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This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.

In many contexts, computers run both critical and untrusted software,necessitating the need for isolating critical software from untrusted software.These computers contain CPUs, memory and peripherals. For performance reasons,some of these peripherals have a direct memory access (DMA) controller (DMAC),allowing them to access memory without involving the CPU. This thesis is a study of memory isolation of peripherals. That is, howperipherals are configured such that they can access only restricted memoryregions. If DMACs are not configured appropriately, they may be able to read andleak sensitive data, or modify and corrupt sensitive data and critical programcode, with potentially disastrous consequences. Since DMACs are configured by anassociated DMAC driver software, the DMAC driver is a critical point for security.Testing drivers for isolation is infeasible, due to the necessity of exhaustivetesting to detect violating writes and differential testing to detect violatingreads. Therefore, we use formal methods and interactive theorem proving (ITP), todevelop formal conditions that, if satisfied by a driver, ensure that theassociated DMAC is memory isolated. We present a detailed case study of formally verifying memory isolation of anEthernet DMAC in the ITP HOL4. We also generalize this case study in the form ofa verification tool in HOL4, reducing the complexity and time needed to formallyverify the conditions under which a DMAC is isolated. The tool includes ageneral DMAC model, reflecting the common features of DMACs. By means ofrefinement, we verified the conditions under which this general DMAC isisolated. By instantiating this general DMAC model with the specifics of a givenDMAC, and by proving the associated proof obligations, the user obtains theformal conditions for DMAC memory isolation. In addition, we have implemented and analyzed a run-time monitor that checksthat Ethernet DMAC configurations respect memory isolation. This monitor is usedin a CCTV system, part of an isolation verified hypervisor, on top of whichLinux and a CCTV guest runs. This system gives Linux Internet access, butwithout being able to to obtain unencrypted photos taken by the CCTV guest: Thehypervisor configures the CPU such that Linux and the CCTV guest are isolated,except for a communication channel used to transfer encrypted photos; and themonitor applies only DMAC configurations, defined by Linux, that cannot causethe DMAC to access non-Linux memory. Många datorer exekverar både kritisk och opålitlig mjukvara. Detta gör det nödvändig att isolera dessa typer av mjukvara från varandra. Dessa datorer innehåller CPUer, minne och periferienheter. För att maximera prestanda så innehåller vissa periferienheter en minnesstyrkrets för direkt minnesåtkomst som gör att periferienheterna kan läsa och skriva minnet utan inblandning av CPUn. Denna uppsats presenterar en studie av hur periferienheter kan konfigureras så att de är isolerade till vissa minnesregioner. Om minnesstyrkretsen inte är konfigurerad på rätt sätt så kan den möjligtvis läsa och läcka känslig data, eller skriva och förstöra känslig data och kritisk programkod, med förödande konsekvenser. En minnesstyrkrets konfigureras av en tillhörande drivrutin. För att begränsa styrkretsen till vissa minnesregioner så måste därför drivrutinen konfigurera styrkretsen på en motsvarande sätt. Detta gör att drivrutiner för minnesstyrkretsar har en kritisk roll i datorsäkerhet. Testning av drivrutiner för isolering är omöjligt på grund av det stora antalet nödvändiga testfall för att utesluta oönskade minnesåtkomster. Därför använder vi formella metoder och ett interaktivt bevisverktyg för att identifiera formella villkor som garanterar, om satisfierade av drivrutinen, att styrkretsen är minnesisolerad. Vi presenterar en detaljerad formell verifiering i bevisverktyget HOL4 av att en minnesstyrkrets i en Ethernet-adapter är minnesisolerad. Vi generaliserar denna studie i form av ett verifieringsverktyg i HOL4, som reducerar arbetsbördan för att verifiera minnesisolering av minnesstyrkretsar. Verktyget inkluderar en generell model som beskriver de gemensamma operationerna som minnesstyrkretsar utför. Vi har utgått från en abstrakt och generell styrkretsmodell, och steg för steg lagt till mer detaljerade styrkretsoperationer och verifierat att den resulterande modellen respekterar minnesisolering, och slutligen erhållit verifiering av en konkret generell modell som beskriver riktiga minnesstyrkretsar. Genom att instantiera den generella styrkretsmodellen med specifika styrkretsoperationer, och bevisa vissa tillhörande förpliktelser, så erhåller användaren de formella isoleringsvillkoren. Vi har också implementerat och analyserat en körtidsmonitor. Denna monitor kontrollerar att konfigurationer av en minnesstyrkrets i en Ethernet-adapter bevaras i ett minnesisolerat tillstånd. Denna monitor används i ett CCTV-system, som del av en hypervisor, som Linux och en CCTV-gäst exekveras ovanpå. I detta system har Linux internetåtkomst, men utan möjlighet att kunna erhålla okrypterade foton tagna av CCTV-gästen. Hypervisorn konfigurerar CPUn sådant att Linux och CCTV-gästen är isolerade, bortsett från en kommunikationskanal som används för att överföra krypterade foton mellan CCTV-gästen och Linux. Monitorn verkställer enbart styrkretskonfigurationer, konstruerade av Linux, som inte kan få styrkretsen att läsa eller skriva minne som inte tillhör Linux. QC 20230523

