Nappies, books and wrinkles: How children, qualifications and age affect female underemployment in Australia

This study examines the determinants of underemployment among part-time employed Australian females, accounting for the interaction of their age, educational qualifications and offspring presence. Females who are young, tertiary educated and without offspring are likeliest to be underemployed, and in general the presence of children lowers the probability of underemployment for those aged below 35, but this impact ameliorates significantly for those aged 35 and above. Policies to address female underemployment need to account for the fact that there is no representative ‘average female worker’, so as to ensure requisite policies better target those most at risk of underemployment. This finding holds for those working either minimum or maximum hours within the part-time spectrum, where greater uniformity of underemployment is found. Age of offspring affects the likelihood of underemployment with younger offspring reducing the incidence of a mismatch between preferred and actual hours, while the opposite holds for older offspring.

Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM

Power domain non-orthogonal multiple access (PD-NOMA) is one of the most perspective multiplexing technologies that allows improving the capacity of actual networks. Unlike orthogonal multiple access (OMA), the PD-NOMA non-orthogonally schedules multiple users in the power domain in the same orthogonal time-spectrum resource segment. Thus, a non-orthogonal multiplexed signal is a combination of several user signals (usually, modulation and coding schemes (MCS) based on quadrature amplitude modulation) with different power weights. The symbol error rate (SER) and bit error rate (BER) performances are one of the main quality characteristics of any commutation channel. The issue is that a known analytical expression for BER and SER calculation for conventional OMA cannot be applied in terms of the PD-NOMA. In the following work, we have derived the SER and BER analytical expressions for gray-coded square quadrature amplitude modulation (QAM) user channels that are transmitted in two-user PD-NOMA channel under additive white Gaussian noise (AWGN). Through the simulation, the verification of the provided expressions is presented for four multiplexing configurations with various user power weights and QAM order combinations.

The Forward Modeling for Surface-borehole Observation Responses of Polarized Target

Surface-hole induced polarization method is a typical deep resource exploration technology, which plays an important role in the mineral survey. The traditional surface-hole induced polarization method is mainly to observe a single polarized secondary field. At present, the time spectrum observation of polarization fields is becoming more and more popular, which greatly enriches the interpretation technology of induced polarization data. In this work, the time spectrum induced polarization method is expounded, the decay polarization fields were numerical calculated and analyzed for the typical 2-dimensional geoelectric model. The results show that for a single polarized target, the time spectrum obtained from different azimuth observation responses are basically the same, which can effectively reflect the time-varying characteristics of the polarized fields. The observation responses of polarized target at different depths can still reflect the time spectrum of decay fields. The main change of the observed response of the polarized target closer to the borehole is the response amplitude. The conclusions and simulations of this study can provide working mode for relevant research and reference for similar work.

CAN A NEW DOUBLE PARTICLE DETECTOR-CLYC (CS2LIYCL6:CE) BE USED IN PN LOGGING FOR TIGHT GAS EVALUATION?

Unconventional oil and gas resources, such as tight oil and gas, have become indispensably succeeding energy sources in nowadays. At the stage of exploration, gas saturation is essential for the evaluation of tight formation, which can provide the key parameters for reserves calculation and development plans making. Conventional logging technologies including acoustic logging and resistivity logging have played a role in gas formation identification and evaluation. Besides, inelastic and capture gamma energy spectrum or time spectrum from pulsed neutron logging tools with NaI, BGO, LaCl3, or LaBr3 detectors are used to realize the quantitative evaluation of gas saturation. With the development of nuclear technology, the new detector, called CLYC (Cs2LiYCl6:Ce), can simultaneously measure the signals of gamma ray and thermal neutron, providing a new mean for gas saturation evaluation use pulsed neutron logging technique. The CLYC scintillation crystal with a density of 3.31g/cm3 has an energy resolution in the order of 4%-5% (0.662MeV), and its light output efficiency of gamma ray and neutron are 20000 photons/MeV and 70500 photons/MeV. Meanwhile, its excellent temperature characteristics in the range from -30℃ to 180℃ can fit the downhole environment. Consisting of the D-T neutron source and CLYC detector, the pulsed neutron logging system is designed in this paper, in which the burst gate is 0 to 40 microseconds and the capture gate is 50 to 100 microseconds. To evaluate gas saturation, this system combines the inelastic gamma ray and thermal neutron recorded from the burst gate and the capture gate. The new pulsed neutron logging tool consists of two LaBr3 detectors and a CLYC detector, and the spacing of the CLYC detector is 75cm. In addition to the conventional C/O and Sigma measurement functions, the new instrument can also realize the quantitative evaluation of gas saturation by the CLYC detector. The inelastic gamma, capture gamma, and thermal neutron distribution in long-detector are simulated by the Monte Carlo method under the condition of tight gas saturated formation with porosity from 3% to 20%. Based on the spatial flux distribution characteristic of inelastic gamma and thermal neutron, the new parameter (RGTH) is defined as the ratio of inelastic gamma counts to thermal neutron counts from the CLYC detector to calculate gas saturation. The results imply that RGTH is positively correlated with porosity and negatively correlated with gas saturation, and the gas and water dynamic range is about 36% under the condition of a sandstone formation with 10% porosity. Different lithology has different RGTH benchmark values. RGTH is not affected by the yield of the neutron source and water salinity, and the subtract coefficient can be accurately determined by the time spectrum of the thermal neutron to acquire the pure inelastic gamma. A tight lime-bearing sandstone formation with 5% porosity has been set by MCNP to check validity, the absolute error of gas saturation calculated by RGTH is less than 5%.

Time–connectivity superposition and the gel/glass duality of weak colloidal gels

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.

Advanced Chirp Transform Spectrometer with Novel Digital Pulse Compression Method for Spectrum Detection

Based on chirp transform and pulse compression technology, chirp transform spectrometers (CTSs) can be used to perform high-resolution and real-time spectrum measurements. Nowadays, they are widely applied for weather and astronomical observations. The surface acoustic wave (SAW) filter is a key device for pulse compression. The system performance is significantly affected by the dispersion characteristics match and the large insertion loss of the SAW filters. In this paper, a linear phase sampling and accumulating (LPSA) algorithm was developed to replace the matched filter for fast pulse compression. By selecting and accumulating the sampling points satisfying a specific periodic phase distribution, the intermediate frequency (IF) chirp signal carrying the information of the input signal could be detected and compressed. Spectrum measurements across the entire operational bandwidth could be performed by shifting the fixed sampling points in the time domain. A two-stage frequency resolution subdivision method was also developed for the fast pulse compression of the sparse spectrum, which was shown to significantly improve the calculation speed. The simulation and experiment results demonstrate that the LPSA method can realize fast pulse compression with adequate high amplitude accuracy and frequency resolution. Compared to existing digital pulse compression technology, this method can significantly reduce the number of required calculations, especially for measurements of sparse signals.