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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

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.

The reliable quantification of daily evapotranspiration (ET) over vast croplands is a quest in many scholarly works aimed at the precise practice of water resources management. Remote sensing–based empirical and nonempirical models were developed to overcome large-scale quantification issues, which are usually experienced when using conventional approaches for the estimation of ET. The surface energy balance system (SEBS) model was used to quantify the daily ET in the arid/semi-arid over Wadi Ad-Dwaser, Saudi. SEBS input variables are parametrically sensitive and climatic dependent, and the model input/output dependencies are of high comprehensibility; therefore, the optimization analysis of SEBS input/output parameters is the target of the current research. SEBS inputs reciprocal inconsistencies were determined using the artificial neural network analysis, while the output dependencies on the daily ET estimation were mapped. Results demonstrated that the temperature and relative humidity are the most sensitive parameters to be considered in the routine crop monitoring procedure. SEBS output thematic maps showed the robust proportional correlation between the daily ET and the conducted temperature map. Moreover, the estimated daily ET was inversely correlated with the estimated cold sensible heat fluxes. The findings suggest systematic monitoring and forecasting procedures for efficient water-saving management plans in Saudi Arabia.

This paper presents a water-restricted multi-regional input–output model to evaluate the economic impacts of water supply reductions in the Canadian Great Lakes Basin (GLB), one of the largest freshwater reservoirs in the world. The proposed model, first of its kind applied to the GLB, aims to minimize the impact of water supply disruptions on the GLB-economy, measured by the loss of GDP. A new flexible economic optimization procedure is introduced, capable of imposing resource constraints and ensuring minimal supply levels for intermediate and final consumption at the same time. The model accounts for inter-regional trade between different lake regions. The impacts of two climate change scenarios on water security and the economy are investigated, with and without additional food and energy security restrictions. The proposed economic optimization model holds promise as a new tool for resource-restricted Input–Output analyses.

A simultaneous wideband input/output matching technique for ultra-wideband (UWB) low-noise amplifier (LNA) is proposed in this paper. Feedback resistors leading the gate inductors combined with inductive dividers at output ports achieve an extended bandwidth and good input/output return loss. Moreover, $Q$ -factor improved vertical solenoid inductors are used in the matching networks for high gain and low noise figure (NF). The proposed matching technique, not only enhances the bandwidth, but also achieves a high gain and a low NF for the fabricated 3.1-10.6-GHz monolithic 180-nm CMOS UWB amplifier. Operating at low supply voltage, the measured power consumption is 18.9 mW, the measured gain of the UWB LNA is 15.02 dB, and the NF is 3.1 dB. Moreover, the measured input/output reflection coefficients S11 and S22 are lower than −9.4 dB and −15.8 dB, respectively, covering the full-band UWB frequencies. Compared to previously published full-band 3.1-10.6-GHz 180-nm CMOS UWB LNAs, the proposed LNA measurements demonstrate high gain, low NF, low supply voltage, low power dissipation, and good input/output reflection coefficients.

The transfer function of a linear mechanical system can be defined in terms of the quadruplet of state-space matrices ( A , B , C , D ) that can be identified from input and output measurements with subspace-based system identification methods. The estimation of the quadruplet has been well studied in the literature from both theoretical and practical viewpoints. Nonetheless, a practical algorithm for uncertainty quantification of its estimation errors and the uncertainty of the resultant parametric transfer function is missing in the context of subspace identification. For several output-only and input/output subspace methods, the covariance related to the matrices ( A , C ) and to the resulting modal parameters can be effectively obtained with recently developed first-order perturbation-based schemes, while the corresponding uncertainty quantification for the input-related matrices ( B , D ) is missing. In this paper, explicit expressions for the covariance related to matrices ( B , D ) are developed, and applied to the covariance estimation of the resulting transfer function. The proposed schemes are validated on a simulated data of a mechanical system and are applied to laboratory measurements of a plate.

