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Networks of nanowires, nanotubes, and nanosheets are important for many applications in printed electronics. However, the network conductivity and mobility are usually limited by the resistance between the particles, often referred to as the junction resistance. Minimising the junction resistance has proven to be challenging, partly because it is difficult to measure. Here, we develop a simple model for electrical conduction in networks of 1D or 2D nanomaterials that allows us to extract junction and nanoparticle resistances from particle-size-dependent DC network resistivity data. We find junction resistances in porous networks to scale with nanoparticle resistivity and vary from 5 Ω for silver nanosheets to 24 GΩ for WS2 nanosheets. Moreover, our model allows junction and nanoparticle resistances to be obtained simultaneously from AC impedance spectra of semiconducting nanosheet networks. Through our model, we use the impedance data to directly link the high mobility of aligned networks of electrochemically exfoliated MoS2 nanosheets (≈ 7 cm2 V−1 s−1) to low junction resistances of ∼2.3 MΩ. Temperature-dependent impedance measurements also allow us to comprehensively investigate transport mechanisms within the network and quantitatively differentiate intra-nanosheet phonon-limited bandlike transport from inter-nanosheet hopping. The electrical properties of nanostructured networks are often dominated by junctions between the particles. Here, Gabett et al. develop transport models and utilise impedance spectroscopy to quantify the factors limiting conduction in these systems. [ABSTRACT FROM AUTHOR]

Urbanization, changes in land use and land cover (LULC), and an increase in population collectively have significant impacts on urban catchments. However, a vast majority of LULC studies have been conducted using readily available satellite imagery, which often presents limitations due to its coarse spatial resolution. Such imagery fails to accurately depict the surface characteristics and diverse spectrum of LULC classifications contained within a single pixel. This study focused on the highly urbanized Dry Creek catchment in Adelaide, South Australia and aimed to determine the impact of urbanization on spatiotemporal changes in LULC and its implications for the land surface condition of the catchment. Very high spatial resolution imagery was utilized to examine changes in LULC over the past four decades. Support Vector Machine-learning-based image classification was utilized to classify and identify the changes in LULC over the study area. The classification accuracy showed strong agreement, with a kappa value greater than 0.8. The findings of this analysis showed that extensive urban development, which expanded the built-up area by 34 km2, were responsible for the decline in grass cover by 43.1 km2 over the last 40 years (1979–2019). Moreover, built-up areas, plantation, and water features, in contrast to grass cover, have demonstrated an increasing trend during the study period. The overall urban expansion over the study period was 136.6%. Urbanization intensified impervious area coverage, increasing the runoff coefficient, equivalent impervious area, and curve number by 60.6%, 60.6%, and 7.9%, respectively, while decreasing the retention capacity by 38.6%. These modifications suggest a potential variability in catchment surface runoff, prompting the need for further research to understand the surface runoff changes brought by the changes in LULC resulting from urbanization. The findings of this study can be used for land use planning and flood management. [ABSTRACT FROM AUTHOR]

The species-area relationship (SAR) is widely applied in ecology. Mathematically, it is usually expressed as either a semi-log or power-law relationship, with the former being introduced by Gleason and the latter by Arrhenius. We here resolve the dispute about which form of the SAR to prefer by introducing a novel model that smoothly transforms between the Gleason semi-log (GSL) and Arrhenius power law (APL) forms. The model introduced has the form of lnq (S) = a + z ln A, with lnq being a generalized logarithmic function, which is a linear map (y = x) for q = 0 and a logarithmic map (y = ln x) for q = 1 and q can take any intermediate value between 0 and 1. We applied this model to 100 datasets (mostly islands), linking species richness to island area. The APL was the preferred model in 68% of head-to-head comparisons with the GSL. Both models were supported in 40% of cases. In just under half (44%) of the cases, an intermediate model best explained the data. The results demonstrate the utility of a simple intermediate SAR model. Visualizing the profile of the range of model fits for all q ∈ [0, 1] (a q chart) allows us to gain extra insight into SARs not yielded by head-to-head comparisons of GSL and APL. The mathematics related to the generalized logarithmic function introduced here should have applications to other areas of ecological modelling. [ABSTRACT FROM AUTHOR]

Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model's output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons. [ABSTRACT FROM AUTHOR]

