morpho-functional heterogeneity among CA3PCs. Importantly, ADPs and dendritically initiated spikes produce opposing somatic output: bursts versus strictly single action potentials, respectively. The uncovered variability of dendritic Ca2+ spikes may underlie heterogeneous input-output transformation and bursting properties of CA3PCs, and might specifically contribute to key associative and non-associative computations performed by the CA3 network.

Despite the extended use of the input-output framework, its acceptance among economists is limited due to lack of microfoundations and imposed omission of economic rent. A new input-output table is built from basic neoclassical profit maximization, which explicitly considers economic rent. The new table holds eight differences with conventional open input-output tables which enhances the framework’s theoretical basis, allows for economic rent, and yields new insights into economic structures.JEL Codes: C67, D57

This article constructs two adaptive control laws to achieve deformation reduction and attitude tracking for a rotary variable-length crane arm with system parameter uncertainties and asymmetric input-output constraints. Two auxiliary systems are given to deal with the input constraints, an asymmetric-logarithm-barrier Lyapunov function is established for achieving the asymmetric output constrains, and five adaptive laws are constructed to handle system parameter uncertainties. Besides, the control design is based on a partial differential equation model, and the S-curve acceleration and deceleration method is used for regulating the arm extension speed. Both the system stability and uniform ultimate boundedness of the controlled crane arm are analyzed. Simulation results validate the effectiveness of our established control laws.

Seed imbibition, absorption of water by a dry seed, is an essential process in which embryo hydration and root establishment occur. In natural environments, this process occurs beneath the soil, making it difficult to observe preliminary growth of seeds. This paper presents a new technique for tracking the imbibition of corn seeds. The proposed system is designed to measure imbibition through seed expansion and wirelessly transmit data, permitting the system to remain beneath the soil with the subject seed. The system consists of low-cost commercial off-the-shelf components and 3D-printed probes. The proposed system is optimized to measure the size of multiple seeds with a single Analog-to-Digital Converter (ADC) pin by utilizing the General-Purpose Input Output (GPIO) pins of the microcontroller, to reconfigure connections to supply voltage or ground. The circuit design of the system shows low power consumption compared to other conventional circuits and utilizes fewer components by taking advantage of the microcontroller GPIOs. Additionally, the proposed circuit design shows less error and insensitivity to the supply voltage variations.

Virtual machine (VM) is an isolated domain that gives off an impression of being an entire PC yet in reality just approaches a bit of the PC assets. Execution of the virtual machine running on a similar PC framework equipment relies upon the exhibition of the host working framework. In this work execution of various visitor VM's on a similar host working framework has been analyzed. Here in this detailed experimentation or comparative analysis, Ubuntu, a Linux based operating system has been utilized for host working framework and for guest working frameworks Ubuntu v14.04, CentOS v6.3 operating systems has been utilized. Execution estimation of virtual operating system has been completed in the equivalent controlled scenarios for both (Ubuntu and Centos) guest operating systems utilizing benchmark applications. In this paper Input Output file operations have been performed on various virtual machine using benchmarking tool (Sysbench) to analyze the performance of various guest (Visitor) VM and compare them.