Parallel Chains Research Articles

МОДЕЛЮВАННЯ ПРОЦЕСІВ ТЕХНІЧНОГО КОНТРОЛЮ ЗЕРНОЗБИРАЛЬНИХ КОМБАЙНІВ МЕРЕЖЕЮ ПАРАЛЕЛЬНО ФУНКЦІЮВАЛЬНИХ ЛАНЦЮГІВ МАСОВОГО ОБСЛУГОВУВАННЯ

Purpose of the study. The aim of the work is to substantiate the modern organization of technical operation of combine harvesters, which is carried out by a network of parallel-functioning queuing chains. The practical result will be the formation of a model of technical control of the requirements of individual systems, which will initially be addressed using the terminology adopted by the theory of queuing, and recommendations will be given on the specific use of the solutions. Research methods. The methodological basis of the work is the generalization and analysis of known scientific results regarding the modern organization of technical operation of combine harvesters, carried out by a network of parallel queuing chains, and the use of requirements division algorithm and queuing theory. Analytical method and comparative analysis were used to form a scientific problem, determine the purpose and set research objectives. When creating empirical models, the main provisions of the theory of queuing, systems of stationary service devices, block diagrams of the failure distribution algorithm are used. The results of the study. The article considers the data, which is considered to be the modern organization of technical operation control of combine harvesters by network, which consists of a finite number of parallel operating queuing chains with serial numbers . In the network at random moments of time from an external source receives service requirements. The issues of choosing a chain for service requirements were considered. Since each chain is designed to restore a particular model of machine by performing one or another type of technical control of the combine harvester, the choice of chain was determined by the parameters ω_i and ξ_i received applications. It was taken into account that the total number of queuing chains Β, as a rule, is less than the number of types of technical control of the number of models of combine harvesters in the park ε, because in the same chain can perform technical control of several types and models of combine harvesters. For each specific case of organization of the network of technical operation in accordance with the accepted specialization of individual chains, a matrix of chain selection for servicing the application according to its parameters and can be compiled. A similar matrix, in which each pair of possible values corresponds to a specific chain number ε, can be easily constructed for any specific case of a given organization and specialization of the network of technical operation of combine harvesters. Conclusions. As a result of the research, new scientifically sound technical and technological solutions and mathematical models were obtained, which are aimed at improving the efficiency of production processes of restoring the efficiency of combine harvesters in agricultural enterprises Modeling of technical control of requirements of separate systems of the combine harvester will be initially solved in a form with use of the terminology accepted by the theory of queuing, and recommendations concerning concrete use of the received decisions will be given. A model of a closed queuing system is obtained, which can be used to describe the operation of a stationary workshop, specialized shop, plant, point, dead-end maintenance area or other set of concentrated stationary service devices with an arbitrary law of distribution of service time. Key words: closed queuing system, requirement, combine harvester, model, efficiency.

The Microscopic Diamond Anvil Cell: Stabilization of Superhard, Superconducting Carbon Allotropes at Ambient Pressure.

A metallic, covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an sp3 carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene chains. The distance between these sp2 carbon atoms renders the phase metallic, and yields two well-nested nearly parallel bands that cross the Fermi level. Calculations show this phase is a conventional superconductor, with the motions of the sp2 carbons being key contributors to the electron-phonon coupling. The sp3 carbon atoms impart superior mechanical properties, with a predicted Vickers hardness of 48 GPa. This phase, metastable at ambient conditions, could be made by on-surface polymerization of graphene nanoribbons, followed by pressurization of the resulting 2D sheets. A family of multifunctional materials with tunable superconducting and mechanical properties could be derived from this phase by varying the sp2 versus sp3 carbon content, and by doping.

