Wavelet: a new tool for business cycle analysis

One basic problem in business-cycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of business cycle analysis, such as the correlation analysis and the spectral analysis, cannot capture such historical information because they do not take the time-varying characteristics of the business cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the business cycle: the wavelet-based time-frequency analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the business cycle, but also situations where the frequency of the business cycle shifts in time. Our empirical analyses show that 1973 marks a new era for the evolution of the business cycle.Business cycles

Quantum Oscillations in EuFe2As2 single crystals

Quantum oscillation measurements can provide important information about the Fermi surface (FS) properties of strongly correlated metals. Here, we report a Shubnikov-de Haas (SdH) effect study on the pnictide parent compounds EuFe$_{2}$As$_{2}$ (Eu122) and BaFe$_{2}$As$_{2}$ (Ba122) grown by In-flux. Although both members are isovalent compounds with approximately the same density of states at the Fermi level, our results reveal subtle changes in their fermiology. Eu122 displays a complex pattern in the Fourier spectrum, with band splitting, magnetic breakdown orbits, and effective masses sistematically larger when compared to Ba122, indicating that the former is a more correlated metal. Moreover, the observed pockets in Eu122 are more isotropic and 3D-like, suggesting an equal contribution from the Fe $3d$ orbitals to the FS. We speculate that these FS changes may be responsible for the higher spin-density wave ordering temperature in Eu122.Comment: 5 pages, 4 figure

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.Comment: 18 pages, 2 figures, 4 Table

Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning

Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online. In this paper, we study the use of such approach in combination with model predictive control for verifiable safe path planning under uncertainties. We first use the learned density distribution to compute the risk of collision online. If such risk exceeds the acceptable threshold, our method will plan for a new path around the previous trajectory, with the risk of collision below the threshold. Our method is well-suited to handle systems with uncertainties and complicated dynamics as our data-driven approach does not need an analytical form of the systems' dynamics and can estimate forward state density with an arbitrary initial distribution of uncertainties. We design two challenging scenarios (autonomous driving and hovercraft control) for safe motion planning in environments with obstacles under system uncertainties. We first show that our density estimation approach can reach a similar accuracy as the Monte-Carlo-based method while using only 0.01X training samples. By leveraging the estimated risk, our algorithm achieves the highest success rate in goal reaching when enforcing the safety rate above 0.99.Comment: NASA Formal Methods 202

Degradation of organic pollutants in leachate from a garbage incineration plant: Exploring Electrochemical oxidation for the treatment of membrane bioreactor (MBR) effluent

Leachate is one of the significant environmental pollutions in municipal solid waste incineration plants (MSWIP), requiring efficient treatment to achieve the discharge standard. Upon treatment of this leachate from MSWIP in the MBR, the resultant effluent contains a large amount of recalcitrant organic pollutants characterized by the high concentration of chemical oxygen demand (COD) and resists biological degradation. Thus, the electrochemical oxidation technology to oxidize such pollutants was introduced. This study studied the degradation of organic contaminants in leachate from the MBR effluent in MSWIP. Electrochemical oxidation (EO) degradation was stimulated in a batch electrochemical reactor employing graphite carbon electrodes as anode and cathode without an electrolyte. The effect of working variables such as current density, electrolysis time (up to 240 min), the inter-electrode distance between the electrode and initial solution pH were studied. The COD removal rate of 83% was obtained at the current density of 0.8 A/m2, and an operation time of 180 min. The optimal inter-electrode distances and the initial pH were 2cm and 5.04, respectively. It was found that the elevated current densities, high electrolysis time, and alkaline conditions substantially affected the COD removal efficiency. The EO is a useful technology for the degradation of recalcitrant organic pollutants in the MBR effluent for leachate treatmen

