Constant-Factor Approximation Algorithms for the Parity-Constrained Facility Location Problem

Facility location is a prominent optimization problem that has inspired a large quantity of both theoretical and practical studies in combinatorial optimization. Although the problem has been investigated under various settings reflecting typical structures within the optimization problems of practical interest, little is known on how the problem behaves in conjunction with parity constraints. This shortfall of understanding was rather discouraging when we consider the central role of parity in the field of combinatorics. In this paper, we present the first constant-factor approximation algorithm for the facility location problem with parity constraints. We are given as the input a metric on a set of facilities and clients, the opening cost of each facility, and the parity requirement - odd, even, or unconstrained - of every facility in this problem. The objective is to open a subset of facilities and assign every client to an open facility so as to minimize the sum of the total opening costs and the assignment distances, but subject to the condition that the number of clients assigned to each open facility must have the same parity as its requirement. Although the unconstrained facility location problem as a relaxation for this parity-constrained generalization has unbounded gap, we demonstrate that it yields a structured solution whose parity violation can be corrected at small cost. This correction is prescribed by a T-join on an auxiliary graph constructed by the algorithm. This auxiliary graph does not satisfy the triangle inequality, but we show that a carefully chosen set of shortcutting operations leads to a cheap and sparse T-join. Finally, we bound the correction cost by exhibiting a combinatorial multi-step construction of an upper bound

Real-Time ISR-YOLOv4 Based Small Object Detection for Safe Shop Floor in Smart Factories

Wearing a hard hat can effectively improve the safety of workers on a construction site. However, workers often take off their helmets because they have a weak sense of safety and are uncomfortable, and this action poses a large danger. Workers not wearing hard hats are more likely to be injured in accidents such as human falls and vertical falls. Therefore, the detection of wearing a helmet is an important step in the safety management of a construction site, and it is urgent to detect helmets quickly and accurately. However, the existing manual monitor is labor intensive, and it is difficult to popularize the method of mounting the sensor on the helmet. Thus, in this paper, we propose an AI method to detect the wearing of a helmet with satisfactory accuracy with a high detection rate. Our method selects based on YOLOv4 and adds an image super resolution (ISR) module at the end of the input. Afterward, the image resolution is increased, and the noise in the image is removed. Then, dense blocks are used to replace residual blocks in the backbone network using the CSPDarknet53 framework to reduce unnecessary computation and reduce the number of network structure parameters. The neck then uses a combination of SPPnet and PANnet to take full advantage of the small target’s capabilities in the image. We add foreground and background balance loss functions to the YOLOv4 loss function part to solve the image background and foreground imbalance problem. Experiments performed using self-constructed datasets show that the proposed method has more efficacy than the currently available small target detection methods. Finally, our model achieves an average precision of 93.3%, a 7.8% increase over the original algorithm, and it takes only 3.0 ms to detect an image at 416 × 416

Practical Operation Strategies for Energy Storage System under Uncertainty

Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method

Engineering Biology to Construct Microbial Chassis for the Production of Difficult-to-Express Proteins

A large proportion of the recombinant proteins manufactured today rely on microbe-based expression systems owing to their relatively simple and cost-effective production schemes. However, several issues in microbial protein expression, including formation of insoluble aggregates, low protein yield, and cell death are still highly recursive and tricky to optimize. These obstacles are usually rooted in the metabolic capacity of the expression host, limitation of cellular translational machineries, or genetic instability. To this end, several microbial strains having precisely designed genomes have been suggested as a way around the recurrent problems in recombinant protein expression. Already, a growing number of prokaryotic chassis strains have been genome-streamlined to attain superior cellular fitness, recombinant protein yield, and stability of the exogenous expression pathways. In this review, we outline challenges associated with heterologous protein expression, some examples of microbial chassis engineered for the production of recombinant proteins, and emerging tools to optimize the expression of heterologous proteins. In particular, we discuss the synthetic biology approaches to design and build and test genome-reduced microbial chassis that carry desirable characteristics for heterologous protein expression

Synthetic Biology Approaches in the Development of Engineered Therapeutic Microbes

Since the intimate relationship between microbes and human health has been uncovered, microbes have been in the spotlight as therapeutic targets for several diseases. Microbes contribute to a wide range of diseases, such as gastrointestinal disorders, diabetes and cancer. However, as host-microbiome interactions have not been fully elucidated, treatments such as probiotic administration and fecal transplantations that are used to modulate the microbial community often cause nonspecific results with serious safety concerns. As an alternative, synthetic biology can be used to rewire microbial networks such that the microbes can function as therapeutic agents. Genetic sensors can be transformed to detect biomarkers associated with disease occurrence and progression. Moreover, microbes can be reprogrammed to produce various therapeutic molecules from the host and bacterial proteins, such as cytokines, enzymes and signaling molecules, in response to a disturbed physiological state of the host. These therapeutic treatment systems are composed of several genetic parts, either identified in bacterial endogenous regulation systems or developed through synthetic design. Such genetic components are connected to form complex genetic logic circuits for sophisticated therapy. In this review, we discussed the synthetic biology strategies that can be used to construct engineered therapeutic microbes for improved microbiome-based treatment

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension

Synthetic 3-UTR valves for optimal metabolic flux control in Escherichia coli.

As the design of genetic circuitry for synthetic biology becomes more sophisticated, diverse regulatory bioparts are required. Despite their importance, well-characterized 3-untranslated region (3-UTR) bioparts are limited. Thus, transcript 3-ends require further investigation to understand the underlying regulatory role and applications of the 3-UTR. Here, we revisited the use of Term-Seq in the Escherichia coli strain K-12 MG1655 to enhance our understanding of 3-UTR regulatory functions and to provide a diverse collection of tunable 3-UTR bioparts with a wide termination strength range. Comprehensive analysis of 1,629 transcript 3-end positions revealed multiple 3-termini classes generated through transcription termination and RNA processing. The examination of individual Rho-independent terminators revealed a reduction in downstream gene expression over a wide range, which led to the design of novel synthetic metabolic valves that control metabolic fluxes in branched pathways. These synthetic metabolic valves determine the optimal balance of heterologous pathways for maximum target biochemical productivity. The regulatory strategy using 3-UTR bioparts is advantageous over promoter- or 5-UTR-based transcriptional control as it modulates gene expression at transcription levels without trans-acting element requirements (e.g. transcription factors). Our results provide a foundational platform for 3-UTR engineering in synthetic biology applications

Intravital longitudinal wide-area imaging of dynamic bone marrow engraftment and multilineage differentiation through nuclear-cytoplasmic labeling

<div><p>Bone marrow is a vital tissue that produces the majority of erythrocytes, thrombocytes, and immune cells. Bone marrow transplantation (BMT) has been widely performed in patients with blood disorders and cancers. However, the cellular-level behaviors of the transplanted bone marrow cells over wide-areas of the host bone marrow after the BMT are not fully understood yet. In this work, we performed a longitudinal wide-area cellular-level observation of the calvarial bone marrow after the BMT <i>in vivo</i>. Using a H2B-GFP/β-actin-DsRed double-transgenic mouse model as a donor, a subcellular-level nuclear-cytoplasmic visualization of the transplanted bone marrow cells was achieved, which enabled a direct <i>in vivo</i> dynamic monitoring of the distribution and proliferation of the transplanted bone marrow cells. The same spots in the wide-area of the calvarial bone marrow were repeatedly identified using fluorescently labeled vasculature as a distinct landmark. It revealed various dynamic cellular-level behaviors of the transplanted BM cells in early stage such as cluster formation, migration, and active proliferation <i>in vivo</i>.</p></div