Free2Shard

In this paper, we formulate and study a new, but basic, distributed resource allocation problem arising in scaling blockchain performance. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally requires the solution to be resilient to adversarial behavior from a fraction of nodes. Scaling blockchain performance is a basic research topic; a plethora of solutions (under the umbrella of sharding) have been proposed in recent years. Although the various sharding solutions share a common thread (they cryptographically stitch together multiple parallel chains), architectural differences lead to differing resource allocation problems. In this paper we make three main contributions: (a) we categorize the different sharding proposals under a common architectural framework, allowing for the emergence of a new, uniformly improved, uni-consensus sharding architecture. (b) We formulate and exactly solve a core resource allocation problem in the uni-consensus sharding architecture -- our solution, Free2shard, is adversary-resistant and achieves optimal throughput. The key technical contribution is a mathematical connection to the classical work of Blackwell approachability in dynamic game theory. (c) We implement the sharding architecture atop a full-stack blockchain in 3000 lines of code in Rust -- we achieve a throughput of more than 250,000 transactions per second with 6 shards, a vast improvement over state-of-the-art.

Variation Analysis Considering the Partial Parallel Connection in Aero-Engine Rotor Assembly

The rotation precision of rotors determines the efficiency and quality of the overall aero-engine, as well as its long-term and reliable operation ability. As the terminal link of aero-engine manufacturing, the assembly is the last guarantee of precision control. Rotor assembly relates to the accurate expression of the connection form and design optimization of the assembly scheme. The existing variation model cannot adequately handle the partial parallel chain problem, ignoring the bayonet circular connector between the rotor parts, and it is still deficient in multistage gyration error control. In this paper, the partial parallel connection and multistage revolving characteristics of rotors were discussed, and a novel modeling and optimizing method for a partial parallel dimension chain was proposed. On the one hand, the variation expression of the connection features for the revolving components considering the partial parallel structure was researched. Contact point-based torsors were represented, and a system for locating points was regarded as an assembly to describe the partial parallel chain. On the other hand, the variation propagation modeling and control for the stacking of the multistage revolving components was researched. A revolution joint was introduced in the unified Jacobian–Torsor model, and a novel assembly technique for concentricity control was proposed. Therefore, a unified variation analysis and control method for rotor assembly has been developed. Experimental results show that through this method, the final concentricity variation is 0.0539 mm, far less than the 0.1595 mm of the traditional model, and is closer to the true value range of 0.030–0.040 mm. Moreover, the optimum installed angles can be calculated as 3.153 rad, 6.025 rad, and 2.590 rad, to obtain the highest concentricity of 0.040 mm, which has strong practical guiding significance.

Ab-Initio Investigations on Hydrogen Dissociation and Cross-Linking of Hydrocarbon Chains of Self-Assembled Monolayers of Alkanes

First-principles calculations were carried out to study the structural and electronic properties of hydrocarbon chains of self-assembled monolayers with hydrogen dissociation. It was found that the incoming hydrogen could lead to the formation of H2 molecules by stripping the nearby hydrogen atoms in the chains and thereby leave the neighboring carbon atoms to be free radicals. Two parallel hydrocarbon chains with dangling bonds can form a direct C-C bond, i.e., cross-linking happens between the two chains, which is ascribed to a charge accumulation in the cross-linking region. The polymerization of short molecules into long hydrocarbon chains through a different cross-linking mode is also discussed.

VNF Embedding and Assignment for Network Function Parallelism

The emergence of Network Function Virtualization (NFV) and Service Function Chaining (SFC) together enable flexible and agile network management and traffic engineering. Recently, Network Function Parallelism (NFP) has been proposed to break the need for sequential services and, hence, significantly reduce the latency of SFC. While some studies have investigated how to identify parallel paths to enhance the efficiency of parallelism, less effort has been paid to efficient network function embedding with consideration of parallelism opportunities. Hence, this work aims at solving the instance deployment problem so as to prevent parallel functions from waiting for each other before continuing to the next stage. As it is extremely difficult to optimize VNF (Virtual Network Functions) embedding for uncertain parallelism opportunities, we propose a practical embedding scoring mechanism to enhance the likelihood of low-cost function parallelism. Our scoring design tends to cluster independent functions for efficient parallelism while ensuring load balancing. After instance deployment, we then assign function instances to parallel chains so as to minimize their end-to-end latency while balancing the workload of instances. Our evaluation results show that the proposed scoringbased embedding scheme ensures homogeneous delays of parallel functions and, hence, reduces the end-to-end latency for NFP by up to 22% as compared to the embedding algorithm without considering parallelism opportunities.

