A polynomial time algorithm for solving a quality control station configuration problem

AbstractWe study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations; existing machines and optional quality control stations (QCS). Our aim is to simultaneously decide where and if to install the QCSs along the line and to determine the production rate, so as to maximize the steady state expected net profit per time unit from the system.We use dynamic programming to solve the cost minimization auxiliary problem where the aim is to minimize the time unit production cost for a given production rate. Using the above developed O(N2) dynamic programming algorithm as a subroutine, where N stands for the number of machines in the line, we present an O(N4) algorithm to solve the Profit Maximization QCS Configuration Problem

Butanol by Two Stage Fermentation

Current techniques for producing butanol tend to have a low yield and form a large amount of other solvents, because there is only one stage for fermentation. Having one stage limits the type of bacteria that can be used, because the chosen bacteria must be able to both convert glucose to butyric acid, and then convert butyric acid to butanol. The only types of bacteria that can perform both these tasks also create a lot of other acids, which are turned to other solvents in the product stream. This is most prevalent in ABE fermentation, which creates significant amounts of acetone and ethanol along with the butanol. David Ramey, of ButylFuel LLC, has created a distinct process that generates butanol, without significant amounts of acetone or ethanol, using a two-stage fermentation process. The first stage converts glucose to butyric acid through acidogenesis, while the second stage converts the butyric acid to butanol via solventogenesis. This process optimizes the efficiency and specific production of the desired solvent, butanol. The purpose of this report is to scale-up Ramey’s process and build a plant based on a two-stage fermentation procedure. The economical viability of producing 50 million gallons of butanol per year, at a purity of 99.5% from the plant will also been discussed. These results will allow the organization to determine the worth of licensing the technology from ButylFuel. Additionally, because this process will compete with many ethanol plants, it is necessary for the design to mirror a typical ethanol plant as much as possible. Because of this, aspects of the current production of ethanol were implemented in the design, including the Dry Grind process and the Dried Distillers Grain Drying process. These implementations allow the process to be constructed from modified ethanol plants, rather than having to rebuild a new plant. The fermentation phase of the design utilizes a series of fibrous bed reactors and two different strands of Clostridium bacteria for each stage. The product stream out of the second fermentation stage, containing butanol, is separated using a liquid-liquid extractor, and a series of distillation columns, to extract the butanol from water. Different separation options were researched, including pervaporation, decanters, and stripping. The liquid-liquid extractor with distillation columns was chosen in the end, because it was the simplest and most economical process for dealing with a product stream that was over 90% water. Also, a butanol/water azeotrope surfaces during the separations process that is efficiently dealt with by the extractor. For the economic analysis, this report uses 50 million gallons per year producing ethanol plant as a comparison with the butanol process. The total capital investment for the ethanol plant is about $74.1 million with an investment rate of return (IRR) of 33.1$1.48/gallon of ethanol produced. Since the design specifications involved the modification of an existing ethanol plant, it was assumed that some existing ethanol equipment would be integrated into the system. Specifically, the Dried Distiller’s Grains (DDGS) dryer and the Dry Grind process are assumed to be installed and operational in year one. Additionally, it was assumed this equipment had been fully depreciated by the time of construction of the butanol plant. The results of this report were based on 54.3 million gallons per year producing butanol plant. , For this design, a total capital investment of $219 million was determined. This is a substantial investment cost highlighted by the fact that the overall net present value (NPV) of the design, after 15 years, was found to be a negative$3.55 billion. The poor investment opportunity stems from the high cost of utilities needed to run the plant. Of the total annual costs, 94.5% is derived from the overall utility costs. The profitability analysis and a review of current market conditions indicate that this investment should not be undertaken due to its high degree of unprofitability. Serious consideration of external factors and of the design itself must be taken before pursuing any investment. These factors, such as the price of corn, will be outlined more thoroughly at the end of the report

Microparticles from tumors exposed to radiation promote immune evasion in part by PD-L1

Radiotherapy induces immune-related responses in cancer patients by various mechanisms. Here, we investigate the immunomodulatory role of tumor-derived microparticles (TMPs)-extracellular vesicles shed from tumor cells-following radiotherapy. We demonstrate that breast carcinoma cells exposed to radiation shed TMPs containing elevated levels of immune-modulating proteins, one of which is programmed death-ligand 1 (PD-L1). These TMPs inhibit cytotoxic T lymphocyte (CTL) activity both in vitro and in vivo, and thus promote tumor growth. Evidently, adoptive transfer of CTLs pre-cultured with TMPs from irradiated breast carcinoma cells increases tumor growth rates in mice recipients in comparison with control mice receiving CTLs pre-cultured with TMPs from untreated tumor cells. In addition, blocking the PD-1-PD-L1 axis, either genetically or pharmacologically, partially alleviates TMP-mediated inhibition of CTL activity, suggesting that the immunomodulatory effects of TMPs in response to radiotherapy is mediated, in part, by PD-L1. Overall, our findings provide mechanistic insights into the tumor immune surveillance state in response to radiotherapy and suggest a therapeutic synergy between radiotherapy and immune checkpoint inhibitors

The data-driven time-dependent orienteering problem with soft time windows

In this paper, we study an extension of the orienteering problem where travel times are random and time-dependent and service times are random. Additionally, the service at each selected customer is subject to a soft time window; that is, violation of the window is allowed but subject to a penalty that increases in the delay. A solution is a tour determined before the vehicle departs from the depot. The objective is to maximize the sum of the collected prizes net of the expected penalty. The randomness of the travel and service times is modeled by a set of scenarios based on historical data that can be collected from public geographical information services. We present an exact solution method for the problem based on a branch-and-bound algorithm enhanced by a local search procedure at the nodes. A numerical experiment demonstrates the merits of the proposed solution approach. This study is the first to consider an orienteering problem with stochastic travel times and soft time windows, which are more relevant than hard time windows in stochastic settings

Optimization and Discrete Geometry

International audienceThe conference brought together a diverse group of researchers to stimulate cross-disciplinary interaction and collaborative work between combinatorial and continuous optimizers, geometers, theory people and practitioners. The variety of topics covered illustrates the fruitful interaction between optimization and discrete geometry. The reader will find recent results in polyhedral optimization, separable optimization, quadratic optimization, neural networks training, parameterized complexity, volume computation, and geometric aspect of linear optimization

Graph Neural Network for Cell Tracking in Microscopy Videos

We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph. This is accomplished by several key contributions incorporated into an end-to-end deep learning framework. We exploit a deep metric learning algorithm to extract cell feature vectors that distinguish between instances of different biological cells and assemble same cell instances. We introduce a new GNN block type which enables a mutual update of node and edge feature vectors, thus facilitating the underlying message passing process. The message passing concept, whose extent is determined by the number of GNN blocks, is of fundamental importance as it enables the `flow' of information between nodes and edges much behind their neighbors in consecutive frames. Finally, we solve an edge classification problem and use the identified active edges to construct the cells' tracks and lineage trees. We demonstrate the strengths of the proposed cell tracking approach by applying it to 2D and 3D datasets of different cell types, imaging setups, and experimental conditions. We show that our framework outperforms current state-of-the-art methods on most of the evaluated datasets. The code is available at our repository: https://github.com/talbenha/cell-tracker-gnn.Comment: Accepted to ECCV 202