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The amygdala, one of the most studied brain structures, integrates brain-wide heterogeneous inputs and governs multidimensional outputs to control diverse behaviors central to survival, yet how amygdalar input-output neuronal circuits are organized remains unclear. Using a simplified cell-type- and projection-specific retrograde transsynaptic tracing technique, we scrutinized brain-wide afferent inputs of four major output neuronal groups in the amygdalar basolateral complex (BLA) that project to the bed nucleus of the stria terminals (BNST), ventral hippocampus (vHPC), medial prefrontal cortex (mPFC) and nucleus accumbens (NAc), respectively. Brain-wide input-output quantitative analysis unveils that BLA efferent neurons receive a diverse array of afferents with varied input weights and predominant contextual representation. Notably, the afferents received by BNST-, vHPC-, mPFC- and NAc-projecting BLA neurons exhibit virtually identical origins and input weights. These results indicate that the organization of amygdalar BLA input-output neuronal circuits follows the input-dependent and output-independent principles, ideal for integrating brain-wide diverse afferent stimuli to control parallel efferent actions. The data provide the objective basis for improving the virtual reality exposure therapy for anxiety disorders and validate the simplified cell-type- and projection-specific retrograde transsynaptic tracing method.

The methodology for calculating the inter-industry map of uncertainty of the Ukrainian economy and the results of the calculations are considered. A historical assessment of a fragment of fiscal risks associated with tax revenue from taxes on products and other production taxes is carried out. The sectors of Ukraine’s economy are classified through the prism of the uncertainty map. It is proposed to use the production portfolio of economic activities as a tool to mitigate fiscal risks.

A novel mathematical approach has been proposed for input–output range extension of Pseudo-Taylor-Exponential-Approximation (PTEA) for achieving over seven-decade exponential function, while reducing the linearity error by 1 dB and attaining simple coefficients such as to realize this technique in designing the MOSFET based circuit. This new MPTEA technique, has been successfully utilized for its current-mode (I-I) CMOS implementation involving translinear principle of MOSFETs in weak inversion (WI) region. The CMOS synthesis procedure has been detailed and relevant second-order effects consists of noise, bulk-effect etc. have been explored. For post-layout simulations of the synthesized CMOS implementation, the SPECTRE simulation tool from Cadence, with 180 nM CMOS parameters, has been utilized to justify its practical workability. The proposition, in its CMOS layout, occupies an area of 0.0126mm2 only. It consumes only ≈4.92 μW static power by utilizing ± 0.65 V power supply and produces 146.5 dB range of linear-in-dB output with ± 1 dB linearity error.

Natural disasters cause damage to regional economies. This study investigated how production activities in 35 municipalities of Shizuoka Prefecture recover when such activities stop due to a nuclear power plant disaster and tsunami that may be caused by a Nankai Megathrust Earthquake (NME). We developed a dynamic inter-regional input–output model (IRIO) featuring a production bottleneck due to a shortage of inputs and other goods. Using a simulation model, the economic damage caused by single and combined disasters was evaluated based on several recovery scenarios. The effect of depopulation in Japan on post-disaster recovery processes was also examined. This analysis provides useful disaster preparedness information for local governments and companies.

This paper is concerned with the problem of input-output finite-time stability (IO-FTS) for a class of switched singular continuous-time systems. First, the concept of IO-FTS is extended to switched singular systems. Then, by using the average dwell time switching approach and constructing multiple Lyapunov functions, a sufficient condition of IO-FTS for the considered system is presented in the form of linear matrix inequalities (LMIs). Furthermore, based on the derived conditions, a suitable state feedback controller is designed to stabilize the switched singular systems. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed technique.