Low-temperature solid-state synthesis of interlayer engineered VS4 for high-capacity and ultrafast sodium-ion storage

The quasi-layered VS4 is the stacked linear chain structure in which parallel chains are bonded by Van der Waals forces. Considering its wide interlayer and high sulfur content, VS4 is supposed to be a potential high-performance anode material for Na-ion storage. Herein, a novel and efficient low-temperature solid-state method is developed to synthesize interlayer engineered VS4 (IE-VS4). The intercalation of [NCN]2- anion expands the interlayer of VS4 to accelerate the Na-ion diffusion. Furthermore, The intercalated anion also reduces the band gap of VS4 and enhances the electronic conductivity. Thanks to these merits, the IE-VS4 enables standout Na-ion storage performance in terms of ultrafast rate capability (500 mAh g−1 at 20 A g−1), high capacity and superior cycle stability (550 mAh g−1 at 5 A g−1 after 2500 cycles). The developed large-scale synthesis method and the interlayer engineering strategy can synergistically promote the practical application of VS4 in electrochemical energy storage.

Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratios of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843–0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ∼9× faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90×–180× faster per chain than BART on that CPU.

GENERALIZED MATHEMATICAL MODEL OF MONEY LAUNDERING AND TERRORISM FINANCING RISK TYPOLOGY IN BIG DATA OF SOCIO-ECONOMIC SYSTEMS

The big data of socio-economic systems is investigated in order to synthesize a generalized model of the typology of the risk of money laundering. The analysis of actual requirements to the typology model was carried out. A model for input, output and classification of objects, sets, categories in big data represented by topos has been developed. It is shown that classified objects can be entered into an artificial intelligence database and used to search and analyze typologies of money laundering risks. The concept is introduced and the category-theoretic formula of the event of the risk of money laundering is synthesized. In addition, a retrospective event was singled out and its theoretical justification was carried out. A method for fragmentation of chains of Markov occurrences is proposed and analyzed. Within the framework of the category-theoretic approach, chains are represented by category compositions. A new scheme developed instead of the well-known scheme of the normal Markov algorithm is analyzed. In the scheme, locally final occurrences were discovered and theoretically substantiated, which were not taken into account and were not used by Markov. Locally final occurrences in the scheme of the Markov algorithm are found in parallel chains of occurrences and voids. It has been established that locally final substitutions play an important role in the search and analysis of typologies of money laundering risks in big data of socio-economic systems. It has been determined that typologies of money laundering risks can be represented as categories of sets and n-categories. A formula for an incomplete risk typology has been developed. It is concluded that the synthesized model of the typology of money laundering risks provides for the detection and analysis of local typologies of individual risks.

Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models

BackgroundMusculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem.MethodsWe generated a reference elbow flexion–extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density.ResultsWe evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix.ConclusionsWhile the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

The kinetics of cathodic hydrogen evolution on titanium disilicide in a sulphuric acid solution

The kinetics and mechanism of hydrogen evolution reaction on the TiSi2 electrode in x M H2SO4 + (0.5 – x) M Na2SO4 (x = 0.5; 0.35; 0.20; 0.05) solutions were studied using the methods of polarisation and impedance measurements. The cathodic polarisation curves of TiSi2 were characterised by a Tafel section with a slope of 0.116–0.120 V at E ranging from –0.30 to –0.48 V (SHE). The value of hydrogen evolution overpotential at i = 1 A/cm2 for TiSi2 was 0.90–0.96 V. The kinetic parameters of the hydrogen evolution reaction on silicide were close to the theoretical values for the slow stage of charge transfer. Based on the measurements of the differential capacitance of the TiSi2 electrode (at f = 10 kHz), depending on the cathodic polarisation and acidity of the electrolyte, it was concluded that a thin dielectric film of silicon dioxide (Si + 2H2O →SiO2 + 4H+ + 4e–) was present on the surface of the silicide. The film was not reduced with low cathodic polarisations. The impedance spectra of the TiSi2 electrode at the potentials of the Tafel region were capacitive semicircles with a misplaced centre. The impedance spectra were described by an equivalent electrical circuit, the Faraday impedance of which consisted of a series-connected charge transfer resistance R1 and a parallel R2C2 chain corresponding to the adsorption of atomic hydrogen on the electrode surface. The impedance of the double layer capacitance was modelled using the constant phase element CPE1. The χ2 criterion for the circuit was (1.3-3.7)·10-4 (when using data modulus weighting), the sum of square deviations was (1.5–4.1)·10–2, and the error in the determination of the values of circuit parameters did not exceed 10 %.The experimental values of the slopes of lg R1,E-, lg R2,E-, and lg C2,E-dependences were close to the theoretical values of the slopes for the discharge-electrochemical desorption mechanism, in which both stages were irreversible and the transfer coefficients of the stages were not equal, when the Langmuir isotherm for adsorbed atomic hydrogen was fulfilled. The reaction of absorption of hydrogen with the kinetic control proceeded at the same time as the hydrogen evolution reaction.

