Theoretical proposal for a biosensing approach based on a linear array of immobilized gold nanoparticles

We propose a sensing mechanism for detection of analytes that can specifically recognized. The sensor is based on closely-spaced chains of functionalized gold nanoparticles (NPs) immobilized on a waveguide surface, with the signal detected by evanescent waveguide absorption spectroscopy. The localized surface plasmon spectrum of a linear array of closely-spaced, hemispherical gold NPs is calculated using the discrete dipole approximation. The plasmon band is found to broaden to a nanowirelike spectrum when a dielectric coating is put on the particles, and the light polarization is along the NP chain. The origin of this broadening is shown to be the polarization-dependent overlap of the evanescent fields of adjacent NPs upon application of the dielectric coating. These features suggests a mechanism for biosensing with an improved sensitivity compared with traditional NP biosensor methods

Statistical physics-based reconstruction in compressed sensing

Compressed sensing is triggering a major evolution in signal acquisition. It consists in sampling a sparse signal at low rate and later using computational power for its exact reconstruction, so that only the necessary information is measured. Currently used reconstruction techniques are, however, limited to acquisition rates larger than the true density of the signal. We design a new procedure which is able to reconstruct exactly the signal with a number of measurements that approaches the theoretical limit in the limit of large systems. It is based on the joint use of three essential ingredients: a probabilistic approach to signal reconstruction, a message-passing algorithm adapted from belief propagation, and a careful design of the measurement matrix inspired from the theory of crystal nucleation. The performance of this new algorithm is analyzed by statistical physics methods. The obtained improvement is confirmed by numerical studies of several cases.Comment: 20 pages, 8 figures, 3 tables. Related codes and data are available at http://aspics.krzakala.or

Control of surface plasmon resonances in dielectrically coated proximate gold nanoparticles immobilized on a substrate

We present experimental and theoretical results for the changes in the optical-plasmon resonance of gold-nanoparticle dimers immobilized on a surface when coated with an organic dielectric material. The plasmon band of a nanoparticle dimer shifts to a higher wavelength when the distance between neighboring particles is decreased, and a well-separated second peak appears. This phenomenon is called cross-talk. We find that an organic coating lets cross-talk start at larger separation distances than for uncoated dimers by bridging the gap between immobilized nanoparticles (creating optical clusters). We study this optical clustering effect as a function of the polarization of the applied light, of the inter-particle distance, of the surrounding environment, and of the optical properties of the coating layer. Theoretical discrete-dipole approximation calculations support the experimental absorption spectroscopy results of gold nanoparticles on glass substrates and on optical waveguides

Morfometric Study Of Seagrass Thalassia Hemprichii, In Pasir Panjang Beach, Paputungan Village, West Likupang, Minahasa Utara

Tujuan penelitian adalah untuk mengetahui jenis lamun apa saja yang ada di lokasi penelitian ini dan membandingkan ukuran morfometrik lamun Thalassia hemprichii berdasarkan stasiun pengambilan sampel. Pengambilan data dilakukan dengan mengunakan metode survei jelajah, sampel diambil dengan parang bersamaan dengan substrat, dicuci dengan air laut dan dimasukan kedalam ember yang berisi air laut. Saat pengambilan sampel dilakukan, posisi diplot dengan mengunakan GPS dan dilanjutkan dengan pengambilan sample lamun Thalassia hemprichii sebanyak 30 individu setiap stasiun, Pada hasil yang diperoleh terlihat bahwa spesies Thalassia hemprichii di Perairan Pantai Pasir Panjang yang lebih besar di daerah mangrove dan daerah lamun dan yang lebih kecil adalah daerah terumbu karang. Hal ini disebabkan, karena daerah mangrove dan lamun tersebut tumbuh pada subsrat lumpur yang memiliki kandungan nutrien lebih tinggi dibandingkan dengan daerah terumbu karang dengan subsrat pecahan karang, dan keadaan perairan pada subsrat lumpur lebih tenang sehingga banyak mengendapkan sedimen

Quantum phase retrieval of a Rydberg wave packet using a half-cycle pulse

A terahertz half-cycle pulse was used to retrieve information stored as quantum phase in an $N$-state Rydberg atom data register. The register was prepared as a wave packet with one state phase-reversed from the others (the "marked bit"). A half-cycle pulse then drove a significant portion of the electron probability into the flipped state via multimode interference.Comment: accepted by PR

Time-frequency relationship between U.S. output with commodity and asset prices

Commodity and asset prices have a well-documented effect on economic growth, manifested through various channels. At the same time, the business cycle influences the commodity and asset prices. Whereas empirical evidence on the effect of commodity and asset prices on the long-run economic growth is ambiguous, most of the previous researches highlight a positive correlation in the short-run. The aim of this paper is to disentangle the short- and long-run comovements between U.S. historical business cycles and commodity and asset prices, over the period 1859-2013. For this purpose we use a time-frequency approach and we test the historical influence of oil, gold, housing and stock prices, over the output growth. Different from other studies, we control for the effect of other prices and monetary conditions, using the wavelet partial coherency. In line with the previous works, we discover that comovements between economic growth and commodity and assets prices manifest especially in the short-run. We also find that stock returns and housing prices have a more powerful effect on the U.S. economic growth rate than the oil and gold prices. The long-run comovements are documented especially around the World War II. Finally, when controlling for the influence of the interest rate, inflation and other commodity and asset prices, comovements become weaker in the short-run. In general the oil and housing prices lead the GDP growth, the U.S. output lead the gold prices, while there is no clear causality direction between business cycle and stock prices.http://www.tandfonline.com/loi/raec202017-06-12hb201

Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices

Compressed sensing is a signal processing method that acquires data directly in a compressed form. This allows one to make less measurements than what was considered necessary to record a signal, enabling faster or more precise measurement protocols in a wide range of applications. Using an interdisciplinary approach, we have recently proposed in [arXiv:1109.4424] a strategy that allows compressed sensing to be performed at acquisition rates approaching to the theoretical optimal limits. In this paper, we give a more thorough presentation of our approach, and introduce many new results. We present the probabilistic approach to reconstruction and discuss its optimality and robustness. We detail the derivation of the message passing algorithm for reconstruction and expectation max- imization learning of signal-model parameters. We further develop the asymptotic analysis of the corresponding phase diagrams with and without measurement noise, for different distribution of signals, and discuss the best possible reconstruction performances regardless of the algorithm. We also present new efficient seeding matrices, test them on synthetic data and analyze their performance asymptotically.Comment: 42 pages, 37 figures, 3 appendixe

Forecasting South African inflation using non-linearmodels : a weighted loss-based evaluation

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-ofsample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT’s performance in forecasting South African inflation.http://www.tandfonline.com/loi/raec202017-07-30hb201

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model.We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.http://www.tandfonline.com/loi/raec202016-05-30hb201