Magnetic properties and magnetocaloric effect of NdMn2−xTixSi2 compounds

The structural and magnetic properties of the intermetallic compounds NdMn2−xTixSi2(x = 0, 0.1, 0.2, and 0.3) have been studied by x-ray and high resolution neutron powder diffraction, specific heat, dc magnetization, and differential scanning calorimetry measurements over the temperature range 3–450 K. The Curie temperature and Néel temperature of NdMn2Si2 decrease from TC = 36 K and TN = 380 K to TC = 14 K and TN = 360 K, respectively, on substitution of Ti (x = 0.3) for Mn. The magnetocaloric effect at the first order ferromagnetic phase transition at TC, has been investigated in detail. Under a change of magnetic field of 0–5 T, the maximum value of the magnetic entropy change is 27 J kg−1 K−1 at x = 0, reducing to 15.3 J kg−1 K−1 at x = 0.1 and 10 J kg−1 K−1 at x = 0.3; importantly, no thermal or field hysteresis losses occur (eliminated from 0.3 K and 28.5 J kg−1 at x = 0 around TC) with increase in Ti concentration. Combined with the lack of any hysteresis effects, these findings indicate that NdMn1.9Ti0.1Si2 compound offers potential as a candidate for magnetic refrigerator applications in the temperature region below 35 K

Studies on Lewis-Acid Induced Reactions of 8-Methoxy[2.2]metacyclophanes: A New Synthetic Route to Alkylated Pyrenes

Anti-8-methoxy[2.2]metacyclophanes (MCPs) 5 a–b were obtained via pyrolysis of the corresponding syn-thiatetraoxide cyclophanes 4 a–b. Coupling reactions of 4-tert-butyl-1-methoxy-2,6-bis(mercaptomethyl)benzenes 1 a–b and 1,5-bis(chloro-methyl)-2,4-dimethylbenzene 2 under high dilution conditions afforded only the syn-conformers of 9-methoxy-2,11-dithia[3.3]metacyclophanes 3 a–b, which with m-CPBA formed the corresponding syn-tetraoxides 4 a–b. Lewis acid (TICl4/AlCl3-MeNO2) or iodine-catalyzed reactions of 5 b under various conditions led to transannular cyclization to afford tetrahydropyrene 6 b and pyrene derivative 7 b and/or de-tert-butylated 6 a. Iodine-catalyzed reaction of 5 a afforded tetrahydropyrene 6 a. These findings suggest the potential for a new route to alkylated pyrenes via strained and alkylated metacyclophanes. Density functional theory (DFT) studies were carried out to investigate the conformational characteristics of 3–5

Magnetic phase transitions and entropy change in layered NdMn1.7Cr0.3Si2

A giant magnetocaloric effect has been observed around the Curie temperature, TC ∼ 42 K, in NdMn1.7Cr0.3Si2 with no discernible thermal and magnetic hysteresis losses. Below 400 K, three magnetic phase transitions take place around 380 K, 320 K and 42 K. Detailed high resolution synchrotron and neutron powder diffraction (10-400 K) confirmed the magnetic transitions and phases as follows: TN intra ∼ 380 K denotes the transition from paramagnetism to intralayer antiferromagnetism (AFl), TN inter ∼ 320 K represents the transition from the AFl structure to the canted antiferromagnetic spin structure (AFmc), while TC ∼ 42 K denotes the first order magnetic transition from AFmc to canted ferromagnetism (Fmc + F(Nd)) due to ordering of the Mn and Nd sub-lattices. The maximum values of the magnetic entropy change and the adiabatic temperature change, around TC for a field change of 5 T are evaluated to be −ΔSM max ∼ 15.9 J kg−1 K−1 and ΔTad max ∼ 5 K, respectively. The first order magnetic transition associated with the low levels of hysteresis losses (therma

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites

Analysis of satellite-telemetry data mostly occurs long after it has been collected, due to the time and effort needed to collate and interpret such material. Such delayed reporting does reduce the usefulness of such data for nature conservation when timely information about animal movements is required. To counter this problem we present a novel approach which combines automated analysis of satellite-telemetry data with rapid communication of insights derived from such data. A relatively simple algorithm (comprising speed of movement and turning angle calculated from fixes), allowed instantaneous detection of excursions away from settlement areas and automated calculation of home ranges on the remaining data Automating the detection of both excursions and home range calculations enabled us to disseminate ecological insights from satellite-tag data instantaneously through a dedicated web portal to inform conservationists and wider audiences. We recommend automated analysis, interpretation and communication of satellite tag and other ecological data to advance nature conservation research and practice