In high performance computing, platforms (the supercomputers) are composed of computational resources divided into racks. Each rack have one (sometimes several) I/O nodes to access the parallel file system (PFS). Following the growth and the optimization of the machines, the amount of data involved in the calculation is increasing. As different parts of these racks are allocated to independent applications, and as the storage is shared between these, too many concurrent accesses can lead to contention and performance loss.This thesis consist in studying strategies to map applications on the platform and scheduling the different accesses to the storage system in order to avoid contention. We discuss the relevance of our solution both from the administrators point of view (using the throughput) and from the users one (using stretch).In a first part, we assume that the mapping was done upstream and only study the scheduling of bandwidth accesses in order to minimize the latency. We show the intractability of the standard I/O scheduling problem, discuss some simple cases and show the relevance of List-scheduling policies.In a second part, we extend our problematic to the problem of mapping applicationsinto the nodes in order to avoid concurrent accesses.To solve this problem, we propose a pack algorithm which, on a single I/O node, increases greatly the stretch while slightly degrading the makespan. However, this degradation in the consequence of a stricter control on resource usage. This allows to a better scaling with more I/O nodes as well as facilitating loss recovery with back-filling for example.Finally, we discuss how to extend standard resource management strategies such as list scheduling and the aforementioned pack scheduling in order to take I/O into account and what it means in terms of resource utilization and performance. Such strategies are largely used by HPC platform for resource allocation due to their simplicity. We observe that the pack scheduling policy induces a limitation on average that enables intensive I/O phases on condition that they are compensated during the execution. In comparison, list strategies always follow the prescribed restriction and are therefore more restrictive. It follows that pack scheduling are to be preferred when high quantities of data compared to the available bandwidth are involved. In most of our work, we considered that I/O accesses were exclusive. However, in highlighting notable performance discrepancies depending on the similarity between applications, we showed that some should be executed in parallel. As a conclusion, we discuss the recent development of our models seek to determine how to define these applications partitions.; En calcul haute performance, les plateformes (supercalculateurs) sont composées de ressources de calcul multiples divisées en étagères.Chacune d’elles possède un (ou plusieurs) nœuds d’E/S dédiés à l’accès au système de fichiers parallèles. Suivant la croissance et l’optimisation des machines, les quantités de données mises en jeux dans les calculs augmentent. Comme différentes parties de ces plateformes sont allouées à des applications indépendantes, et comme le système de stockage est partagé par toutes, de trop nombreux accès concurrent peuvent mener à de la contention et à une perte de performance.Cette thèse consiste à étudier des stratégies de placement d’applications sur la plateforme et comment ordonner les différents accès au système de stockage afin d’éviter la contention.Nous discutons de la pertinence de nos solutions à la fois du point de vue des administrateurs (en utilisant le débit) et du point de vue des utilisateurs (à travers le ralentissement).Dans une première partie, nous considérons que le placement effectué en amont et nous concentrons sur l’étude de l’ordonnancement des accès à la bande passante dans le but de minimiser la latence. Nous montrons que le problème d’ordonnancement d’E/S standard est NP-difficile. Nous étudions donc des cas simples et montrons la pertinence d’heuristiques de listes.Dans une seconde partie, nous étendons notre problématique au placement des applications sur les nœuds afin de limiter les accès concurrents.Afin de résoudre ce problème, nous proposons un algorithme par paquets qui, sur un nœud d’E/S simple diminue considérablement le ralentissement tout en dégradant seulement légèrement le temps total d’exécution. Cependant cette dégradation est la conséquence logique d’un plus grand contrôle sur les ressources utilisées. Cela permet un meilleur passage à l’échelle quand le nombre de nœuds d’E/S augmente tout en facilitant la récupération des des ressources inutilisées par des méthodes de remblayage par exemple.Enfin, nous étudions comment étendre les stratégies standard de gestion de ressource telles que les heuristiques de liste et les techniques susmentionnées d’ordonnancement par paquets dans le but d’inclure les E/S et ce que cela signifie en terme d’utilisation de ressources et de performance.De telles stratégies, sont largement utilisées par les plateformes de calcul haute performance pour l’allocation de ressources en raison de leur simplicité. Nous observons que les stratégies par paquets induisent une limitation en moyenne qui permet des phases d’E/S intensives à la condition qu’elles soient compensées au cours de l’exécution. En comparaison, les stratégies de listes suivent constamment la restriction prescrite et sont par conséquent plus restrictives.Il en résulte que les stratégies par paquets sont préférables lorsque de grandes quantités de données relativement à la bande passante disponible sont impliquées.Dans la majeure partie de notre travail, nous considérons que les accès à la bande passante sont exclusifs. Cependant, en soulignant des différences notables de performances en fonction du degré de similarité des applications, nous montrons que certains de ces accès devraient être exécutés en parallèle. En conclusion, nous esquissons des développement récents de nos modèles cherchant à déterminer quelles partitions d’applications seraient concernées.