The series capacitor converter is one kind of high-gain bidirectional DC–DC converter based on interleaved and switched capacitor techniques. For the n-phase series capacitor converter, n times voltage gain when compared with the traditional boost converter under a specific duty cycle, as well as automatic current sharing among phases can be achieved. The specific duty cycle is (n − 1)/n to 1 in the boost mode and 0 to 1/n in the buck mode, which means that it is not suitable for wide input and/or wide output applications. To solve this issue, an 180° interleaved switching strategy is proposed in this paper. The proposed strategy is more suitable for converters to widen the working duty cycle range. The working principle, voltage gain and current distribution/sharing characteristics with the duty cycle ranging from 0 to 1 are analyzed and calculated in greater detail. In addition, as one of the n-phase series capacitor converters, a small signal model of a three-phase series capacitor converter is built, and a dual closed-loop controller is studied and designed. Finally, an experimental prototype with an input voltage that varies from 10 to 30 V and an output voltage of 100 V is built and tested. Results obtained with the prototype verify the correctness of the proposed switching strategy.

SS 201 had been reported as a good substitute for SS304 without any significant compromise in performance. However, modeling EBW process using an efficient tool like adaptive neuro-fuzzy inference system (ANFIS) and use of multi-objective optimization to optimize its performance are not reported yet. Thus, the present study employed ANFIS models tuned by genetic algorithm, particle swarm optimization, gray wolf optimizer, and bonobo optimizer (BO) to predict weld attributes during EBW of SS201 as a function of input process parameters. Among the developed models, ANFIS tuned by BO was seen to yield the best prediction accuracy. In multi-objective optimization (MOO), the two conflicting goals were to minimize secondary dendritic arm spacing and maximize Vicker’s hardness number simultaneously. In MOO, some interesting facts were observed, such as the fixed input parameter of power (P) as 3200-W and squeezed experimental range for the welding speed (S).

Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. One of the standard approaches to assessing this problem is via input–output equations and, in particular, characteristic sets of differential ideals. The precise relation between identifiability and input–output identifiability is subtle. The goal of this note is to clarify this relation. The main results are: We expect that some of these facts may be known to the experts in the area, but we are not aware of any articles in which these facts are stated precisely and rigorously proved.

This study traces the intersectoral linkages or the dependence of industries on one another in Vietnam’s economy within the period of 2000–2012 basing on the input–output analysis. The total linkages—computed using Leontief inverse—are generally employed amongst policymakers as an essential reference in choosing the critical industry. However, for many countries that heavily dependent on imported inputs like Vietnam, total linkages can give an erroneous result. The paper shows how important are the domestic linkages, which is the inverse net of imports, in analyzing the importance of industries in the economy. Constructing the non-competitive input–output tables relying on the assumption that imports are distributed across industries in the same proportion as the gross domestic output of the corresponding industry, the paper finds that there is a considerable divergence between total and domestic linkages. The results imply that import plays a significant role in the intersectoral linkages in the Vietnamese economy. The strength of linkages of some sectors is due to the import utilization effects, but not domestic sectors’ real own ability to create linkages.

In the demand-driven open input–output model, output is determined by final demand, given the production technology in every industry. On the contrary, in the supply-sided version, value added determines the level of output and producers must induce sales in order to achieve a desired level of income. This latter version of the model has been criticised and even rejected on its implausibility, its difficult interpretation and its bizarre implications, among other aspects. This paper argues that the supply-side model is not logically, mathematically or otherwise at odds with Leontief’s arguments. Rejection of the model is a matter of theoretical reading.

The main scope of the paper is to review the construction method of (multi-)regional input–output tables (RIOTs) in the Czech Republic at the NUTS 3 regional level. We compare the estimation of regional IO tables based on a modified cross-hauling adjusted regionalisation method (CHARM) applied by the authors with application of the hybrid approach of (Sixta and Vltavska in Stat Stat Econ J 97:4–14, 2017) from the Department of Statistics at the University of Economics in Prague. The aim of the presented paper is to contribute to the discussion about the suitability of alternative methodological approaches from the perspective of quality and time. We assume that despite the lack of official, published RIOTs by state statistical offices, the existing methodology of the relatively simpler non-survey CHARM method represents an adequate option for the generation of regional input–output tables suitable for further analytical research.

This paper deals with a state model identification of a gas turbine used for gas transport, using a subspace approach of the state space model. This method provides a reliable and robust state representation of the model, taking advantage of its benefits in the control, monitoring, and supervision of this machine. The model for each variable is set so that the state matrices associated with the gas turbine model are determined from their real input/output data. The comparison of the obtained identification results with those of the actual turbine operation serves to validate the proposed model in this work. This numerical algorithm of the subspace identification method is full of information and more accurate in terms of residual modeling error, and expresses a very high level of confidence in the identified turbine system dynamics. Hence, the controllability and observability tests of turbine operation for different input/output variables allowed to validate the real-time operating stability of the turbine.

This study is concerned with the problem of finite-time fault-tolerant control for uncertain semi-Markovian jump systems with input constraint and multiple time delays. There exist nonlinear functions, exogenous disturbances and measurement noise in these nonlinear systems. First, a dynamic output feedback controller is constructed for the uncertain semi-Markovian jump systems. A closed-loop system is earned. Next, sufficient stability conditions of the closed-loop system are obtained via stochastic Lyapunov function. Not only finite-time boundedness but also input-output finite-time mean square stabilization is achieved. This study is the first time to study. Finally, an example is provided to prove the novelty of the approach proposed in this paper.

Input-output (Leontief) production function is widely used in economic analysis. And diminishing marginal rate of return is a very well accepted economic fact. Leontief production function normally results in a linear production possibility frontier (PPF) due to its linear feature, whereas diminishing marginal rate of return implies a non-linear PPF. In this paper, the authors aim to fix this problem by considering multiple primary inputs in a simplified two-sector economy. The authors find that it is possible to curve a non-linear PPF by using Leontief production function when the authors add heterogeneous primary inputs. The authors also discuss the PPF using non-linear production function. Furthermore, the authors propose that three commonly used economic presumptions cannot hold in the same framework. These presumptions are “single primary input”, “fixed-proportion inputs” and “law of diminishing marginal returns”.

In Korea, weather forecasts for fundamental weather factors, such as temperature, precipitation, wind direction and speed, humidity, and cloudiness, are provided for a three-day period in each region. This can facilitate predicting photovoltaic power generation based on weather forecasting. For this purpose, in the present paper, we aim to propose corresponding model. However, the Korea Meteorological Administration does not forecast the amount of solar radiation and sunshine that mostly influence the results of photovoltaic power generation prediction. In this study, we predict these parameters considering various input/output (I/O) variables and learning algorithms applied to weather forecasts on hourly weather data. Finally, we predict photovoltaic power generation based on the best sunshine and solar radiation prediction results. The data structure underlying all predictions relies on four models applied to fundamental weather factors on sunshine and solar radiation data two hours ago. Then, the photovoltaic power generation prediction is implemented using four models depending on whether to add the predicted sunshine and solar radiation data obtained at the previous step. The prediction algorithm relies on an adaptive neuro-fuzzy inference system and artificial neural network (ANN) techniques, including dynamic neural network (DNN), recurrent neural network (RNN), and long short-term memory (LSTM). The results of the conducted experiment indicate that ANN perform better than the neuro-fuzzy approach. Moreover, we demonstrate that RNN and LSTM are more suitable for the time series data structures compared with DNN. Furthermore, we report that the weather forecast structure and the model 4 structure, which includes sunshine and solar radiation data two hours ago, achieve the best prediction results.

High-performance storage devices, such as Non-Volatile Memory express Solid-State Drives (NVMe SSDs), have been widely adopted in data centers. Especially, multiple storage devices provide higher I/O performance compared with a single device. However, the performance can be reduced in the case of workloads with mixed read and write requests (e.g., key-value stores) even though multiple storage devices are adopted. This is because read requests can be blocked until the processing for write requests is finished. In this article, we propose an I/O isolation scheme to improve the performance of the key-value store for multiple SSDs. In our scheme, we classify files of the key-value store and deploy files to the separated storage devices according to the characteristics of each file. Thus, read/write operations are performed in different storage devices. In addition, we propose two different device mapping methods, namely fixed and adaptive device mapping to deploy files to the proper device. We implement our scheme in RocksDB with multiple storage devices (six NVMe SSDs) and extend our scheme on an open-channel SSD, which reveals internal hardware architecture to verify the effectiveness of read/write isolation within a single storage device. The experimental results demonstrate that our scheme improves performance by up to 29% and 26% in the open-channel SSD and multiple storage devices, respectively, compared with the existing scheme.

