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In response to the escalating need for sustainable manufacturing practices amid fluctuating energy prices, this study introduces a novel dispatching rule that integrates energy price and workload considerations with Material Requirement Planning (MRP) to optimize production logistics and energy costs. The dispatching rule effectively adjusts machine operational states, i.e. turn the machine on or off, based on current energy prices and workload. By developing a stochastic multi-item multi-stage job shop simulation model, this research evaluates the performance of the dispatching rule through a comprehensive full-factorial simulation. Findings indicate a significant enhancement in shop floor decision-making through reduced overall costs. Moreover, the analysis of the Pareto front reveals trade-offs between minimizing energy and production logistics costs, aiding decision-makers in selecting optimal configurations.

Selecting the appropriate production planning and control systems (PPCS) presents a significant challenge for many companies, as their performance, i.e., overall costs, depends on the production system environment. Key environmental characteristics include the system's structure, i.e., flow shop, hybrid shop, or job shop, and the planned shop load. Besides selecting a suitable PPCS, its parameterization significantly influences the performance. This publication investigates the performance and the optimal parametrization of Material Requirement Planning (MRP), Reorder Point System (RPS), and Constant Work In Progress (ConWIP) at different stochastic multi-item multi-stage production system environments by conducting a comprehensive full factorial simulation study. The results indicate that MRP and ConWIP generally outperform RPS in all observed environments. Moreover, when comparing MRP with ConWIP, the performance clearly varies depending on the specific production system environment.

In many supply chains, the current efforts at digitalization have led to improved information exchanges between manufacturers and their customers. Specifically, demand forecasts are often provided by the customers and regularly updated as the related customer information improves. In this paper, we investigate the influence of forecast updates on the production planning method of Material Requirements Planning (MRP). A simulation study was carried out to assess how updates in information affect the setting of planning parameters in a rolling horizon MRP planned production system. An intuitive result is that information updates lead to disturbances in the production orders for the MRP standard, and, therefore, an extension for MRP to mitigate these effects is developed. A large numerical simulation experiment shows that the MRP safety stock exploitation heuristic, that has been developed, leads to significantly improved results as far as inventory and backorder costs are concerned. An interesting result is that the fixed-order-quantity lotsizing policy performs - in most instances - better than the fixed-order-period lotsizing policy, when periodic forecast updates occur. In addition, the simulation study shows that underestimating demand is marginally more costly than overestimating it, based on the comparative analysis of all instances. Furthermore, the results indicate that the MRP safety stock exploitation heuristic can mitigate the negative effects of biased forecasts.

In manufacturing, a bottleneck workstation frequently emerges, complicating production planning and escalating costs. To address this, Drum-Buffer-Rope (DBR) is a widely recognized production planning and control method that focuses on centralizing the bottleneck workstation, thereby improving production system performance. Although DBR is primarily focused on cre-ating a bottleneck schedule, the selection of planning parameters is crucial, as they significantly influence the scheduling process. Conducting a compre-hensive full factorial enumeration to identify the ideal planning parameters requires substantial computational effort. Simulation Budget Management (SBM) offers an effective concept to reduce this effort by skipping less promising parameter combinations. This publication introduces a method for integrating SBM into multi-stage multi-item DBR planned and controlled production system with limited capacity, aimed at determining the optimal planning parameters. Furthermore, we conduct a simulation study to analyze the effects of different production system environments, i.e., varying levels of shop load and process uncertainty, on both the performance and parame-terization of DBR and the efficacy of SBM. Our results show significant re-duction in simulation budget for identifying optimal planning parameters compared to traditional full factorial enumeration.

Cell biologists study in parallel the morphology of cells with the regulation mechanisms that modify this morphology. Such studies are complicated by the inherent heterogeneity present in the cell population. It remains difficult to define the morphology of a cell with parameters that can quantify this heterogeneity, leaving the cell biologist to rely on manual inspection of cell images. We propose an alternative to this manual inspection that is based on topological data analysis. We characterise the shape of a cell by its contour and nucleus. We build a filtering of the edges defining the contour using a radial distance function initiated from the nucleus. This filtering is then used to construct a persistence diagram that serves as a signature of the cell shape. Two cells can then be compared by computing the Wasserstein distance between their persistence diagrams. Given a cell population, we then compute a distance matrix that includes all pairwise distances between its members. We analyse this distance matrix using hierarchical clustering with different linkage schemes and define a purity score that quantifies consistency between those different schemes, which can then be used to assess homogeneity within the cell population. We illustrate and validate our approach to identify sub-populations in human mesenchymal stem cell populations., Comment: 21 pages, 7 Figures