The construction industry is responsible for large quantities of construction and demolition waste and almost 50% of the worldwide annual resource consumption, putting the environment, its natural resources, and ecosystems under high pressure. Therefore, governments are implementing regional policies that support a circular economy (CE). But how do we know whether these developments will lead to a shift toward a CE on a regional scale? How can we identify hotspots in a value chain and regional economy to support decision-makers and to develop regional policies? We propose an integrated assessment method that considers indicators for environmental impacts and economic benefits by combining Material Flow Analysis (MFA) and Life Cycle Assessment (LCA) with Input-Output Analysis (IOA) as the connecting element. It provides the necessary data and indicators for a holistic and comprehensive evaluation of a region or industry. We demonstrate its benefits and limitations taking the Swiss canton of Aargovia as an example. We analyze which processes in the material flow system of construction minerals are decisive for formulating mass-related or financial policies encouraging a CE. We show that a shift toward a CE can only be captured by combining material and money flows in a joined model, because a significant increase of services—mainly waste management—is a core element in this development. It can only be covered sufficiently by combining environmental and economic assessment. Our model captures the degree to which a regional economy is advanced in the transition toward a CE to compare different regions or analyze scenarios of future developments., Journal of Industrial Ecology, 26 (2), ISSN:1088-1980, ISSN:1530-9290

The aim of the present paper is to provide new criteria for the asymptotic behaviors of dynamical systems, approaching simultaneously four directions: nonuniform, uniform stability and respectively nonuniform, uniform expansiveness, treating both discrete and continuous-time case and using computational type methods. In order to characterize the asymptotic properties of discrete variational systems, we consider two classes of weighted l ∞ -spaces and we define two types of nonuniform input-output properties, by employing spaces from these classes in the admissible pair. Our study continues the line initiated in Dragicevic, Sasu and Sasu [J. Differential Equations 268 (2020), 4786-4829] and [J. Dynam. Differential Equations, doi.org/10.1007/s10884-020-09918-4], giving a completely new perspective regarding the connections between admissibility techniques for stability and expansiveness. We obtain necessary and sufficient conditions for nonuniform, uniform exponential stability and also for nonuniform, uniform exponential expansiveness, providing a unified approach and emphasizing the key differences when passing from nonuniform to uniform behavior. Next, we present two categories of applications of the discrete-time results. First, we deduce criteria for stability and expansiveness in terms of input-output type properties along period orbits. After that, we obtain characterizations for uniform exponential stability and uniform exponential expansiveness of skew-product semiflows in terms of the solvability of an associated integral control system between well-chosen weighted spaces of continuous functions, pointing out new computational methods of recovering the continuous information from the discrete-time behavior in the framework of nonuniform input-output techniques. Our results are applicable to general classes of variational systems.

The article describes and compares the methods of input-output (I/O) virtualization, adapted for the Elbrus architecture. The presented methods of software emulation and paravirtualization of devices do not require hardware support and provide complete isolation of real devices from virtual ones at the hypervisor level, which allows for better consolidation of physical I/O. Direct assignment of the device to the guest provides better performance over the other methods, but requires hardware modifications in the I/O Memory Management Unit (IOMMU). Hardware support for I/O virtualization was implemented in the new generation of Elbrus microprocessors. The research results can help the users of the Elbrus platform virtualization to setup a detailed configuration of the virtual machine I/O to optimize each guest system for specific tasks.

This paper investigates the role of the domestic value chain in transmitting the economic impact of Covid-19 lockdown measures. By employing techniques of complex networks analysis and input–output traditional tools, the study identifies those sectors that are key in the complex structure of the Italian supply chain and provides different rankings of the most ‘systemically important’ industries involved in the Covid-19 lockdown. The results suggest that by stopping the production process of many key sectors, the lockdown has led to a drop in input and output that, in turn, has generated a lock of about 52% of total circulating value added, 30% of which has been locked within indirect value chains. Further, by adding sectoral physical proximity indexes to the scenarios analysis, the method developed here provides a tool to guide governments in designing safe and efficient reopening policies. Electronic supplementary material The online version of this article (10.1007/s40812-020-00164-9) contains supplementary material, which is available to authorized users.