There is an increasing demand for cooling cities because of its importance on human health and the quality of life in outdoor urban spaces. However, the development of methods in improving outdoor thermal comfort and zoning cities based on outdoor thermal comfort is still challenging. In this work, we propose a new approach to cities zoning from the lens of outdoor thermal comfort in the arid climate of the city of Mashhad, Iran, and investigate the impacts of urban form characteristics on pedestrian thermal comfort. The effects of complex urban form parameters including height to width (H/W) ratio, canyon orientation, tree canopy cover, and building surface materials on the thermal comfort of pedestrians were studied in the arid climate of Mashhad. Microclimate simulation and analysis is conducted in ENVI-met software, and ArcMap is used to calculate Mashhad urban heat islands. Path analysis in SPSS presents an urban form formulation, which predicts approximate outdoor thermal comfort condition in current and future urban context of Mashhad and other cities with the same climate. We finally demonstrate the use of our research method as an alternative method for all cities: urban heat island (UHI) zoning can be used as a substitute for urban form zonings based on outdoor thermal comfort, especially in large cities where data collection on urban form can be difficult due to limited time and resources. [ABSTRACT FROM AUTHOR]

Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS2) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ1 = 27.4 ms, τ2 = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort is influenced by people's climate expectations, perceptions and adaptation capacity. Varied individual response to comfortable or stressful thermal environments results in a deviation between actual outdoor thermal activity choices and those predicted by thermal comfort indices. This paper presents a passive activity observation (PAO) method for estimating contextual limits of outdoor thermal adaptation. The PAO method determines which thermal environment result in statistically meaningful changes may occur in outdoor activity patterns, and it estimates thresholds of outdoor thermal neutrality and limits of thermal adaptation in public space based on activity observation and microclimate field measurement. Applications of the PAO method have been demonstrated in Adelaide, Melbourne and Sydney, where outdoor activities were analysed against outdoor thermal comfort indices between 2013 and 2014. Adjusted apparent temperature (aAT), adaptive predicted mean vote (aPMV), outdoor standard effective temperature (OUT_SET), physiological equivalent temperature (PET) and universal thermal comfort index (UTCI) are calculated from the PAO data. Using the PAO method, the high threshold of outdoor thermal neutrality was observed between 24 °C for optional activities and 34 °C for necessary activities (UTCI scale). Meanwhile, the ultimate limit of thermal adaptation in uncontrolled public spaces is estimated to be between 28 °C for social activities and 48 °C for necessary activities. Normalised results indicate that city-wide high thresholds for outdoor thermal neutrality vary from 25 °C in Melbourne to 26 °C in Sydney and 30 °C in Adelaide. The PAO method is a relatively fast and localised method for measuring limits of outdoor thermal adaptation and effectively informs urban design and policy making in the context of climate change. [ABSTRACT FROM AUTHOR]

Solar radiation components assessment is a highly required activity for solar energy applications. Despite their importance, diffuse and direct solar radiation components are not available in many locations in the world. This is specifically the case in the studied region due to high fiscal demands and technical limitations. In the present work, our main objective is to estimate the two components using global solar radiation components as the only measured input parameter. Machine learning (ML) techniques seem to be a good solution of such an estimation problem but the main issue of ML techniques is the need of long historical data of both desired outputs, direct and diffuse, to build the optimum model in the training phase. However, in the present study, we adopt the use of the Boland–Ridley–Lauret (BRL) model to deal with the problem of estimation of the direct and diffuse components from the global radiation. The adjusted BRL model was applied on two time scales, daily and hourly components. It was found that the estimated direct and diffuse solar radiation values by the adjusted BRL model are in favorable agreement with the measured data. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s10666-023-09873-6. Equation (5) which currently reads 5 HT ht Graph Should instead read, as it did in the original manuscript submission, 5 HT ht Graph The original article has been corrected. [Extracted from the article]

This study aims to test the appropriateness of multivariate skew- t copula and checkerboard copula of maximum entropy in generating monthly rainfall total data. The generation of synthetic data is important, as it provides hypothetical data in areas for which data availability remains limited. Three selected meteorological stations in Kelantan, Malaysia, Stesen Pertanian Melor, Rumah Pam Salor, and Ladang Lepan Kabu, are considered in this study. Monthly rainfall total data for the driest and wettest months in the year are tested in this study. For these three stations, the identified month with the least total of rainfall received (driest) is May, while the month with the highest total of rainfall received (wettest) is November. The data is fitted to gamma distribution with the corresponding parameters estimated. The observed data will be transformed to be in unit uniform using the gamma marginal. The resulting data is compared to simulated uniform data generated using multivariate skew- t copula and checkerboard copula of maximum entropy models based on the correlation values of the observed and simulated data. Next, the Kolmogorov-Smirnov test is used to assess the fit between the observed and generated data. The results show that the values of simulated correlation coefficients do not differ much for gamma distribution, multivariate skew- t, and maximum entropy approaches. This implies that the multivariate skew- t and maximum entropy may be used to generate monthly rainfall total for cases in which actual data is unavailable. [ABSTRACT FROM AUTHOR]