This paper studies the input-output analysis of the productivity and cost of Ukraine’s industries. A classification in the space “productivity–cost” of the industries is proposed. The paper reflects computation results on real data. Descriptive statistics of productivity and cost indices are presented. Selected cross-countries and intertemporal comparison is discussed.

Some notes are presented to this paper “Input–output finite-time stability of fractional-order positive switched systems” by Liang et al. (Circuit Syst Signal Process 38:1619–1638, 2019). A counterexample is taken to verify our analysis.

This article presents some novel results on sampled-data H∞ control for a class of linear systems. The proposed system is affected by time-varying delay and external disturbance. The main goal of this paper is to transform the original system into an equivalent two interconnected subsystems through the combination of input-output approach and scaled small gain (SSG) theorem. Then, the three term approximation method is adopted to approximate the time-varying delay. By incorporating Lyapunov-Krasovskii functional and wirtinger integral inequality, a new set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which guarantee the stability of the closed loop system, and a H∞ norm bound performances. Finally, the applicability of the developed control design technique and its less conservativeness over other existing ones are proven by means of simulation examples.

This paper considers asynchronously input-output finite-time control of positive impulsive switched systems(PISS). Firstly, the definition of input-output finite-time stability(IO-FTS) is introduced. By using the Lyapunov functional and average dwell time(ADT) approach, a state feedback controller is designed and new sufficient conditions are obtained to guarantee the corresponding closed-loop system is IO-FTS under asynchronous switching. Such conditions can be solved by linear programming. Finally, a practical example is provided to show the effectiveness of the proposed method.

It is hard for applications to make full utilization of the peak bandwidth of the storage system in highperformance computers because of I/O interferences, storage resource misallocations and complex long I/O paths. We performed several studies to bridge this gap in the Sunway storage system, which serves the supercomputer Sunway TaihuLight. To locate these issues and connections between them, an end-to-end performance monitoring and diagnosis tool was developed to understand I/O behaviors of applications and the system. With the help of the tool, we were about to find out the root causes of such performance barriers at the I/O forwarding layer and the parallel file system layer. An application-aware I/O forwarding allocation framework was used to address the I/O interferences and resource misallocations at the I/O forwarding layer. A performance-aware data placement mechanism was proposed to mitigate the impact of I/O interferences and performance variations of storage devices in the PFS. Together, applications obtained much better I/O performance. During the process, we also proposed a lightweight storage stack to shorten the I/O path of applications with -N I/O pattern. This paper summarizes these studies and presents the lessons learned from the process.

Modern High-Performance Computing (HPC) systems are adding extra layers to the memory and storage hierarchy, named deep memory and storage hierarchy (DMSH), to increase I/O performance. New hardware technologies, such as NVMe and SSD, have been introduced in burst buffer installations to reduce the pressure for external storage and boost the burstiness of modern I/O systems. The DMSH has demonstrated its strength and potential in practice. However, each layer of DMSH is an independent heterogeneous system and data movement among more layers is significantly more complex even without considering heterogeneity. How to efficiently utilize the DMSH is a subject of research facing the HPC community. Further, accessing data with a high-throughput and low-latency is more imperative than ever. Data prefetching is a well-known technique for hiding read latency by requesting data before it is needed to move it from a high-latency medium (e.g., disk) to a low-latency one (e.g., main memory). However, existing solutions do not consider the new deep memory and storage hierarchy and also suffer from under-utilization of prefetching resources and unnecessary evictions. Additionally, existing approaches implement a client-pull model where understanding the application’s I/O behavior drives prefetching decisions. Moving towards exascale, where machines run multiple applications concurrently by accessing files in a workflow, a more data-centric approach resolves challenges such as cache pollution and redundancy. In this paper, we present the design and implementation of Hermes: a new, heterogeneous-aware, multi-tiered, dynamic, and distributed I/O buffering system. Hermes enables, manages, supervises, and, in some sense, extends I/O buffering to fully integrate into the DMSH. We introduce three novel data placement policies to efficiently utilize all layers and we present three novel techniques to perform memory, metadata, and communication management in hierarchical buffering systems. Additionally, we demonstrate the benefits of a truly hierarchical data prefetcher that adopts a server-push approach to data prefetching. Our evaluation shows that, in addition to automatic data movement through the hierarchy, Hermes can significantly accelerate I/O and outperforms by more than 2x state-of-the-art buffering platforms. Lastly, results show 10%–35% performance gains over existing prefetchers and over 50% when compared to systems with no prefetching.