This editorial summarizes the papers published in issue 15(2) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper combines a conditionally autoregressive process from the spatial statistics literature with a spatial Durbin error model from the spatial econometrics literature. The second paper feeds a multistage and multilevel data envelopment analysis with a microeconomic foundation. The third paper provides empirical evidence that Flegg's location quotient combined with a gravity model produces the most accurate interregional input-output multipliers. The fourth paper investigates the impact of inventor networks on the number of patents per capita in Brazil.

Introduction. Based on the brain science theory of “how people learn” and in order to modernize the methodology of psycholinguistic research, this research used documentary analysis and addressed the standpoint that the 4MAT Teaching and Learning Model can be subsumed into or superimposed on the Kirkpatrick Four-Level Evaluation Model, and vice versa. Meanwhile, the phase of language input and output is analyzed on the basis of the two Models above. In the end, some implications arise so as to provide reference for prospective researchers and practitioners in psycholinguistics. The aim of the study. The 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model are both widely applied, so a deliberate literature review to clarify the integration and the unity between them is conducted that expects to make some theoretical references inspired by the unity available to a wide range of linguistic teaching design and learning performance evaluation. The authors argue that the references interconnect teaching design and learning performance evaluation in light of language input and output and therefore help linguistic teachers/trainers with a whole and valid scheme at the very beginning of student learning, and this is the unity that also corresponds to Kirkpatrick & Kirkpatrick’s standpoint: “The end is the beginning”. Research methods. The study was conducted using the semantic differential scaling and the method of documentary analysis. Results. A combination of brain science theory and Fractal Information Theory has verified initially how the 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model subsume and superimpose in terms of the theoretical framework, i.e., the unity between a teaching theory and a learning performance evaluation theory. Such integration not only originates from the inherent unity verified by a thoughtful literature review but also receives theoretical support from interdisciplinary studies. Meanwhile, this integration is intertwined with language input and output in a psycholinguistic/neurolinguistic manner. Conclusions. A primary investigation using brain science theory and other theories to analyze the integration between the 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model shows the unity between both models. This investigation led to achieving the purpose of the study: modernizing the methodology of psycholinguistic research. With implementing the components/stages of language input and output as this article proposed, it is expected to be promising in extending and applying both models theoretically and practically in linguistics and other relevant areas in the future. As it comes to studies, it is recommended that the two Models be connected to analyze more teaching models and/or learning performance evaluation models for unity, inquire performance evaluation in collaborations with scenarios in practice, or even associate other disciplines under the implementation of Fractal Information Theory. A possible suggestion for psycholinguistic researchers is to design curricular and lessons based on the Unified Models (Figure 1 & 2) proposed in this study and evaluate instructional efficacy and student learning performance. Another potential research direction is to use each quadrant of the Unified Models and analyze related components in more specific language input and output phases: listening, reading, speaking, writing, and even smaller components in the four types of language skills. As it comes to practice, especially in psycholinguistics and/or other relevant disciplines, the key to apply the two target Models simultaneously depends on how to regulate respective quadrants/levels pro rata as well as the wholeness between them to simultaneously achieve “dynamic equilibrium” in the 4MAT Teaching and Learning Model and “The end is the beginning” in the Kirkpatrick Four-Level Evaluation Model.

There are increasing numbers of published articles in the field of input–output analysis and modelling that use the GTAP input–output database; particularly, in relation to the estimation of carbon, energy and water footprints and the analysis of global value chains and international trade. The policy relevance of those topics is also increasing, thus calling for consistently linking these databases with official statistics. Although, so far, GTAP has been using their own classification and reconciliation methods, this paper develops a new conversion method for the EU that guarantees that the EU-GTAP database respects the new statistical standards and Eurostat official statistics. We recommend for future updates, a shift of the current GTAP classification of industries to the new official standard classifications to which countries are progressively moving to. Otherwise, the lack of matching official data would jeopardize the usefulness of such database. This method can be extended to other similar input–output databases with different classification schemes from the original input data sources.