We analytically characterize the aggregate productivity loss from allocative distortions in a setting that accounts for the sectoral linkages of production. We show that the effects of distortions and the role of sectoral linkages depend crucially on how substitutable inputs are. We find that the productivity loss is smaller if input substitutability is low. Moreover, with low input substitutability, sectoral linkages do not systematically amplify the effects of distortions. In addition, the impact of the sectors that supply intermediate inputs becomes smaller. We quantify these effects in the context of the distortions caused by market power, using industry-level data for 35 countries. With our benchmark calibration, which accounts for low input substitutability, the median aggregate productivity loss from industry-level markups is 1.3%. To assume instead unit elasticities of substitution (i.e., to use a Cobb-Douglas production function) would lead to overestimating the productivity loss by a factor of 1.8. Sectoral linkages do amplify the cost of markups, but the amplification factor is considerably weaker than with unit elasticities.

This paper presents a phase rotator (PR)-based delay-locked loop (DLL) for a dynamic random-access memory interface in 28-nm CMOS technology. A direct input-output comparison using a sub-sampling technique reduces the effect of a timing mismatch of a replica delay line for synchronizing an input clock and output strobe clock. A divided clock samples the input clock for phase alignment. A 4-b analog-to-digital converter was used for input phase acquisition. The output phase is aligned with respect to the sampling clock phase using a bang-bang phase detector. Two DLLs for the input-output synchronization are implemented in a cascade structure, and the loop delay of the replica delay line is considerably reduced. In the proposed DLL, a PR delay line using an 8-phase clock generator is adopted for linearity to reduce the phase difference of the phase interpolation from π/2 to π/4. The resolution of the PR-DLL is 7-b of 2π, which consists of a 3-b coarse and a 4-b fine control. The DLL operates from 2.4 to 8 GHz, and the power dissipation at the maximum input frequency is 20.2 mW. The measured clock root mean square jitter was 1.68 ps. According to the simulation results, the phase mismatch between the input and output clocks was reduced by 80.5%.

In this paper, the optimal output synchronization of heterogeneous multi-agent systems is studied. To overcome the shortcoming of previous methods that require system model, a data-based optimal control policy is proposed for the output synchronization problem . Besides, the intrinsic equivalence relationship between model-based optimal solution and the proposed data-based optimal solution is proved. To solve the proposed control policy, a reinforcement learning algorithm based on measured input-output data is presented, which is different from existing model-free algorithms based on internal state. According to the algorithm, the optimal output synchronization problem of heterogeneous multi-agent systems can be solved when the full-state vector is unavailable. Finally, the effectiveness of proposed algorithm is verified by simulation examples.

Neural networks (NNs) are effective models for data-driven system identification. However, these data-based models do not give the probability properties of the system. Is it possible to use NNs to learn both the dynamics and input–output probability distributions to improve the modeling? In this article, we propose a special neural model, which combines the restricted Boltzmann machine (RBM) and the feedforward NNs. The stochastic RBM learns the input–output probability distributions. The feedforward NN learns the dynamics of nonlinear systems. For the input–output probability distributions, we give the calculation methods of their conditional and joint probabilities. The approximation abilities of this neural model are analyzed. We use two nonlinear systems to compare our guaranteed probability distribution method with the other black-box identification methods. The results show that this novel model is much better, when there are big noises and the dynamics of the system are complex.

This paper presents an input–output linearization (IOL) control strategy to achieve PFC (power factor correction) and output voltage constant in a boost converter. The method transforms the nonlinear system to a linear one in new coordinates by the appropriate nonlinear coordinate transformation and state feedback, ultimately allowing the controller to be designed according to the classical linear control theory. An example implementation is simulated using PSIM software. The simulation results show that the phases of input voltage and input current are consistent, and the output voltage remains constant under load step changes. A set of experimental devices are set up to verify the control method, and the results are consistent with the simulation. Thus, the method is proved to be effective in achieving PFC and provides robust constant voltage output in the boost converter.