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.

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.

This paper shows an application of plausible reasoning methods (mainly, specialization and analogous) in mathematical modeling. Our attention is how a practitioner to determine analogously a more balanced scientific model to assist the desire during solving the entire problem. Taking interval samples modeling as a problem, we exemplify (with consideration paid to the motivation and course of discovering) how to discover, based on the classical corresponding methods, three linear regression models and two linear-like interpolation models relying on n-variable interval input-1-variable interval output samples. The rationality of these recommended models are proved, and applications of them are illuminated in detail by examples. Strategies to model further interval samples towards satisfactory are also exposed.

Extending the technique of unit structure analysis, which was originally developed by Ozaki (J Econ 73(5):720–748, 1980), this study introduces a method of value chain mapping that uses international input–output data and reveals both the upstream and downstream transactions of goods and services, as well as primary input (value added) and final output (final demand) transactions, which emerge along the entire value chain. This method is then applied to the agricultural value chain of three Greater Mekong Subregion countries: Thailand, Vietnam, and Cambodia. The results show that the agricultural value chain has been increasingly internationalized, although there is still room to benefit from participating in global value chains, especially in a country such as Cambodia. Although there are some constraints regarding the methodology and data, the method proves useful in tracing the entire value chain.

In this work, an adaptive controller is designed by an adaptive network called fuzzy rules emulated network (FREN) for a class of nonlinear discrete-time systems when the mathematical model is completely neglected. The characteristic curve between the plant’s input and output is drawn by results of the experimental setup. The design parameters are determined by the characteristic curve and utilized to design the controller. The network structure of FREN controller is established by the set of human knowledge-based IF-THEN rules according to the relation between the plant’s input and output. The theoretical result is provided to guarantee the convergence of tracking error of the learning algorithm. Furthermore, experimental systems with the LED current control and the robotic system are constructed to validate the performance of the proposed controller.

On the grounds of the long discussion in the literature for the construction of regional input–output (I/O) tables, the present study endeavors to identify the best performing method among the most applied location quotient based non-survey techniques. The analysis uses actual data—in particular, the EU input–output tables for the years 2010 and 2014—and by employing three different statistics, it compares the tables created by the quotients FLQ, AFLQ, SLQ, CILQ and RLQ. Comparison is made in both the technical coefficient tables and the Leontief ones. The results indicate that the AFLQ and FLQ provide better results for values of δ from 0.1 to 0.3, whereas for values of δ from 0.4 to 0.9, the results of the two quotients are not satisfactory. At the same time, using another statistic, the SLQ–CILQ technique seemed to yield satisfactory results. Similar results are also presented for the Leontief tables. Finally, using a linear regression model, the imports do not appear to be related to the value of δ in the FLQ.

