Multi-period Project Portfolio Selection under Risk considerations and Stochastic Income

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably

A new method to determine the elastopalstic properties of ductile materials by conical indentation

Based on load-displacement curves, indentation is widely used to extract the elastoplastic properties of materials. It is generally believed that such a measure is non-unique and a full stress-strain curve cannot be obtained using plural sharp and deep spherical indenters. In this paper we show that by introducing an additional dimensionless function of DA / A (the ratio of residual area to the area of an indenter profile) in the reverse analysis, the elastoplastic properties of several unknown materials that exhibit visually indistinguishable load-displacement curves can be uniquely determined with a sharp indentation

A Single-Photon Imager Based on Microwave Plasmonic Superconducting Nanowire

Detecting spatial and temporal information of individual photons by using single-photon-detector (SPD) arrays is critical to applications in spectroscopy, communication, biological imaging, astronomical observation, and quantum-information processing. Among the current SPDs1,detectors based on superconducting nanowires have outstanding performance2, but are limited in their ability to be integrated into large scale arrays due to the engineering difficulty of high-bandwidth cryogenic electronic readout3-8. Here, we address this problem by demonstrating a scalable single-photon imager using a single continuous photon-sensitive superconducting nanowire microwave-plasmon transmission line. By appropriately designing the nanowire's local electromagnetic environment so that the nanowire guides microwave plasmons, the propagating voltages signals generated by a photon-detection event were slowed down to ~ 2% of the speed of light. As a result, the time difference between arrivals of the signals at the two ends of the nanowire naturally encoded the position and time of absorption of the photon. Thus, with only two readout lines, we demonstrated that a 19.7-mm-long nanowire meandered across an area of 286 {\mu}m * 193 {\mu}m was capable of resolving ~590 effective pixels while simultaneously recording the arrival times of photons with a temporal resolution of 50 ps. The nanowire imager presents a scalable approach to realizing high-resolution photon imaging in time and space

TRPA1 Is a Polyunsaturated Fatty Acid Sensor in Mammals

Fatty acids can act as important signaling molecules regulating diverse physiological processes. Our understanding, however, of fatty acid signaling mechanisms and receptor targets remains incomplete. Here we show that Transient Receptor Potential Ankyrin 1 (TRPA1), a cation channel expressed in sensory neurons and gut tissues, functions as a sensor of polyunsaturated fatty acids (PUFAs) in vitro and in vivo. PUFAs, containing at least 18 carbon atoms and three unsaturated bonds, activate TRPA1 to excite primary sensory neurons and enteroendocrine cells. Moreover, behavioral aversion to PUFAs is absent in TRPA1-null mice. Further, sustained or repeated agonism with PUFAs leads to TRPA1 desensitization. PUFAs activate TRPA1 non-covalently and independently of known ligand binding domains located in the N-terminus and 5th transmembrane region. PUFA sensitivity is restricted to mammalian (rodent and human) TRPA1 channels, as the drosophila and zebrafish TRPA1 orthologs do not respond to DHA. We propose that PUFA-sensing by mammalian TRPA1 may regulate pain and gastrointestinal functions

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer-reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state-of-the-art handbook for basic and clinical researchers

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures