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Laser-based powder bed fusion (L-PBF) is a promising technology for the production of near net–shaped metallic components. The high surface roughness and the comparatively low-dimensional accuracy of such components, however, usually require a finishing by a subtractive process such as milling or grinding in order to meet the requirements of the application. Materials manufactured via L-PBF are characterized by a unique microstructure and anisotropic material properties. These specific properties could also affect the subtractive processes themselves. In this paper, the effect of L-PBF on the machinability of the aluminum alloy AlSi10Mg is explored when milling. The chips, the process forces, the surface morphology, the microhardness, and the burr formation are analyzed in dependence on the manufacturing parameter settings used for L-PBF and the direction of feed motion of the end mill relative to the build-up direction of the parts. The results are compared with a conventionally cast AlSi10Mg. The analysis shows that L-PBF influences the machinability. Differences between the reference and the L-PBF AlSi10Mg were observed in the chip form, the process forces, the surface morphology, and the burr formation. The initial manufacturing method of the part thus needs to be considered during the design of the finishing process to achieve suitable results.
Fucoidans are multifunctional marine macromolecules that are subjected to numerous and various downstream processes during their production. These processes were considered the most important abiotic factors affecting fucoidan chemical skeletons, quality, physicochemical properties, biological properties and industrial applications. Since a universal protocol for fucoidans production has not been established yet, all the currently used processes were presented and justified. The current article complements our previous articles in the fucoidans field, provides an updated overview regarding the different downstream processes, including pre-treatment, extraction, purification and enzymatic modification processes, and shows the recent non-traditional applications of fucoidans in relation to their characters.
Background: The positive effect of carbohydrates from commercial beverages on soccer-specific exercise has been clearly demonstrated. However, no study is available that uses a home-mixed beverage in a test where technical skills were required. Methods: Nine subjects participated vol-untarily in this double-blind, randomized, placebo-controlled crossover study. On three testing days, the subjects performed six Hoff tests with a 3-min active break as a preload and then the Yo-Yo Intermittent Running Test Level 1 (Yo-Yo IR1) until exhaustion. On test days 2 and 3, the subjects received either a 69 g carbohydrate-containing drink (syrup–water mixture) or a carbo-hydrate-free drink (aromatic water). Beverages were given in several doses of 250 mL each: 30 min before and immediately before the exercise and after 18 and 39 min of exercise. The primary target parameters were the running performance in the Hoff test and Yo-Yo IR1, body mass and heart rate. Statistical differences between the variables of both conditions were analyzed using paired samples t-tests. Results: The maximum heart rate in Yo-Yo IR1 showed significant differ-ences (syrup: 191.1 ± 6.2 bpm; placebo: 188.0 ± 6.89 bpm; t(6) = −2.556; p = 0.043; dz = 0.97). The running performance in Yo-Yo IR1 under the condition syrup significantly increased by 93.33 ± 84.85 m (0–240 m) on average (p = 0.011). Conclusions: The intake of a syrup–water mixture with a total of 69 g carbohydrates leads to an increase in high-intensive running performance after soccer specific loads. Therefore, the intake of carbohydrate solutions is recommended for intermit-tent loads and should be increasingly considered by coaches and players.
This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.
In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.
It is difficult for robots to handle a vibrating deformable object. Even for human beings it is a high-risk operation to, for example, insert a vibrating linear object into a small hole. However, fast manipulation using a robot arm is not just a dream; it may be achieved if some important features of the vibration are detected online. In this paper, we present an approach for fast manipulation using a force/torque sensor mounted on the robot's wrist. Template matching method is employed to recognize the vibrational phase of the deformable objects. Therefore, a fast manipulation can be performed with a high success rate, even if there is acute vibration. Experiments inserting a deformable object into a hole are conducted to test the presented method. Results demonstrate that the presented sensor-based online fast manipulation is feasible.
As a consequence of the real estate market crash after 2008, large investors invested a significant amount of wealth into single-family houses to construct a portfolio of rental dwellings, whose income is securitized in the capital. In some local housing markets, these investors own remarkable numbers of single-family houses. Furthermore, their trading activities have resulted in a new investment strategy, which exacerbates property wealth concentration and polarization. This new investment strategy and its portfolio optimization inspire curiosity about its influence on housing markets. This paper first aims to find an optimal portfolio strategy by employing an expected utility optimization from the terminal wealth, which adopts a stochastic model that includes a variety of economic states to estimate house prices. Second, it aims to analyze the effect of large investors on the housing market. The results show the investment strategies of large investors depend on the balance among economic state, maintenance cost, rental income, interest rate and investment willingness of large investors to housing and their effect depends on the state of the economy.
Load modeling is one of the crucial tasks for improving smart grids’ energy efficiency. Among many alternatives, machine learning-based load models have become popular in applications and have shown outstanding performance in recent years. The performance of these models highly relies on data quality and quantity available for training. However, gathering a sufficient amount of high-quality data is time-consuming and extremely expensive. In the last decade, Generative Adversarial Networks (GANs) have demonstrated their potential to solve the data shortage problem by generating synthetic data by learning from recorded/empirical data. Educated synthetic datasets can reduce prediction error of electricity consumption when combined with empirical data. Further, they can be used to enhance risk management calculations. Therefore, we propose RCGAN, TimeGAN, CWGAN, and RCWGAN which take individual electricity consumption data as input to provide synthetic data in this study. Our work focuses on one dimensional times series, and numerical experiments on an empirical dataset show that GANs are indeed able to generate synthetic data with realistic appearance.
Recently, phase field modeling of fatigue fracture has gained a lot of attention from many researches and studies, since the fatigue damage of structures is a crucial issue in mechanical design. Differing from traditional phase field fracture models, our approach considers not only the elastic strain energy and crack surface energy, additionally, we introduce a fatigue energy contribution into the regularized energy density function caused by cyclic load. Comparing to other type of fracture phenomenon, fatigue damage occurs only after a large number of load cycles. It requires a large computing effort in a computer simulation. Furthermore, the choice of the cycle number increment is usually determined by a compromise between simulation time and accuracy. In this work, we propose an efficient phase field method for cyclic fatigue propagation that only requires moderate computational cost without sacrificing accuracy. We divide the entire fatigue fracture simulation into three stages and apply different cycle number increments in each damage stage. The basic concept of the algorithm is to associate the cycle number increment with the damage increment of each simulation iteration. Numerical examples show that our method can effectively predict the phenomenon of fatigue crack growth and reproduce fracture patterns.
Phospho-regulation of the Shugoshin - Condensin interaction at the centromere in budding yeast
(2020)
Correct bioriented attachment of sister chromatids to the mitotic spindle is essential for chromosome segregation. In budding yeast, the conserved protein shugoshin (Sgo1) contributes to biorientation by recruiting the protein phosphatase PP2A-Rts1 and the condensin complex to centromeres. Using peptide prints, we identified a Serine-Rich Motif (SRM) of Sgo1 that mediates the interaction with condensin and is essential for centromeric condensin recruitment and the establishment of biorientation. We show that the interaction is regulated via phosphorylation within the SRM and we determined the phospho-sites using mass spectrometry. Analysis of the phosphomimic and phosphoresistant mutants revealed that SRM phosphorylation disrupts the shugoshin–condensin interaction. We present evidence that Mps1, a central kinase in the spindle assembly checkpoint, directly phosphorylates Sgo1 within the SRM to regulate the interaction with condensin and thereby condensin localization to centromeres. Our findings identify novel mechanisms that control shugoshin activity at the centromere in budding yeast.