El objetivo principal de esta investigación es la generación de la Matriz Insumo Producto Regional y la identificación de los sectores determinantes para el Estado de Durango. Lo anterior se lleva a cabo mediante la metodología indirecta de Flegg y Weber (1997) para determinar las relaciones intersectoriales regionales, a partir de un ajuste sobre los coeficientes de ubicación cruzada de la Matriz de Insumo Producto Nacional (MIPN) 2013. Este análisis muestra como sectores clave:1) Generación, transmisión y distribución de suministro de energía eléctrica, agua y gas; 2) Servicios financieros y de seguros, 3) Información en los medios de comunicación, entre otros. En estudios regionales similares, el parámetro de ajuste es determinado a priori, por lo que una recomendación es la de estimar el valor adecuado para cada región. La principal limitación es el uso de MIPN, que son publicadas cada 5 años, lo que implica que la herramienta realiza un análisis estático, sin embargo, esto puede solventarse mediante pronósticos basados en metodologías para la actualización de los coeficientes técnicos. Hasta donde tenemos conocimiento, somos los primeros en realizar un análisis sobre los sectores productivos del estado de Durango y de implementar un proceso de optimización sobre el parámetro de ajuste. Los resultados de la clasificación concuerdan con la realizada a partir del Censo Económico y los Valores Agregados Brutos Estatales de los sectores que se encuentran en INEGI.

Since the beginning of the 2008 economic crisis, economic growth and development have been in the forefront of economic research. In a global context, the highest levels of poverty as well as malnutrition problems are found in sub-Saharan African countries. Social Accounting Matrices (SAMs) are useful tools to describe the economic situation of these countries, the interactions among economic agents and to support policymakers in implementing their policies. The paper shows the macro- and micro-economic indicators of Kenya, which can be directly extracted from the described SAM 2017 for Kenya The analysis of the SAM multipliers shows that agri-food multipliers are in general above the average reflecting the strength of backward and forward linkages of Kenya’s economy.

This article investigates the problem of input–output finite-time estimation for a class of discrete-time complex networks with switching topology. The dynamic event-triggered communication manner is proposed to conduct the data transmission of the switching signal and measurement outputs subject to random sensor nonlinearities. The asynchronism of the switching and triggering actions also leads to the received switching index by the designed estimator being inconsistent with that of the estimated switched complex networks. Then, a novel piecewise Lyapunov functional, which is proved to be less conservative than the general one, is then constructed for handling the input–output finite-time estimation under the unmatched circumstance. Sufficient conditions are formulated to ensure that the underlying estimation error systems are input–output finite-time stochastically stable with respect to the norm bounded inputs within a finite time. Finally, the superiority and applicability of the developed approach are verified by a simulation example.

In the absence of data on the destination industry of international trade flows most multi-regional input-output (MRIO) tables are based on the import proportionality assumption. Under this assumption imported commodities are proportionally distributed over the target sectors (individual industries and final demand categories) of an importing region. Here, we quantify the uncertainty arising from the import proportionality assumption on the four major environmental footprints of the different regions and industries represented in the MRIO database EXIOBASE. We randomise the global import flows by applying an algorithm that randomly assigns imported commodities block-wise to the target sectors of an importing region, while maintaining the trade balance. We find the variability of the national footprints in general below a coefficient of variation (CV) of 4%, except for the material, water and land footprints of highly trade-dependent and small economies. At the industry level the variability is higher with 25% of the footprints having a CV above 10% (carbon footprint), and above 30% (land, material and water footprint), respectively, with maximum CVs up to 394%. We provide a list of the variability of the national and industry environmental footprints in the Additional files so that MRIO scholars can check if an industry/region that is important in their study ranks high, so that either the database can be improved through adding more details on bilateral trade, or the uncertainty can be calculated and reported. Supplementary Information The online version contains supplementary material available at 10.1186/s40008-021-00250-8.

This research uses the publicly available World Input-Output Database (WIOD) to investigate the relationship between an industry’s markup and its upstreamness, the industry’s position in the vertical chain of production; the research also identifies common attributes among high-markup industrial sectors: higher-markup industries display a higher level of capital compensation and a lower share of labour and other inputs in the value of output. Finally, it is found that upstream industries, those producing mostly raw materials and intermediate products enjoy higher market power than their downstream counterparts. This result could be among the first in the literature to find evidence of double marginalization at an industry level of aggregation. It also suggests that virtually all final product prices may incorporate substantial markups through their inputs.