Virtualization is widely used in modern computer systems ranging from personal computers to cloud servers as it provides various heterogeneous platforms at low cost. However, due to its nested software structure in host and guest machines that are difficult to harmonize, it is challenging to manage resources efficiently in virtualized systems. In this article, we anatomize the overhead of virtualization associated with file system journaling and discover the excessively frequent commits that take place in virtualized systems. This is because the host triggers commit on every write request from all guest machines. This also generates unnecessary write traffic to storage. To remedy these problems, we propose the VM-separated commit, and implement it on QEMU-KVM and Ext4. Specifically, we devise a data structure that manages modified file blocks from each guest as a separate list and split the running transaction list into two sub-transactions based on this data structure upon a commit request from a guest. Measurement studies with Filebench and IOzone benchmarks show that the proposed policy improves the I/O throughput by 19.5% on average and up to 64.2% over existing systems. It also reduces the variability in performance.

The article presents a successful implementation of the milling process simulation at the Field-Programmable Gate Array (FPGA). By using FPGA, very rigorous Real-Time (RT) simulation requirements can be met. The response time of the FPGA simulations is significantly reduced, and the time synchronization is better than in a typical RT system implemented in software. The FPGA-based approach is characterized by enormous flexibility when it comes to input and output operations that can be implemented deterministically in RT. Complex simulation software has been implemented using the High Level Synthesis technique, which is a relatively easy and fast approach for FPGA programming without using complex Hardware Description Languages. The hardware functions are based on procedures written in high-level C programming language. The mathematical descriptions of simulations, results of computer simulations, Hardware-in-the-Loop Simulation experiments, and real experiments are presented. The approach presented in this paper can be used to simulate the dynamics of various mechatronic systems.

Data envelopment analysis (DEA) is a data-oriented procedure to evaluate the relative performances of a set of homogenous decision making units (DMUs) with multiple incommensurate inputs and outputs. Performance measurement using tools such as DEA needs to construct an empirical production technology set. In this analysis, DMUs are partitioned into two groups: efficient and inefficient. Inefficient DMUs are projected onto efficient frontier in such a way that their inputs are reduced and their outputs are increased. In this sense, finding a projection point with the shortest distance is important and it is a most frequently studied subject in the field of DEA. In this paper, a two-steps procedure is proposed to determine a projection point on the efficient frontier with closest distance. The reference point is constructed in such a way that it is located on the strong defining hyperplane of the DEA technology set. As we will show, the low computational efforts and the guarantee of determining an efficient projection point on the strong efficient frontier are the two important advantages of the proposed model.To show the applicability of the proposed approach, a real case on 28 international airlines is given.

In this paper, we analyze I/O log data monitored in the Windows operating system for improving the system performance. Especially, we focus on the I/O operations to the Windows registry. As a result, we identify redundant access patterns of the Windows applications. To find all the possible redundant patterns from the large-scale log data, we propose the redundancy detection algorithm. Then, we propose the two-level redundancy elimination method to remove unnecessary redundant operations. We also present an event-driven method that guarantees that the result of redundancy elimination is equivalent to that of the original program. Through experiments, we show that the proposed redundancy elimination method improves the performance of the original program having redundant access patterns by up to 90.25% for individual access patterns; by 8.93% ~ 26.21% when the multiple programs having combined access patterns are running concurrently.

To effectively compute convolutional layers, a complex design space must exist (e.g., the dataflow techniques associated with the layer parameters, loop transformation techniques, and hardware parameters). For efficient design space exploration (DSE) of various dataflow techniques, namely, the weight-stationary ( WS ), output-stationary ( OS ), row-stationary ( RS ), and no local reuse ( NLR ) techniques, the processing element (PE) structure and computational pattern of each dataflow technique are analyzed. Various performance metrics are calculated, namely, the throughput (in giga-operations per second, GOPS), computation-to-communication ratio ( CCR ), on-chip memory usage, and off-chip memory bandwidth, as closed-form expressions of the layer and hardware parameters. In addition, loop interchange and loop unrolling techniques with a double-buffer architecture are assumed. Many roofline model-based simulations are performed to explore relevant dataflow techniques for a wide variety of convolutional layers of typical neural networks. Through simulation, this paper provides insights into the trends in accelerator performance as the layer parameters change. For convolutional layers with large input and output feature map ( ifmap and ofmap ) widths and heights, the GOPS of the NLR dataflow technique tends to be higher than that of the techniques. For convolutional layers with low weight and ofmap widths and heights, the RS dataflow technique achieves optimal GOPS and on-chip memory usage. In the case of convolutional layers with small weight widths and heights, the GOPS of the WS dataflow technique tends to be high. In the case of convolutional layers with small ofmap widths and heights, the OS dataflow technique achieves optimal GOPS and on-chip memory usage.