In publicly available waste reports only the totals of waste produced for municipal, or industry waste typically feature. The types of waste generated and the generating industry sector are usually omitted. We propose the direct inputs waste estimation methodology to create a detailed estimate of municipal solid waste and industrial solid waste for an economy (including sectoral and waste type disaggregation) using a top-down estimation methodology that links the aforementioned limited publicly available waste data with an input-output table's direct inputs ( A) matrix. We then provide an application of the direct inputs waste estimation methodology upon the 2008 waste generation of Australia resulting in a 344 industry sector and 14 waste type data set. The resulting estimation gives unique insights into Australian waste generation; including the large C&I tonnages of waste estimated to be produced from the Service sectors such as the Education, Hospitality, and Health sectors as well as the large amount of food waste produced throughout the economy. [ABSTRACT FROM AUTHOR]

Although heatwave-related excess mortality and morbidity have been widely studied, results are not comparable spatially and often longitudinally because of different heatwave definitions applied. The excess heat factor (EHF) quantifies heatwave intensity relative to the local climate, enabling cross-regional comparisons. Previous studies have shown a strong relationship between EHFs and daily mortality during severe heatwaves. An extensive study about the relationship between EHFs and daily morbidity compared to the currently applied heatwave thresholds in Adelaide has not yet been undertaken. This paper analyzes the association of EHFs with daily morbidity between 2008 and 2014 in the Adelaide metropolitan region, South Australia, and probes three different approaches to calculate the EHF. The EHF is found to differentiate days with heatwave-related excess morbidity significantly better than other widely used weather parameters, resulting in fewer days per year with heatwave alerts than using previously proposed methods. The volume of excess morbidity can be predicted by the EHF more reliably with a model proposed for the SA Ambulance Service to support their heatwave preparation plan. [ABSTRACT FROM AUTHOR]

In the financial year 2011-2012, wind farms supplied 26 % of South Australia's electricity demand according to the Australian Energy Market Operator's report. This contribution has risen from zero in 2003. The operation of the electricity grid depends heavily on knowledge of the variability of supply. Wind farm output displays similar conditional volatility as financial market variables. In this paper, a new method of estimating wind farm output volatility on a 5-min time scale is developed through the use of higher-frequency wind farm output data. First, an autoregressive model for the high-frequency data is developed, and it is used to derive a volatility measure for 5-min data. The results are also true in certain general situations when the high-frequency data follow an autoregressive moving average process or exhibits long memory features. The methods described here are analogous to realised volatility measures used in financial series, except that wind farm output data are measured at uniform intervals, unlike random trading times for financial transactions. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, ). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, ), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. [ABSTRACT FROM AUTHOR]

Fully exploiting the properties of graphene will require a method for the mass production of this remarkable material. Two main routes are possible: large-scale growth or large-scale exfoliation. Here, we demonstrate graphene dispersions with concentrations up to ∼0.01 mg ml−1, produced by dispersion and exfoliation of graphite in organic solvents such as N-methyl-pyrrolidone. This is possible because the energy required to exfoliate graphene is balanced by the solvent–graphene interaction for solvents whose surface energies match that of graphene. We confirm the presence of individual graphene sheets by Raman spectroscopy, transmission electron microscopy and electron diffraction. Our method results in a monolayer yield of ∼1 wt%, which could potentially be improved to 7–12 wt% with further processing. The absence of defects or oxides is confirmed by X-ray photoelectron, infrared and Raman spectroscopies. We are able to produce semi-transparent conducting films and conducting composites. Solution processing of graphene opens up a range of potential large-area applications, from device and sensor fabrication to liquid-phase chemistry. [ABSTRACT FROM AUTHOR]

Nanowires have attracted considerable interest as nanoscale interconnects and as the active components of both electronic and electromechanical devices. Nanomechanical measurements are a challenge, but remain key to the development and processing of novel nanowire-based devices. Here, we report a general method to measure the spectrum of nanowire mechanical properties based on nanowire bending under the lateral load from an atomic force microscope tip. We find that for Au nanowires, Young's modulus is essentially independent of diameter, whereas the yield strength is largest for the smallest diameter wires, with strengths up to 100 times that of bulk materials, and substantially larger than that reported for bulk nanocrystalline metals (BNMs). In contrast to BNMs, nanowire plasticity is characterized by strain-hardening, demonstrating that dislocation motion and pile-up is still operative down to diameters of 40 nm. Possible origins for the different mechanical properties of nanowires and BNMs are discussed. [ABSTRACT FROM AUTHOR]

