In the period 1991-2015, algorithmic advances in Mixed Integer Optimization (MIO) coupled with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving MIO ...problems. We present a MIO approach for solving the classical best subset selection problem of choosing k out of p features in linear regression given n observations. We develop a discrete extension of modern first-order continuous optimization methods to find high quality feasible solutions that we use as warm starts to a MIO solver that finds provably optimal solutions. The resulting algorithm (a) provides a solution with a guarantee on its suboptimality even if we terminate the algorithm early, (b) can accommodate side constraints on the coefficients of the linear regression and (c) extends to finding best subset solutions for the least absolute deviation loss function. Using a wide variety of synthetic and real datasets, we demonstrate that our approach solves problems with n in the 1000s and p in the 100s in minutes to provable optimality, and finds near optimal solutions for n in the 100s and p in the 1000s in minutes. We also establish via numerical experiments that the MIO approach performs better than Lasso and other popularly used sparse learning procedures, in terms of achieving sparse solutions with good predictive power.
This study empirically investigates the statistical characteristics and predictability of Bitcoin return and volatility. The distribution of Bitcoin returns and volatility display a fat right tail ...and high central parts. Bitcoin does not show the dynamic property of volatility persistence, contrary to stylized facts in financial time series. Also, the autoregressive model using past volatility does not well work in predicting changes in Bitcoin volatility for future periods. Investor sentiment regarding Bitcoin has a significant information value for explaining changes in Bitcoin volatility for future periods. These results suggest that Bitcoin appears to be an investment asset with high volatility and dependence on investor sentiment rather than a monetary asset.
•This study examines the statistical properties and predictability of Bitcoin return and volatility.•Distributional property of Bitcoin shows a fat right tail and high central parts.•Interestingly, dynamic property of Bitcoin does not show the general volatility persistence.•Investor sentiment on Bitcoin has an information effect to predict Bitcoin volatility.•Investor sentiment might play a crucial role in the predictability of Bitcoin price changes.
Several researchers have shown how sisal fibres possess remarkable tensile properties that yield them good candidates as reinforcement in biocomposite materials.
This work aims to evaluate the effect ...of an eco-friendly and cost effective surface treatment method based on the use of commercial sodium bicarbonate (i.e. baking soda) on properties of sisal fibre and its epoxy composites. In particular, raw sisal fibres were treated with a 10%w/w of sodium bicarbonate solution for different periods (24, 120 and 240 h), at room temperature. Changes occurring in sisal fibres were characterized through scanning electron microscope, Fourier transform infrared spectroscopy, thermogravimetric analysis and helium pycnometer analysis.
The mechanical characterization of sisal fibre was carried out through single fibre tensile tests and a reliability analysis of the experimental data was performed. A mathematical model was also applied to investigate the relation between the transverse dimension of the fibres and their tensile properties. Interfacial adhesion of sisal fibre with an epoxy matrix was investigated using single fibre pull out technique. Moreover, to deeper investigate the effect of the proposed treatment, epoxy based composites reinforced with short randomly oriented sisal fibres were manufactured and characterized by means of quasi-static flexural tests.
The experimental results showed that 120 h is the optimum time for treating sisal fibre to achieve highest interfacial adhesion and mechanical properties with epoxy matrix.
With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle communication systems, the ability of conventional stationary channel ...models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters, including the number of clusters, the powers, and the delays of the clusters, the angles of departure, and the angles of arrival. Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function and local temporal autocorrelation function are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model.
In this work, we investigate thermodynamic properties and the relativistic behavior of a neutral spin-one boson particle in one-dimensional space by means of the generalized Duffin–Kemmer–Petiau ...(DKP) equation generated by incorporating a new nonminimal coupling related to the q-deformed formalism. Then, we obtain analytical solutions of the generalized DKP relativistic q-deformed quantum oscillator. To form the interaction of the scalar field with the q-deformed non-minimal coupling, we express the q-deformed oscillator in terms of the q-momentum and q-position operators so that these operators depend on the raising and lowering operators. In this context, we calculate some deformed thermodynamic quantities such as the partition function, Helmholtz free energy, mean energy, entropy, and specific heat capacity.
Representative volume elements (RVEs) with random fiber distribution are widely used in micro-mechanics for determining the properties of unidirectional fiber-reinforced composites from their ...microstructure. In this paper, the random sequential expansion (RSE) and particle swarm optimization (PSO) algorithms are combined to develop an efficient methodology for generating such RVEs. This methodology can successfully eliminate the biased regions of dense fiber population and unreal matrix-rich corners that usually appear in created RVEs and resolve the complications in selection of the input parameters in the RSE algorithm. Statistical analysis of the generated microstructures and two- and three-dimensional finite element analyses are performed to validate the capability of the proposed algorithms.
Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D ...structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.
In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate ...important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability.
To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving ...as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.
In this work, kenaf fibers were pre-treated in a NaOH solution (6% in weight) at room temperature for two different periods (48 and 144h). The chemical treatment of kenaf fibers for 48h allowed to ...clean their surface removing each impurity whereas 144h of immersion time had detrimental effect on the fibers surface and, consequently, on their mechanical properties.
Untreated and NaOH treated kenaf fibers (i.e. for 48h) were also used as reinforcing agent of epoxy resin composites. The effect of the stacking sequence (i.e. using unidirectional long fibers or randomly oriented short fibers) and the chemical treatment on the static mechanical properties was evaluated showing that the composites exhibit higher moduli in comparison to the neat resin. As regards the strength properties, only the composites reinforced with unidirectional layers show higher strength than the neat resin. Moreover, the alkali treatment increased the mechanical properties of the composites, due to the improvement of fiber–matrix compatibility.
The dynamic mechanical analysis showed that the storage and the loss moduli are mainly influenced by the alkali treatment above the glass transition temperature. Moreover, the alkali treatment led to a notable reduction of tanδ peaks in addition to significant shifts of tanδ peaks to higher temperatures whereas the stacking sequence did not influence the trends of storage modulus, loss modulus and damping of the composites.
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