A load matching system which utilizes a time-sharing controller for an standalone solar PV system employing a Dual Input/Output Biphase Buck dc–dc Converter is presented here. The load matching system consists of an integrated load voltage and MPPT controller to regulate power flow between a standalone dc load, solar PV and a battery. The Dual Input/Output Biphase Buck dc–dc Converter incorporates the advantages of both multiport converters used for standalone systems and the multiphase converters used for low voltage and high-current applications. The steady-state analysis of the proposed system is performed by deriving the appropriate analytical equations and computing using the MATLAB programming language. A dynamic model of the Dual Input/Output Biphase Buck dc–dc Converter is developed and has been used for simulating the dynamic performance of the proposed load matching system. The efficacy of the proposed load matching scheme was also validated through experimentation under various operating conditions.

This work deals with the prediction and control problems associated with a class of linear systems with time-delay at the input-output path. A general prediction observer scheme that estimates the future value of the delayed system from the output is proposed. Later, a full-information predictor-observer is introduced and the convergence of the estimated future values is formally proven for a time-delay τ of any size by increasing the dimension of the proposed predictor-observer. The estimated future state is used to design a feedback law that compensates the delay effects on the original system. The performance of the prediction-observation control strategy is shown by means of numerical simulations. Thus, it is illustrated that the performance of the proposed full-information predictor-observer is improved with respect to the sequential sub-predictors previously presented in the literature.

Supercomputers’ capability is approaching the exascale level, which enables large computing systems to run more jobs concurrently. Since modern data-intensive scientific applications can sometimes produce millions of I/O requests per second, I/O systems always suffer from heavy workloads and impede the overall performance. How to allocate I/O resources and guarantee the QoS (Quality of Service) for each individual application is becoming an increasingly important question. In this paper, we propose SDQoS, a software-defined QoS framework with the token bucket algorithm, aiming to meet the I/O requirements of concurrent applications contending for the I/O resources and improve the overall performance of the I/O systems. Evaluation shows that SDQoS can effectively control the I/O bandwidth within a 5%–10% deviation and improve the performance by 20% in extreme cases.

In this paper, a generalized input-output-based digital sliding-mode control (GIODSMC) is developed for piezoelectric actuators (PEA) with non-minimum phase (NMP) property. First, the instability mechanism of the traditional input-output-based digital sliding-mode control (IODSMC) for NMP systems is analyzed. Then, a generalized approximate input-output model is established to cancel the effect of unstable zeros in NMP systems. The generalized model can be transformed into different forms, which represents a class of approximate models suitable for this situation. Based on this model, a controller, called the GIODSMC, is presented. Unlike existing works, the developed controller ensures precision motion control for PEA with NMP property. Moreover, additional control parameters are not required to stabilize the NMP systems, and neither a hysteresis model nor a state observer is needed for the developed method. Stability of the closed-loop system is theoretically analyzed. At last, the presented method is tested through numerical simulations and experimental investigations on a piezoelectric actuator device.

Random-utility-based multiregional input–output (RUBMRIO) models are used to study the impact of changes in transport networks or spatial economies on interregional or international trade patterns. These models rely on elastic prices algorithms to estimate trade flows. According to the literature, two different RUBMRIO elastic prices algorithms exist: an original algorithm that was the subject of theoretical investigation, and a modified algorithm that has been commonly used in practice. The original algorithm measures prices and acquisition costs in dollars, whereas the modified algorithm measures prices and acquisition costs in units of utility. By deriving the equivalence conditions of these algorithms, it is proven that the modified algorithm is only equivalent to the original algorithm under very restrictive conditions: first, initial sector prices must be the same in each region; second, cost parameters must be the same for all industries; and third, no other variables can be introduced into the original trade coefficient model specification. In a numerical example, the modified algorithm results in a mean absolute percentage error of 56% for trade flow values. Due to these restrictions, it is recommended that future studies adopt the approach of determining initial RUBMRIO prices endogenously before calibration, which are shown be solved directly from a system of linear equations, and applying the original RUBMRIO elastic prices algorithm (measuring prices in dollars).