A Bayesian Framework for Addressing the Uncertainty in Production Forecasts of Tight-Oil Reservoirs Using a Physics-Based Two-Phase Flow Model

Summary Extrapolation of history matched single-phase flow solutions is a common practice to forecast production from tight-oil reservoirs. Nonetheless, this approach (a) omits multiphase flow effects that exist below bubblepoint conditions and (b) has not included the quantification of the uncertainty of the estimated ultimate recovery (EUR). This work combines a new two-phase (oil and gas) flow solution within a Bayesian framework to address the uncertainty in the EUR. We illustrate the application of the procedure to tight-oil wells of west Texas. First, we combine the oil and the gas flow equations into a single dimensionless two-phase flow equation. The solution is a dimensionless flow rate model that can be easily scaled using two parameters: hydrocarbon pore volume and characteristic time. Second, this study generates the probabilistic production forecasts using a Bayesian approach in which the parameters of the model are treated as random variables. We construct parallel Markov chains of the parameters using an adaptative Metropolis-Hastings (M-H) Markov chain Monte Carlo (MCMC) for this purpose. Third, we evaluate the robustness of our inferences using posterior predictive checks (PPCs). Finally, we quantify the uncertainty in the EUR percentiles using the bootstrap method. The results of this research are as follows. First, this work shows that EUR estimates based on single-phase flow solutions will consistently underestimate the ultimate oil recovery factors in solution-gas drives where the reservoir pressure is less than the bubblepoint. The degree of underestimation will depend on the reservoir and flowing conditions as well as the fluid properties. Second, the application of parallel Markov chains using an adaptative M-H MCMC scheme that addresses the correlation between the model’s parameters solves the issues of mixing and autocorrelation of Markov chains and, thus, it speeds up speeding up the convergence of the Markov chains. Third, we generate replicated data from our posterior distributions to assess the robustness of our inferences (PPCs). Finally, we use hindcasting to calibrate and strengthen our inferences. To our knowledge, all these approaches are novel in EUR forecasting. Using a Bayesian framework with a low-dimensional (two-parameter) physics-based model provides a fast and reliable technique to quantify the uncertainty in production forecasts. In addition, the use of parallel chains with an adaptative M-H MCMC accelerates the rate of convergence and increases the robustness of the method.

A novel trans-dimensional Bayesian inversion strategy for airborne time-domain electromagnetic data

The trans-dimensional Bayesian inversion method based on statistical theory regards the inversion parameters as random variable, it not only provides a reasonable inverse model based on the probability, but also offers the probability distribution and the uncertainty information of the inverse model parameters. However, for the conventional trans-dimensional Bayesian inversion, the model sampling efficiency and inversion convergence rate are influenced by several factors, and it requires sampling over a big model space, those limit the application of the trans-dimensional Bayesian inversion for airborne time-domain electromagnetic data. In this paper, we present a novel trans-dimensional Bayesian inversion strategy for airborne time-domain electromagnetic data, which uses a combining sampling update method to implement the model sampling. This method uses the block-wise updating method to run only one Markov chain with the initial state constructed by conductivity-depth imaging during the burn-in period, and adopts the component-wise updating method with an adaptive sampling step size to perform multiple Markov chains in parallel after the burn-in period ends. The effectiveness of the novel Bayesian inversion strategy was validated by both synthetic data and survey data. The experimental results showed that this inversion strategy can not only obtain better inversion results, but also shorten the burn-in period and reduce the total sampling times of the Markov chain.