Este artículo estudia el desempeño comercial de los 12 principales países exportadores de bienes industriales en el periodo 2004-2013, analizando tanto la pretendida asociación existente entre la productividad laboral y la competitividad externa, como el rol de las industrias altamente tecnológicas en el éxito exportador. La aplicación de la metodología de Wang, Wei, y Zhu (2018), a partir del uso de matrices insumo-producto globales, para el caso del comercio de E.U y China, muestra el mejor posicionamiento de E.U en CGV y ejemplifica la relevancia de las nuevas dinámicas productiva globales para la evaluación del comercio internacional a nivel sectorial.

This article addresses the issue of input–output finite-time stability (IO-FTS) for nonlinear systems by employing sliding-mode control (SMC) methodology. Many practical systems subject to complex factors, such as the single-link robot arm model (SLRAM), can be characterized as nonlinear systems. Our attention is focused on designing a fuzzy-model-based finite-time SMC law to attenuate the influences of uncertainty, nonlinear term, and external disturbance during the finite-time region. First, a novel integral sliding-mode surface is proposed based on the Takagi–Sugeno fuzzy rule. Then, by using the key point of Lyapunov function theory, an appropriate fuzzy SMC law is designed to make sure that the signal variables can arrive at a domain within the assigned fixed-time level. Moreover, some new IO-FTS criteria are constructed for the resulting sliding dynamics over the whole finite-time level, including reaching phase and sliding motion phase. Via the SLRAM, we demonstrate the effectiveness of the proposed SMC approach.

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.

Global climate change is an alarming problem nowadays. Weather temperatures have been uncertain because of what many scientists claim is the effect of greenhouse gases, contributed mainly by the energy industry. In Malaysia, the energy industry provides a significant contribution to it economy making up about 20 percent of the GDP. Malaysia is the third largest natural gas exporter in the Asia-Pacific region in 2011 and eleventh in the world (British Petroleum Statistical Review of World Energy, 2014). Malaysia's CO2 emission recorded at 185 million tonnes in 2010 (International Energy Agency, 2012). Mohd Safaai et al. (2011) projected that without any mitigation measures 285.73 million tonnes of CO2 will be released in 2020. In the Copenhagen forum 2009, Malaysia has pledged to reduce 40% of carbon emissions by 2020 compared to the 2005 levels. Motivated by the government efforts, this study will construct environmentally extended input-output framework for Malaysia to obtain the energy-related CO2 emission intensities by sectors. The study is responded to the inadequate of reliable data on Malaysia’s sectoral CO2 emissions and to the growing awareness of the effectiveness of Malaysia climate change policies. This study expanding the current understanding of interactions among economic activities, energy intensities, and CO2 emissions. Results of this study found that Transportation recorded the second highest contributor to CO2 emission, and these findings were different to many other existing studies that found transportation sector as a main contributor to CO2 emission. In addition, results suggest that it is necessary to control environmental problems and encourage the energy use efficiency in the production process, particularly in the Building and construction sector, Transportation, Electricity, gas and water and Agriculture sector in Malaysia The findings will help the policy-makers particularly in Malaysia to develop a strategic plan and tools to manage CO2 emission with respect to climate change in these four sectors.

Regional industrialisation and integration form part of the economic policy priorities of a number of countries in Southern Africa and of the Southern African Development Community (SADC). Manufact...

The production process in the system of economic cycle of the national economy, as well as the relevant tools for the study of production processes, is considered. The production infrastructure of the national economy is studied as an important part of critical infrastructure, reproduction processes. Production is considered in the context of the formation of added value by large groups of types of economic activity, at the institutional and regional levels, in the context of making management decisions at the macro- and microeconomic levels. The inter-industry balance sheet tools are defined as the basic one for assessing production processes and their criticality. To this end, aggregated three-sector input-output tables in producer’s prices for a number of countries have been prepared. Within their framework, the sectoral structure of the economy is considered, direct costs and full costs (Leontief matrix) are determined, indicators of criticality of the production infrastructure are determined. It is proposed to assess the criticality of production infrastructure using such parameters as the share of gross added value in output, the cost of sectors and the economy as a whole, the coefficients of impact and recall, the classification of industries into key, with direct and inverse impact, etc. Comparative studies of criticality indicators for a number of countries were carried out, in particular, direct costs and derivative indicators of criticality of the production system (Frobenius number, Frobenius vector, etc.) were determined. The proposed approaches to improving the parameters of criticality of the production infrastructure of the Ukrainian economy were worked out.