This paper uses an input–output (I–O) framework, considering five I–O matrices, over the period 1989–90 to 2007–08 to evaluate the total factor productivity growth (TFPG) of different sectors of the Indian economy. The methodology adopted under this framework (Miller and Blair 2009) takes into account not only the contributions of the value-added inputs such as labour and capital but also the contribution of the intermediate inputs to production. Subsequently, we compute forward and backward linkages of sectors in terms of output as well as employment to examine whether the high-linked sectors of the economy are relatively better in terms of productive growth or not. Our results show that agriculture has high employment linkages with other sectors of the economy together with positive TFPG. Several other sectors such as food, beverages, textile, printing, and so on do not necessarily have high linkages even though they reveal positive TFPG, especially during the post-liberalization period. By carrying out a sector-wise analysis, the paper identifies the high-linked sectors that need special attention in terms of productivity improvements for improving overall competitiveness of those industry segments that can generate a high level of output and/or employment through their linkage effects.

This paper is concerned with distributed cooperative control of linear uncertain agents with constraints on the amplitudes of inputs and outputs. We derive a condition for achieving both state synchronization and input/output constraints in the presence of norm-bounded uncertainties in the agent models. We develop a synthesis method of the robustly synchronizing state feedback gain in terms of linear matrix inequalities based on the notion of positively invariant sets. The desired feedback gain can be efficiently obtained by solving a certain convex programming whose size is independent of the number of agents.

This paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.

Input–output finite-time stability (IO-FTS) of fractional-order positive switched systems (FOPSS) is investigated in this paper. First of all, the concept of IO-FTS is extended to FOPSS. Then, by using co-positive Lyapunov functional method together with average dwell time approach, some sufficient conditions of input–output finite-time stability for the considered system are derived. Furthermore, the state feedback controller and the static output feedback controller are designed, and sufficient conditions are presented to ensure that the corresponding closed-loop system is input–output finite-time stable. These conditions can be easily obtained by linear programming. Finally, three numerical examples are given to show the effectiveness of the theoretical results.

The expansion of the wide-area measurement system has provided some control strategies to improve the low-frequency oscillation modes in electric power systems. One of these strategies is to use remote signals for a wide-area damping controller (WADC) to enhance the small-signal stability of the power system. However, the expansion of the Electric Power System increased the generator number connected to the grid, and then some challenges to the WADC design surged such as which input–output pairs of the WADC should be designed to improve the closed loop system damping. Typically, the WADC is a centralized controller and may have many design elements. The interactions among the elements may compromise the central controller design and its purpose. This paper proposes a procedure based on genetic algorithms in order to design a robust central controller. This procedure automatically chooses the input–output pairs of the central controller that will contribute to damping the low-frequency oscillation modes. The design procedure considers topological changes and time delay variations. Small-signal analysis and time-domain nonlinear simulations are carried out in the multi-machine Australian Equivalent Power System, an IEEE benchmark model for small-signal stability analysis.

In this paper, we develop intuitionistic fuzzy data envelopment analysis (IFDEA) and dual IFDEA (DIFDEA) models based on $$\alpha $$ - and $$\beta $$ -cuts. We determine intuitionistic fuzzy (IF) efficiencies based on $$\alpha $$ - and $$\beta $$ -cuts. We develop an IF correlation coefficient (IFCC) between IF variables to validate the DIFDEA models. We propose an index ranking approach to rank the decision making units (DMUs). Also, we propose an approach to find the IF input–output targets which help to make inefficient DMUs as efficient DMUs in IF environment. Finally, an example and a health sector application are presented to illustrate and compare the proposed methods.

Modern experiments in High Energy and Nuclear Physics heavily rely on distributed computations using multiple computational facilities across the world. One of the essential types of the computations is a distributed data production where petabytes of raw files from a single source has to be processed once (per production campaign) using thousands of CPUs at distant locations and the output has to be transferred back to that source. The data distribution over a large system does not necessary match the distribution of storage, network and CPU capacity. Therefore, bottlenecks may appear and lead to increased latency and degraded performance. In this paper we propose a new scheduling approach for distributed data production which is based on the network flow maximization model. In our approach a central planner defines how much input and output data should be transferred over each network link in order to maximize the computational throughput. Such plans are created periodically for a fixed planning time interval using up-to-date information on network, storage and CPU resources. The centrally created plans are executed in a distributed manner by dedicated services running at participating sites. Our simulations based on the log records from the data production framework of the experiment STAR (Solenoid Tracker at RHIC) have shown that the proposed model systematically provides a better performance compared to the simulated traditional techniques.