Free2Shard

In this paper, we study a canonical distributed resource allocation problem arising in blockchains. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally requires the solution to be resilient to adversarial behavior from a fraction of nodes. Scaling blockchain performance is a basic research topic; a plethora of solutions (under the umbrella of sharding ) have been proposed in recent years. Although the various sharding solutions share a common thread (they cryptographically stitch together multiple parallel chains), architectural differences lead to differing resource allocation problems. In this paper we make three main contributions: (a) we categorize the different sharding proposals under a common architectural framework, allowing for the emergence of a new, uniformly improved, uni-consensus sharding architecture. (b) We formulate and exactly solve a core resource allocation problem in the uni-consensus sharding architecture -- our solution, Free2shard, is adversary-resistant and achieves optimal throughput. The key technical contribution is a mathematical connection to the classical work of Blackwell approachability in dynamic game theory. (c) We implement the sharding architecture atop a full-stack blockchain in 3000 lines of code in Rust -- we achieve a throughput of more than 250,000 transactions per second with 6 shards, a vast improvement over state-of-the-art.

On the methods of analytical mechanics for mathematical modeling of the dynamics of non-free systems and some variants of their application to the dynamics of parallel manipulators

The level of development of actuator technology and the element base and software of control loops of modern automatic devices makes it possible to use in technical practice complex control algorithms based on the results of mathematical control theory, including with incomplete information about the state. The effectiveness of such algorithms is determined by the presence of an adequate mathematical model of the device under study. For such a widespread class of automatic devices as manipulators with parallel kinematics, the problem of developing methods for creating mathematical models that allow taking into account nonlinear effects, despite the efforts of numerous researchers, remains relevant. To a large extent, the complexity of modeling the dynamics of this class of automatic devices is associated with the presence of parallel kinematic chains, which leads to the need to apply the results of analytical mechanics of non-free systems. In this section of theoretical mechanics, several rigorous methods have been developed, as a result of which mathematical models of different dimensions are obtained with different levels of complexity of the algorithms for their derivation and study. This leads to the need for a critical analysis of both the methods themselves and their possible application in technical practice. In this paper, the main algorithms for obtaining mathematical models of the dynamics of systems with geometric constraints are presented in detail and some comparative analysis of their application to modeling the dynamics of manipulators with parallel kinematics is carried out.

Synthesis, Structural Characterization, and Biological Activities of Organically Templated Cobalt Phosphite (H2DAB)[Co(H2PO3)4]·2H2O

A novel hybrid cobalt phosphite, (H2DAB)[Co(H2PO3)4] 2H2O, was synthesized by using a slow evaporation method in the presence of cobalt nitrate, phosphorous acid, and 1,4-diaminobutane (DAB = 1,4-diaminobutane) as a structure-directing agent. Single-crystal X-ray diffraction analysis showed that the compound crystallizes in the triclinic system (space group P-1(n.2)) with the following unit cell parameters (Å, °) a = 5.4814 (3), b = 7.5515 (4), c = 10.8548 (6), α = 88.001 (4), β = 88.707 (5), γ = 85.126 (5), and V = 447.33 (4) Å3. The crystal structure is built up from corner-sharing [CoO6] octahedra, forming chains parallel to [001], which are interconnected by H2PO3− pseudo-tetrahedral units. The diprotonated cations, residing between the parallel chains, interact with the inorganic moiety via hydrogen bonds, thus leading to the formation of the 3D crystal structure. The Fourier transform infrared spectrum showed characteristic bands corresponding to the phosphite group and the organic amine. The thermal behavior of the compound mainly consisted of the loss of its organic moiety and the water molecules. The biological tests exhibited significant activity against Candida albicans and Escherichia coli strains at different concentrations, while less inhibitory activity was pronounced against Staphylococcus epidermidis and Saccharomyces cerevisiae, and in the case of multi-cellular organisms, no activity against the nematode model Steinernema feltiae was detected.