Computational application demands do push the scaling of the number of cores, which themselves further increase the demand for more bandwidth. The use of larger rank widths and/or scaling the number of memory controllers (MCs) is a straightforward way to increase memory bandwidth. Connecting wide ranks and MCs via low-capacitance Through Silicon Vias (TSVs) favors high-bandwidth 3DStacking systems (e.g. Wide I/O). Given that voltage and frequency scaling (VFS) lower power utilization but the use of lower clock frequencies reduces bandwidth, this article proposes $Walter$ as a $W$ ide I/O technique that trades off sc $al$ ing of the number of memory con $t$ roll $e$ rs (MCs) versus clock $\text{f}r$ equency and voltage (VFS) to mitigate low bandwidth and improve energy-per-bit usage. Our findings show that $Walter$ ’s Wide I/O architectural benefits of using a larger number of MCs coupled with wider ranks when combined to VFS are promising: compared to the baseline for a 75% frequency/voltage reduction, MC scalability improved memory bandwidth by 2.4x and energy-per-bit reduced by 20% (most benchmarks for up to 16 MCs). $Walter$ ’s architectural replacement of ranks set at specification frequencies with ones set at lower frequencies allows temperature reduction thus likely allowing further rank stacking.

Differential algebra (DA) methods are currently being exploited for analyzing dynamic biosystem models for their structural identifiability (SI) properties. An early step in this approach entails finding an equivalent input–output (I/O) model. A recent approach for finding these equations, based on Grbner bases and imbedded in the app COMBOS is relatively slow and sometimes unsuccessful, even for moderate size models. In this paper, we propose a faster algorithm, using a Gaussian elimination approach for analyzing the subclass of linear dynamic biosystem models. We apply this methodology to find the simplest set of globally SI parameter combinations of these models and show that it works effectively for linear biosystem models of simple to moderate complexity.

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.

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).

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.

In this paper, the design, fabrication, and measurements of an S band multi harmonic tuned power amplifier in GaN technology is described. The amplifier has been designed by exploiting second and third harmonic tuning conditions at both input and output ports of the active device. The amplifier has been realized in a hybrid form, and characterized in terms of small and large signal performance. An operating bandwidth of 300 MHz around 3.55 GHz, with 42.3 dBm output power, 9.3 dB power gain and 53.5% power added efficiency PAE (60% drain efficiency) at 3.7 GHz are measured.

Well known to the machine learning community, the random feature model is a parametric approximation to kernel interpolation or regression methods. It is typically used to approximate functions mapping a finite-dimensional input space to the real line. In this paper, we instead propose a methodology for use of the random feature model as a data-driven surrogate for operators that map an input Banach space to an output Banach space. Although the methodology is quite general, we consider operators defined by partial differential equations (PDEs); here, the inputs and outputs are themselves functions, with the input parameters being functions required to specify the problem, such as initial data or coefficients, and the outputs being solutions of the problem. Upon discretization, the model inherits several desirable attributes from this infinite-dimensional viewpoint, including mesh-invariant approximation error with respect to the true PDE solution map and the capability to be trained at one mesh resolution and then deployed at different mesh resolutions. We view the random feature model as a non-intrusive data-driven emulator, provide a mathematical framework for its interpretation, and demonstrate its ability to efficiently and accurately approximate the nonlinear parameter-to-solution maps of two prototypical PDEs arising in physical science and engineering applications: viscous Burgers' equation and a variable coefficient elliptic equation., Comment: To appear in SIAM Journal on Scientific Computing; 32 pages, 9 figures

Natural and anthropogenic disasters frequently affect both the supply and demand side of an economy. A striking recent example is the Covid-19 pandemic which has created severe disruptions to economic output in most countries. These direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation. We find that even in this best case scenario network effects substantially amplify the initial shocks. To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on industries if they are not able to satisfy incoming demand. Our results show that economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions., Comment: 29 pages, 10 figures