Silicon-based quantum emitters are candidates for large-scale qubit integration due to their single-photon emission properties and potential for spin-photon interfaces with long spin coherence times. Here, we demonstrate local writing and erasing of selected light-emitting defects using fs laser pulses in combination with hydrogen-based defect activation and passivation. By selecting forming gas (N2/H2) during thermal annealing of carbon-implanted silicon, we form Ci centers while passivating the more common G-centers. The Ci center is a telecom S-band emitter with very promising spin properties that consists of a single interstitial carbon atom in the silicon lattice. Density functional theory calculations show that the Ci center brightness is enhanced by several orders of magnitude in the presence of hydrogen. Fs-laser pulses locally affect the passivation or activation of quantum emitters with hydrogen and enable programmable quantum emitter formation in a qubit-by-design paradigm.

In this paper, we consider a simple class of stratified spaces -- 2-complexes. We present an algorithm that learns the abstract structure of an embedded 2-complex from a point cloud sampled from it. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and prove the correctness of the identified abstract structure under assumptions on the embedding.

Magnetization switching in ferromagnets has so far been limited to the current-induced spin-orbit-torque effects. Recent observation of helicity-independent all-optical magnetization switching in exchange-coupled ferromagnet ferrimagnet heterostructures expanded the range and applicability of such ultrafast heat-driven magnetization switching. Here we report the element-resolved switching dynamics of such an exchange-coupled system, using a modified microscopic three-temperature model. We have studied the effect of i) the Curie temperature of the ferromagnet, ii) ferrimagnet composition, iii) the long-range RKKY exchange-coupling strength, and iv) the absorbed optical energy on the element-specific time-resolved magnetization dynamics. The phase-space of magnetization illustrates how the RKKY coupling strength and the absorbed optical energy influence the switching time. Our analysis demonstrates that the threshold switching energy depends on the composition of the ferrimagnet and the switching time depends on the Curie temperature of the ferromagnet as well as RKKY coupling strength. This simulation anticipates new insights into developing faster and more energy-efficient spintronics devices.

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal by an ultrashort laser pulse is extremely important. We demonstrate optical switching of Tb/Comultilayer-based nanoscale MTJs by combining optical writing and electrical read-out methods. A 90 fs-long laser pulse switches the magnetization of the storage layer (SL). The change in magnetoresistance between the SL and a reference layer (RL) is probed electrically across the tunnel barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The switching dynamics in a continuous film is probed with the magneto-optical Kerr effect technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices., Comment: total pages 22, Total figure 8

Electrically controllable non-volatile magnetic memories show great potential for the replacement of semiconductor-based technologies. Recently there has been strong interest in spin-orbit torque (SOT) induced magnetization reversal due to the device's increased lifetime and speed of operation. However, recent SOT switching studies reveal an incubation delay in the ~ns range due to stochasticity in the nucleation of a magnetic domain during reversal. Here, we experimentally demonstrate ultrafast SOT-induced magnetization switching dynamics of a ferromagnet with no incubation delay by avoiding the nucleation process and driving the magnetization coherently. We employ an ultrafast photo-conducting switch and a co-planar strip line to generate and guide ~ps current pulses into the heavy metal/ferromagnet layer stack and induce ultrafast SOT. We use magneto-optical probing to investigate the magnetization switching dynamics with sub-picosecond time resolution. Depending on the relative current pulse and in-plane magnetic field polarities, we observe either an ultrafast demagnetization and subsequent recovery along with a SOT-induced precessional oscillation, or ultrafast SOT switching. The magnetization zero-crossing occurs in ~70 ps, which is approximately an order of magnitude faster than previous studies. Complete switching needs ~250 ps and is limited by the heat diffusion to the substrate. We use a macro-magnetic simulation coupled with an ultrafast heating model to analyze the effect of ultrafast thermal anisotropy torque and current-induced torque in the observed dynamics. Good agreement between our experimental results and the macro-spin model shows that the switching dynamics are coherent and present no noticeable incubation delay. Our work suggests a potential pathway toward dramatically increasing the writing speed of SOT magnetic random-access memory devices., Comment: 17 pages, 4 figures, Supplemental Material