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input–output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input–output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches.

As many other developing countries, the statistical office of Turkey displays an input-output table at the national level only and it is not regularly updated. Yet, the need for regional-level information is growing because numerous publicly-funded development projects and private investments have taken place at the sub-national level over the recent years. In this paper we create an input-output table for one region, TR33, for the year 2014 by regionalizing and updating the national table through a location quotient approach. It allows us to calculate the regional employment and output multipliers across 35 sectors and to verify if their ranking varies across location quotient techniques. We expect our conclusions will help policy-makers and private investors take an informed decision when investing in TR33.

This paper will focus on optimizing parameters of sliding mode controllers (SMC) for hybrid stepper motor models simulated in Matlab/Simulink. The main objective is to achieve a smooth transient and robust, steady-state to track reference rotor position when the stepper motor is subjected to load disturbances. Two different structures of SMC controllers will be studied, which are based on the flat system concept that is applicable to the stepper motor model. The hassle to determine controller parameters will be optimized using the Simulink Response Optimizer application. The performance of the controllers will be evaluated by considering load torque and variation in the model parameters. Although the results showed that an open-loop controller could move the rotor to the desired position, however, the transient response had undesired oscillations before the output settled at the steady state. The response was improved by optimizing SMC controllers’ parameters to meet the desire step response requirement. Despite both SMC methods have successfully tracked the reference, there are some challenges to deal with each method in regard to the state measurements, the number of optimized controllers’ parameters, and the scattering of control inputs.

In this paper, we develop a metric of reshoring at the macroeconomic level, at the level of industries or countries. Our dynamic measure computes the change in the domestic–foreign input ratio and therewith captures the idea of moving production back to the home country. Additional conditions ensure that the measure is confined to actual reshoring and does not pick up, for example, mere changes in the scale and composition of production. We show that reshoring is only weakly approximated by reduced offshoring. We compute the reshoring intensity from the World Input Output Database and illustrate the use of the data with several applications. Specifically, we compute trends in reshoring for examples of countries and industry sectors, we investigate reshoring from China, and examine the impact of an increasing degree of automation for reshoring in emerging and developing economies.

The main problems of constructing multiregional input-output (IO) models used for the development of spatial and sectoral long-term national economy forecasts are caused by the absence of proper statistics. Therefore, it is necessary to make up the input-output tables (IOTs) based on a limited set of direct and indirect indicators that measure spatial patterns of production, consumption, capital formation, etc., sufficiently accurate for filling up multiregional IO models. The paper discusses Russian IOT spatial mapping problems and proposes an approach to partial automation of the procedures necessary for regional IOT construction (by federal districts). The regionalization was carried out using a static IO model with the bounding sum control. As a result, a set of consistent regional tables for 2015 was developed, with the eight IOT sum equalling Russia’s IOT. Based on the estimated regional IOTs, a static multiregional input-output model (OMIOM) across federal districts for 2015 was made up. The model allows us to proceed to the follow-up phase, i. e., developing a semi-dynamic model for long-term national economic projection computations

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

Multiplier effects play a major role in determining importance of a sector of interest to the national economy. In order to determine the significance of Croatian transport sectors, in this paper input-output analysis was applied. A comparative analysis of type I and type II output, gross value added and employment multipliers based on input-output tables for Croatian economy for the year 2010 and 2015 was performed. Type I multipliers were calculated based on the open IO model. Closed IO model was used in type II multipliers determination. Results indicate that sector code H49—Land transport services and transport services via pipelines and sector code H50— water transport services in the year 2015 recorded lower type I and type II output, gross value added and employment multipliers compared to the year 2010. For sector code H51—Air transport services, significant growth in terms of all three economic indicators was detected, especially when interest is focused on type I and type II employment multipliers. Results presented in this paper could be valuable for academia and general public because they reflect multipliers estimation of transport sectors in Republic of Croatia for the first time based on sound macroeconomic analysis tool.