Input-Output models and their extensions offer multiple prospects to explore the energy, water, food inter-linkages – Energy, Water, Food nexus (EWF-n). This paper takes India as a case study to examine a nexus-informed approach to policy making for redressing the EWF security (EWF-s) challenges. First, a Leontief demand-driven EWF-extended Input-Output model is developed. The Leontief's production functions (representing endogenous, fixed technical coefficients) of the developed model are then further modified using flexible functions to capture EWF-related technological and policy interventions. The resulting changes in the model (to include divergent input substitution possibilities) allow evaluation of policy scenarios with nexus or non-nexus considerations towards EWF-s. These scenarios assess outcomes across diverse domains (physical, social, economic, environmental) in short, medium, and long-run over the period 2015-2047. The results show that not only does the EWF nexus-oriented scenario produce major co-benefits demonstrated in terms of the most significant long-term improvement in EWF outcomes but that it also achieves considerably superior economic, social, and environmental outcomes. Synergies and trade-offs across various policy scenarios are also discussed. The insights obtained from the application of this approach, especially cross-sectorial (EWF), cross-domain, and temporal can provide promising takeaways for policymakers to adopt a robust and sustainable strategy for tackling the EWF-s challenges.

This paper presents the impulseest Python package, used for estimating the impulse response of a system relying solely on input and output data. This package can provide estimates in a non-parametric fashion either with regularization techniques. For the regularized estimates, impulseest function uses the Empirical Bayes method. On the other hand, the non-regularized case is solved through the least squares algorithm. This function is tested considering an experimental situation, several dynamic processes and also through Monte Carlo simulations. The obtained results are analyzed mainly in terms of the Mean Square Error (MSE), besides other quantities. Through those results, it is shown that the impulseest function with regularization using the proposed regularization kernels leads to low MSE for all tested cases.

This paper develops a model predictive controller (MPC) for constrained nonlinear MIMO systems subjected to bounded disturbances. A linear parameter varying (LPV) model assists MPC in dealing with nonlinear dynamics. In this study, the nonlinear process is represented by an LPV using past input–output information (LPV-IO). Two primary objectives of this study are to reduce online computational load compared with the existing literature of MPC with an LPV-IO model and to confirm the robustness of the controller in the presence of disturbance. For the first goal, a recurrent neural network (RNN) is employed to solve real-time optimization problems with lower online computation. Regarding robustness, a new control law is developed, which comprises a fixed control gain (K) and a free perturbation (C). The proposed method enjoys a shrunken conservatism owing to the finding of a larger possible terminal region and using free control moves. The strategy is examined in an alkylation of benzene process and displays outstanding performance in both setpoint tracking and disturbance rejection problems. Moreover, the superiority of RNN over three conventional optimization algorithms is underlined in terms of MSE, the average time for solving the optimization problem, and the value of the cost function.

The present paper has proposed the use of EIO models to describe and analyse logistics flows to support managers and policy makers in the definition of policies for their management and coordination. In particular, different approaches and models have been proposed. Two main perspectives have been used to analyse logistics flows, such as a spatial and operational one, pursuing different goals. In fact, geo-referring the production processes belonging to a supply chain, and their inputs and outputs, spatial-oriented analyses can be performed in order to deal with issues related to traffic congestion, pollutant emissions, transportation infrastructures, and work force availability. Therefore, on the basis of these analyses policies aimed at improving the transportation sustainability and reducing its negative impact on the environment can be identified. Differently, the adoption of an operational framework of analysis can permit to describe logistics flows involved in specific supply chains, or, more in detail, managed by specific actors, in order to analyse and improve logistics economic performance. For instance, solutions aimed at consolidating the flows and reducing the number of trips and the transportation costs can be achieved. Moreover, transportation has been modelled both as a process and as a primary input. The difference between these two approaches depends on the inclusion of inputs required and wastes produced by transportation. In fact, in the former all the transportation inputs, such as workforce and fuel, and its pollutant emissions are considered. Therefore, this approach can be useful for 3PL to evaluate the economic and environmental performance of their activities. On the contrary, the modelling of transportation as a primary input can be used to investigate its impact on the supply chains. In fact, in this case the model can be a suitable accounting and planning tool for actors representing the demand of logistics services, in order to analyse, for instance, how logistics affects their profit and operations. Transportation has been also modelled at an aggregate and disaggregate level. The aggregate level of analysis permits to investigate managerial-oriented issues, giving a holistic view of the transportation to evaluate its global role on the supply chains' efficiency and effectiveness. Differently, the disaggregate approach permits a more in-depth analysis, since it describes all the tracks covered by transportation means, thus allowing policy makers to propose actions for the logistics management according to a social and environmental perspective. Finally, EIO models have been used to represent logistics service markets, in order to account and analyse the demand and supply of transportation. This can be useful for both logistics providers and customers to organize markets, on the basis of different criteria (e.g. consolidation, specialization, geographical areas) and to improve their performance. The present study contributes to extend the existing framework on logistics management, providing a set of complete and complementary tools, based on the use of EIO models, able