Five classes of zero–one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand for services is analogous to the problem of selecting reserves to support species diversity. The classes of models include the set covering and maximal covering models, as well as models for backup and redundant coverage. Issues of reliability and uncertainty are addressed by chance constrained covering models and maximal expected covering models. Exact and heuristic solution approaches are discussed. Multi-objective and economic issues are considered. [ABSTRACT FROM AUTHOR]

Focuses on the greenhouse gas abatement cost and benefit projections of emission reduction through 2100. Effect of the emission of greenhouse gases into the atmosphere; Estimated damages caused by carbon dioxide doubling; Analysis of abatement costs.

Focuses on the impact of climate change on urban water management in Washington D.C. Feasibility of a scenario approach to describing possible changes in climate; Effectiveness of conservation and pricing interventions.

The article offers information on the emulation of touch by electronic means. It states that high spatial-resolution and pressure-sensitive artifical skins need to be developed for touch emulation. It also mentions several benefits of using artificial skin including development of precision tools with advanced skills, and amalgamation of touch capacity into flexible and scrollable display technologies.

The article presents a study which revealed the impact of the surface effects in thin silicon films on the transport of charge carriers and on device performance. The result was considered as a new opportunity for device physics. Researchers added that the position of the Fermi energy is still controlled during silicon doping. Thus, a need to develop a high-resolution technique for the implantation of dopant ions on semiconductor materials is needed.

Considerable attention has been drawn to the lead halide perovskites (LHPs) because of their outstanding optoelectronic characteristics. LHP nanosheets (NSs) grown from single crystalline lead halide possess advantages in device applications as they provide the possibility for control over morphology, composition, and crystallinity. Here, free-standing lead bromide (PbBr2) single-crystalline NSs with sizes up to one centimeter are synthesized from solution. These NSs can be converted to LHP while maintaining the NS morphology. We demonstrate that these perovskite NSs can be processed directly for fabrication of photodetector and laser arrays on a large scale. This strategy will allow high-yield synthesis of large-size perovskite NSs for functional devices in an integrated photonics platform. [ABSTRACT FROM AUTHOR]

Networks of metallic nanowires have the potential to meet the needs of next-generation device technologies that require flexible transparent conductors. At present, there does not exist a first principles model capable of predicting the electro-optical performance of a nanowire network. Here we combine an electrical model derived from fundamental material properties and electrical equations with an optical model based on Mie theory scattering of light by small particles. This approach enables the generation of analogues for any nanowire network and then accurately predicts, without the use of fitting factors, the optical transmittance and sheet resistance of the transparent electrode. Predictions are validated using experimental data from the literature of networks comprised of a wide range of aspect ratios (nanowire length/diameter). The separation of the contributions of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated and provides a benchmark for the minimum attainable sheet resistance. The predictive power of this model enables a material-by-design approach, whereby suitable systems can be prescribed for targeted technology applications. [ABSTRACT FROM AUTHOR]

Extinction spectra of nanomaterial suspensions can be dominated by light scattering, hampering quantitative spectral analysis. No simple models exist for the wavelength-dependence of the scattering coefficients in suspensions of arbitrary-sized, high-aspect-ratio nanoparticles. Here, suspensions of BN, talc, GaS, Ni(OH)2, Mg(OH)2 and Cu(OH)2 nanosheets are used to explore non-resonant scattering in wide-bandgap 2D nanomaterials. Using an integrating sphere, scattering coefficient (σ) spectra were measured for a number of size-selected fractions for each nanosheet type. Generally, σ scales as a power-law with wavelength in the non-resonant regime: σ(λ)∝[λ/〈L〉]−m, where 〈L〉 is the mean nanosheet length. For all materials, the scattering exponent, m, forms a master-curve, transitioning from m = 4 to m = 2, as the characteristic nanosheet area increases, indicating a transition from Rayleigh to van der Hulst scattering. In addition, once material density and refractive index are factored out, the proportionality constant relating σ to [λ/〈L〉]−m, also forms a master-curve when plotted versus 〈L〉. [ABSTRACT FROM AUTHOR]

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a “winner-takes-all” conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing. Nanowire networks with memristive properties are promising for neuromorphic applications. Here, the authors observe the formation of a preferred conduction pathway which uses the lowest possible energy to get through the network and could be exploited for the design of optimal brain-inspired devices. [ABSTRACT FROM AUTHOR]