In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them find the information they are looking for. In recent years, a research field has emerged known as recommender systems. Recommenders have become important as they have many real-life applications. This paper reviews the different techniques and developments of recommender systems in e-commerce, e-tourism, e-resources, e-government, e-learning, and e-library. By analyzing recent work on this topic, we will be able to provide a detailed overview of current developments and identify existing difficulties in recommendation systems. The final results give practitioners and researchers the necessary guidance and insights into the recommendation system and its application., Comment: 22 pages, 13 figures

In this note, the importance of spectral properties of viscous flux discretization in solving compressible Navier-Stokes equations for turbulent flow simulations is discussed. We studied six different methods, divided into two different classes, with poor and better representation of spectral properties at high wavenumbers. Both theoretical and numerical results have revealed that the method with better properties at high wavenumbers, denoted as $\alpha$-damping type discretization, produced superior solutions compared to the other class of methods. The proposed compact $\alpha$-damping method converged towards the direct numerical simulation (DNS) solution at lower grid resolution compared with the other class of methods and is, therefore, a better candidate for high fidelity large-eddy simulations (LES) and DNS studies., Comment: 14

This paper discusses the importance of high-frequency damping in high-order conservative finite-difference schemes for viscous terms in the Navier-Stokes equations. Investigating nonlinear instability encountered in a high-resolution viscous shock-tube simulation, we have discovered that a modification to the viscous scheme rather than the inviscid scheme resolves a problem with spurious oscillations around shocks. The modification introduces a term responsible for high-frequency damping that is missing in a conservative high-order viscous scheme. The importance of damping has been known for schemes designed for unstructured grids. However, it has not been recognized well in very high-order difference schemes, especially in conservative difference schemes. Here, we discuss how it is easily missed in a conservative scheme and how to improve such schemes by a suitably designed damping term.

The electronic, optical and magnetic properties of graphene nanoribbons (GNRs) can be engineered by controlling their edge structure and width with atomic precision through bottom-up fabrication based on molecular precursors. This approach offers a unique platform for all-carbon electronic devices but requires careful optimization of the growth conditions to match structural requirements for successful device integration, with GNR length being the most critical parameter. In this work, we study the growth, characterization, and device integration of 5-atom wide armchair GNRs (5-AGNRs), which are expected to have an optimal band gap as active material in switching devices. 5-AGNRs are obtained via on-surface synthesis under ultra-high vacuum conditions from Br- and I-substituted precursors. We show that the use of I-substituted precursors and the optimization of the initial precursor coverage quintupled the average 5-AGNR length. This significant length increase allowed us to integrate 5-AGNRs into devices and to realize the first field-effect transistor based on narrow bandgap AGNRs that shows switching behavior at room temperature. Our study highlights that optimized growth protocols can successfully bridge between the sub-nanometer scale, where atomic precision is needed to control the electronic properties, and the scale of tens of nanometers relevant for successful device integration of GNRs.

Bottom-up assembled nanomaterials and nanostructures allow for the studies of rich and unprecedented quantum-related and mesoscopic transport phenomena. However, it can be difficult to quantify the correlations between the geometrical or structural parameters obtained from advanced microscopy and measured electrical characteristics when they are made into macroscopic devices. Here, we propose a strategy to connect the nanomaterial morphologies and the device performance through a Monte Carlo device model and apply it to understand the scaling trends of bottom-up synthesized armchair graphene nanoribbon (GNR) transistors. A new nanofabrication process is developed for GNR transistors with channel length down to 7 nm. The impacts of the GNR spatial distributions and the device geometries on the device performance are investigated systematically through comparison of experimental data with the model. Through this study, challenges and opportunities of transistor technologies based on bottom-up synthesized GNRs are pinpointed, paving the way to the further improvement of the GNR device performance for future transistor technology nodes.