Unfortunately, some errors were detected after publication of the article referred to in the title. This errata corrects errors in the proofs of Lemma 5 on page 23 and Proposition 1 on page 26 of the article. The authors also noticed a weaker condition for Lemma 1 on page 19 to hold. The corrections and improvements do not impact the main results and conclusion of the article.

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices (aka. the hardware double fetch). Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O (input and output) memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

This paper is concerned with the input-output finite-time control of uncertain positive impulsive switched systems(UPISS) with time-varying and distributed delays. Firstly, the definition of input-output finite-time stability is extended to UPISS, and the proof of the positivity of UPISS is also given. Then, by constructing multiple linear copositive Lyapunov functions and using the mode-dependent average dwell time(MDADT) approach, a state feedback controller is designed, and sufficient conditions are derived to guarantee that the corresponding closedloop system is input-output finite-time stable(IO-FTS). Such conditions can be easily solved by linear programming. Finally, two examples are given to demonstrate the effectiveness of the proposed method.

Jorg Hansen, John Horty, and Xavier Parent and Leendert van der Torre have all recently described some sort of nonmonotonic logic to model reasons and their interactions. Horty’s framework is broader in scope than the other two, encompassing both reasoning about the relative strengths of reasons and reasoning about which reasons to consider in the first place. Hansen discusses a plethora of approaches and examples, including Horty’s, arguing that his preferred system best captures our intuitions. And Parent and van der Torre present a family of systems of input/output logic, which are in some ways the most flexible. In this paper, I aim to combine these features. Without attempting to answer the question of which intuitions are the best to capture, I first argue that there are good reasons to explore systems that behave more like Hansen’s than Horty’s. I then show that Parent and van der Torre’s framework of input/output logic can exactly duplicate Hansen’s system but is flexible enough to produce other results as well. Finally, I extend their framework to include the additional kinds of reasoning Horty discusses, showing that the resulting theory can handle a wider range of cases than Horty’s.

This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and there input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed to solve these problems. More specifically, it is shown that robust semi-global leaderless consensus can be achieved over a connected undirected graph and robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.

This paper applies the conventional DEA model and super-efficiency analysis in measuring the efficiency of an electricity distribution utility which involves 12 power lines as DMUs. The input indicators considered are purchased electricity supply and the total length of power lines, while electricity consumed, the number of consumers, and total power losses are the output indicators. The results revealed that 4 out of the 12 power lines are inefficient and thus need to be improved. The model provided a guideline how these inefficiencies may be addressed by means of benchmarking.

Hot data identification is crucial for many applications though few investigations have examined the subject. All existing studies focus almost exclusively on frequency. However, effectively identifying hot data requires equally considering recency and frequency. Moreover, previous studies make hot data decisions at the data block level. Such a fine-grained decision fits particularly well for flash-based storage because its random access achieves performance comparable with its sequential access. However, hard disk drives (HDDs) have a significant performance disparity between sequential and random access. Therefore, unlike flash-based storage, exploiting asymmetric HDD access performance requires making a coarse-grained decision. This paper proposes a novel hot data identification scheme adopting multiple bloom filters to efficiently characterize recency as well as frequency. Consequently, it not only consumes 50% less memory and up to 58% less computational overhead, but also lowers false identification rates up to 65% compared with a state-of-the-art scheme. Moreover, we apply the scheme to a next generation HDD technology, i.e., Shingled Magnetic Recording (SMR), to verify its effectiveness. For this, we design a new hot data identification based SMR drive with a coarse-grained decision. The experiments demonstrate the importance and benefits of accurate hot data identification, thereby improving the proposed SMR drive performance by up to 42%.