Industrial symbiosis (IS) is a collaborative approach among firms involving physical exchanges of materials, energy, and wastes, which creates economic advantages for firms and environmental benefits for the society. In this paper, we adopt an ecosystem approach to conceptualize the network of firms involved in IS relationships (ISN), in terms of organisms (firms), functions (waste exchange), and services (environmental benefits), and provide new insight on how to assess and compute IS performance indicators. In particular, we designed five classes of indicators aimed at assessing 1) the impact of services provided by ISNs on the environment, 2) the performance of the ISN services, 3) how the single functions contribute to ISN services, 4) the performance of the ISN functions, and 5) how the single firms contribute to ISN functions. A numerical example is also discussed showing how to compute them and the information they provide. The proposed indicators are useful to develop proper strategies to increase the efficiency of the system in exploiting the IS synergies, to improve the symbiotic exchanges carried out in ISNs, and to identify firms contributing most to IS benefits. Hence, they may assist managers of ISNs and policymakers in decision-making aspects, an urgent need of the literature.

A series of Ln3+-metal centered complexes, Ln(TTA)3(DPPI) (Ln = La, 1, Ln = Eu, 2, Ln = Tb, 3, or Ln = Gd, 4) [(DPPI = N-(4-(1H-imidazo [4,5-f][1,10]phenanthrolin-2-yl)phenyl)-N-phenylbenzenamine) and (TTA = 2-Thenoyltrifluoroacetone)] have been synthesized and characterized. Among which, the Eu3+-complex shows efficient purity red luminescence in dimethylsulfoxide (DMSO) solution, with a Commission International De L’ Eclairage (CIE) coordinate at x = 0.638, y = 0.323 and ΦEuL = 38.9%. Interestingly, increasing the amounts of triethylamine (TEA) in the solution regulates the energy transfer between the ligand and the Eu3+-metal center, which further leads to the luminescence color changing from red to white, and then bluish-green depending on the different excitation wavelengths. Based on this, we have designed the IMPLICATION logic gate for TEA recognition by applying the amounts of TEA and the excitation wavelengths as the dual input signal, which makes this Eu3+-complex a promising candidate for TEA-sensing optical sensors.

This article presents socio-economic, energetic and CO2 emissions' impact by considering a large-scale integrated plant to treat Municipal Solid Waste (MSW) in the Metropolitan Region of São Paulo (MRSP); the most important Brazilian macro-region. As an alternative to landfills, this plant offers a source of renewable energy and a good assessment of benefits the circular economy provides. Based on the most recent interregional economic transactions data, the proposed Input-Output model has shown reductions in the local energy demand (-0.31%), carbon emissions (-3.40%). On the other hand, it has suggested gains in the GDP (+0.21%) and formal job positions (+0.08% or 10.6k) when recyclables are introduced massively in the economy., Waste-to-energy, Input-output, Recycling, Renewable energy, Municipal solid waste, Circular economy

—Quantum programs implement quantum algorithms solving complex computational problems. Testing such programs is challenging due to the inherent characteristics of Quantum Computing (QC), such as the probabilistic nature and computations in superposition. However, automated and systematic testing is needed to ensure the correct behavior of quantum programs. To this end, we present an approach called Quito (QUantum InpuT Output coverage) consisting of three coverage criteria defined on the inputs and outputs of a quantum program, together with their test generation strategies. Moreover, we define two types of test oracles, together with a procedure to determine the passing and failing of test suites with statistical analyses. To evaluate the cost-effectiveness of the three coverage criteria, we conducted experiments with five quantum programs. We used mutation analysis to determine the coverage criteria’ effectiveness and cost in terms of the number of test cases. Based on the results of mutation analysis, we also identified equivalent mutants for quantum programs. Index Terms—quantum programs, software testing, coverage criteria, mutation analysis