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSI based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%., Comment: 9 pages, 12 images, 3 tables with results, We have an intention to submit this paper to the current journal focused on computer methods/deep learning in biomedicine

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results., Comment: Accepted to SYSID 2021 (revised with reviewer feedback)

In this paper, we consider the simplest class of stratified spaces -- linearly embedded graphs. We present an algorithm that learns the abstract structure of an embedded graph and models the specific embedding from a point cloud sampled from it. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and prove the correctness of the identified abstract structure under assumptions on the embedding. The algorithm is implemented in the Julia package http://github.com/yossibokor/Skyler.jl , which we used for the numerical simulations in this paper.

Many questions are still open regarding the physical mechanisms behind the magnetic switching in GdFeCo alloys by single optical pulses. Phenomenological models suggest a femtosecond scale exchange relaxation between sublattice magnetization as the driving mechanism for switching. The recent observation of thermally induced switching in GdFeCo by using both several picosecond optical laser pulse as well as electric current pulses has questioned this previous understanding. This has raised the question of whether or not the same switching mechanics are acting at the femo- and picosecond scales. In this work, we aim at filling this gap in the understanding of the switching mechanisms behind thermal single-pulse switching. To that end, we have studied experimentally thermal single-pulse switching in GdFeCo alloys, for a wide range of system parameters, such as composition, laser power and pulse duration. We provide a quantitative description of the switching dynamics using atomistic spin dynamics methods with excellent agreement between the model and our experiments across a wide range of parameters and timescales, ranging from femtoseconds to picoseconds. Furthermore, we find distinct element-specific damping parameters as a key ingredient for switching with long picosecond pulses and argue, that switching with pulse durations as long as 15 picoseconds is possible due to a low damping constant of Gd. Our findings can be easily extended to speed up dynamics in other contexts where ferrimagnetic GdFeCo alloys have been already demonstrated to show fast and energy-efficient processes, e.g. domain-wall motion in a track and spin-orbit torque switching in spintronics devices.

Reducing energy dissipation while increasing speed in computation and memory is a long-standing challenge for spintronics research. In the last 20 years, femtosecond lasers have emerged as a tool to control the magnetization in specific magnetic materials at the picosecond timescale. However, the use of ultrafast optics in integrated circuits and memories would require a major paradigm shift. An ultrafast electrical control of the magnetization is far preferable for integrated systems. Here we demonstrate reliable and deterministic control of the out-of-plane magnetization of a 1 nm-thick Co layer with single 6 ps-wide electrical pulses that induce spin-orbit torques on the magnetization. We can monitor the ultrafast magnetization dynamics due to the spin-orbit torques on sub-picosecond timescales, thus far accessible only by numerical simulations. Due to the short duration of our pulses, we enter a counter-intuitive regime of switching where heat dissipation assists the reversal. Moreover, we estimate a low energy cost to switch the magnetization, projecting to below 1fJ for a (20 nm)^3 cell. These experiments prove that spintronic phenomena can be exploited on picosecond time-scales for full magnetic control and should launch a new regime of ultrafast spin torque studies and applications., Comment: Includes article + supplementary information. Latest version uses full name of the first author. Nature Electronics (2020)

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (\textit{a}-) rare earth-transition metal (\textit{a}-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic \textit{a}-Gd$_{22-x}$Tb$_x$Co$_{78}$ thin films, from x = 0 to x = 18, and elucidate the role of Gd in HI-AOS in \textit{a}-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with $L = 0$) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing \textit{a}-Gd$_{10}$Tb$_{12}$Co$_{78}$ leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that acounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.

Many data-rich industries are interested in the efficient discovery and modelling of structures underlying large data sets, as it allows for the fast triage and dimension reduction of large volumes of data embedded in high dimensional spaces. The modelling of these underlying structures is also beneficial for the creation of simulated data that better represents real data. In particular, for systems testing in cases where the use of real data streams might prove impractical or otherwise undesirable. We seek to discover and model the structure by combining methods from topological data analysis with numerical modelling. As a first step in combining these two areas, we examine the recovery of the abstract graph $G$ structure, and model a linear embedding $|G|$ given only a noisy point cloud sample $X$ of $|G|$., Comment: 7 pages, 3 figures, accepted for MODSIM 2019 conference

The classical Kneser-Milnor theorem says that every closed oriented connected 3-dimensional manifold admits a unique connected sum decomposition into manifolds that cannot be decomposed any further. We discuss to what degree such decompositions exist in higher dimensions and we show that in many settings uniqueness fails in higher dimensions., Comment: 25 pages, fixed several minor mistakes, final version