Asynchronous MMC PSA inversion of transient electromagnetic data

This paper focuses on low computational efficiency in simulated annealing (SA) inversion of Transient Electromagnetic (TEM) data. Asynchronous multiple Markov chains (MMC) parallel strategy is a very promising SA acceleration method, which can be accelerated almost linearly. However, this method also reduces the accuracy of the solution. To overcome this problem, we added the solution set strategy to the asynchronous MMC parallel simulated annealing (PSA) algorithm for the first time. In this new algorithm, each thread independently searches for direction and exchanges data with the solution set in the shared memory. We used both the synthetic and field data to test the new algorithm. The synthetic data tests showed that the MMC PSA results are better than those of the original MMC PSA. We analyzed the efficiency of the new algorithm. Compared with the sequential VFSA, the maximum speedup of the new algorithm is approximately 10 times. The field data test also showed that the improved MMC PSA algorithm has good practicability. These tests demonstrate that the improved algorithm is effective, showing that its convergence speed is greatly improved without reducing the accuracy.

Data sharing in food supply chains and the feasibility of cross-chain data platforms for added value

Data analysis and data sharing is becoming increasingly important for creating value in the food supply chain. Readily available data is often already shared between supply chain parties, but to achieve additional benefits these parties will have to invest in additional data capturing. Especially product identity-claims, such as sustainable, equitable, or traceable from farm to fork, require transparency of all parties participating in a supply chain and necessitate investments that unlock the required data. This poses problems in supply chains where power is distributed. Typically, supply chains benefitting from the opportunities of data sharing have a higher degree of vertical integration, in which the power of a purchasing party forces suppliers to invest in data capture and exchange. Such investments are not always a possibility: food supply chains can have a dynamic set of participants, meaning that part of the supplier-purchaser relations is discontinuous or even ad-hoc, making investments in chain-specific data infrastructure unattractive for the chain parties involved. Cross-chain data platforms could contribute to solve such problems, although such initiatives have also generally been pushed by dominant supply chain parties. This limits the scope of application of such platforms. In this research, we discuss the feasibility of cross-chain platforms based on case studies with seafood, fresh fruit, fruit juice and processed potato supply chains in the Netherlands. Five dimensions appear to determine the fit with a cross-chain data platform: (i) shared interest in supply chain performance and data sharing, (ii) parallel chains and dynamic chain composition, (iii) alignment in incentives for data collection, (iv) degree of digitisation, and (v) revenue model for data sharing and collaboration.

Connections Between System of System Sustainability and Resilience in an Electric Motor Manufacturing Supply Chain

Systems of Systems combine constituent systems (such as financial, transportation, energy, food, and water) to improve performance. System of Systems employment, however, often requires additional material and infrastructure investment. Due to their widespread use, it is essential that Systems of Systems are sustainable. One approach to increasing Systems of Systems sustainability is to increase its resilience (e.g. a less-resilient System of Systems would require additional resources to recover from a fault). The need for increased Systems of Systems resilience inspired our research question: How does SoS resilience change environmental impact during post-fault operation? To examine this research question, a model of an electric motor supply chain is examined. The supply chain architecture is divided into two parallel supply chains servicing four distribution centers. If supply to a distribution center is disrupted, the non-impacted supply chain can provide motors, but at a changed use-phase impact due to new shipping routes (e.g. further geographic distance). To answer our research question, each of the 14 constituents were disrupted one at a time, supply rerouting occurred as necessary, and the change in operational impact is reported (additional km of supply chain travel per week. This study provides key evidence that even if System of System infrastructure repair could occur without impact, changes in operational impact is an important factor for decision makers. Additionally, this paper provides case study evidence supporting the claim that improved resilience can improve System of Systems sustainability.

A Solution of Partial Parallel Chains Caused by Multi-Datum Geometric Tolerances for the Unified Jacobian-Torsor Model

A Solution of Partial Parallel Chains Caused by Multi-Datum Geometric Tolerances for the Unified Jacobian-Torsor Model