The plastic industry is an economic sector that plays an important role in promoting a circular economy in Vietnam. This study used the updated 2018 input-output (IO) table to identify and analyse the production and consumption of seven different plastics including: HDPE, PS, PE, PET, PVC, PP, and others. The study also integrated the IO model to unveil the environmental burden of these plastics through the plastic demand of 40 economic sectors and households. As a result, in 2018, the amount of direct solid waste from the plastic industry was 58,147 tons and the amount of indirect solid waste from the plastic industry to other economic sectors and the household sector were 214,258 tons and 6,262 tons, respectively. Agriculture and its services, food processing, fashion manufacturing, basic chemical production, electrical and electronic equipment production, and transport production embodied the highest indirect burdens due to their use of plastic products. This study contributes to MFA research and developing strategies for sustainable production and consumption of plastics and the management of plastic waste in Vietnam.

The main problems of constructing models used to develop of spatial and sectoral long-term economic forecasts are due to the lack of direct statistical data in full, i.e. regional tables of distribution of goods and services. Their calculation has to be carried out on the basis of a limited set of direct and indirect indicators that make it possible to determine the spatial structures of production, consumption, accumulation of fixed capital, etc. with an accuracy sufficient for further use in the information content of interregional intersectoral models. The report examines the problems of spatial allocation of the all-Russian table “input-output” and proposes the author's approach to the partial automation of the procedures necessary to construct a set of regional tables (by federal district). Regionalization was carried out using a static interregional intersectoral model with control of bordering outcomes. The result is a set of consistent regional tables as of 2015 - the sum of eight input-output tables exactly matches the all-Russian table. The constructed static multiregional input-output model (OMIOM) across federal districts is necessary for the next stage of work - the development of a semi-dynamic model used to calculate options for long-term national economic forecasts.

In black-box testing, test sequences may be constructed from systems modelled as deterministic finite-state machines (DFSMs) or, more generally, observable non-deterministic finite state machines (ONFSMs). Test sequences usually contain state identification sequences, with unique input output sequences ( UIOs ) often being used with DFSMs. This paper extends the notion of UIOs to ONFSMs. One challenge is that, as a result of non-determinism, the application of an input sequence can lead to exponentially many expected output sequences. To address this scalability problem, we introduce ${\mathcal K}$ K - UIOs : UIOs that lead to at most ${\mathcal K}$ K output sequences from states of $M$ M . We show that checking ${\mathcal K}$ K - UIO existence is PSPACE-Complete if the problem is suitably bounded; otherwise it is in EXPSPACE and PSPACE-Hard. We provide a massively parallel algorithm for constructing ${\mathcal K}$ K - UIOs and the results of experiments on randomly generated and real FSM specifications. The proposed algorithm was able to construct UIOs in cases where the existing UIO generation algorithm could not and was able to construct UIOs from FSMs with 38K states and 400K transitions.

Groundwater quality assessment is characterized by pollution injection rates, pollution injection locations and duration of pollution injection for identifying spatial and temporal variation. In this study, spatial variations are obtained by placing observation wells in the downstream zone. Temporal variations in contaminant concentration has been simulated during the study period. Generally, simulations are carried out using various numerical models, which are subject to the availability of all required input parameters and are necessary for the proper management of contaminated aquifers. In previous publications, artificial neural networks (ANNs) are prescribed in such situations as these modeling methods focus on available input/output datasets, thus resolving the concern of obtaining all inputs that a numerical simulator usually demands. Past studies have predicted groundwater breakthrough contaminants. But the effects of input/output variations need to be discussed. This study aims to quantify the effects of a few input/output datasets in the performance of ANN models to simulate pollutant transport in groundwater systems. The combinations of input/output scenarios have rendered these ANN models sensitive to variations, thus affecting model efficiency. These outcomes can reliably be employed for contaminant estimation and provide a paradigm in data collection that will help hydrogeologists to develop more efficient prediction models. HIGHLIGHTS A brief review on groundwater modeling using artificial neural networks.; Effects of input and output parameters in ANN modeling.; ANN modeling strengths and weakness in varying input/output parameters.; The practical implication of this methodology.

How to identify the key nodes effectively in urban traffic networks to achieve the equitable resource allocation face to the complex traffic network? This issue needs to be solved in current traffic management. This study considered the urban traffic network topology and network traffic status, put forward an improved model based on the economics of the input-output method by introducing a virtual node to the selected network set up with the flow of urban traffic network, sensor nodes by Leontief inverse matrix calculation coefficient to determine node importance, according to the node importance to deliberate attack traffic network to analyze its robustness, to test the accuracy and practicability of the method. The results show that this improved method adopted to measure the importance of traffic nodes from the global scope has the advantages of fast calculation and simple process and provides a more reliable basis for rational allocation of transport resources.

In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.