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The measurement of site amplification of microtremors is an important tool for evaluating seismic hazard at local and regional level. The H/V spectral ratio (HVSR) method is applied to estimate the amplification of ground motion at resonance frequency. The fundamental resonance frequency is estimated at 8 different sites in study region and shows the maximum vulnerability of the site to the earthquakes. This frequency ranges between a minimum of 2.8 Hz to a maximum of 9.3 Hz. The thickness of the sediments below the surface of recording sites is calculated using the empirical relationship that comes out to be in a particular range of 9.5 m–33.1 m above the bed rock. The sedimentary thickness and the resonance frequency clearly matched with geological setting of the study area. The observation shows a slow reduction in the thickness of the sediments and increase in the resonance frequency from South-western part to North-eastern part of the study region. The results show that most of the sites are highly vulnerable to seismic risks with high amplification of seismic waves in the area. Thus, current study provides a deep insight in to the seismic hazard of the Sikkim region with a clear emphasis on the site amplification and the sedimentary cover for the region.

The impact of the rigid flora on longitudinal dispersion was examined in the laboratory channel located at Thapar University Patiala. The total of nine rigid plant density used with a varying diameter of 12, 16, and 20 mm. The total 27 velocities taken and the discharge is calculated using calibrated v-notch. Rhodamine dye is used as a tracer and injected in upstream of flow and collection of a sample every 1 m distance in downstream of the canal. The dispersion coefficient stands influenced by the diameter of the rigid flora. As the diameter of rigid vegetation is increased the dispersion coefficient increases and declines with increases in the density of the flora. In vegetation, the longitudinal scatter is reduced as the vegetation density increases, with a decrease of about 30% to 50% when the density is doubled. In the pollution aspect, the speed of cloud and dispersion play a vital role and as the Reynolds number value is large the speed of cloud is slow and vice-versa. The results of the experiences gained in this study are compared with the empirical relationship of different researchers. Also, the equation is developed for calculating the dispersion coefficient with rigid vegetation in the flow of the channel of varying density and diameter.

Much of traffic flow theory and highway design is based upon the fundamental relationship, FR, between speed, flow, and density. Traditionally, the FR is assumed to be a static curve over time and space. This paper presents new empirical findings that show the FR in one lane of a freeway can actually change shape in a rapid and systematic response to conditions in the adjacent lanes. This paper demonstrates the dependencies for a general purpose freeway lane that is operating in the congested regime. The drivers become more conservative in response to slower speeds in either adjacent lane, conversely, they become less conservative in response to higher speeds in either adjacent lane. It is also shown that as drivers become more conservative the signals and waves move upstream slower. So this inter-lane dependency has a direct impact on how fast signals propagate through the traffic stream. For the study site the empirical relationship between the wave speed and relative speed is close to linear. This interdependence between lanes is an important factor that it is absent from most traffic flow and car following models. Given these findings the discussion explores hypothetical examples showing how the inter-lane dependencies can give rise to mechanisms for waves in adjacent lanes to synchronize, as well as a mechanism for a queue spreading from one lane to another without any lane change maneuvers. Finally, this work closes with an exploration of the possible physiological mechanisms that might be occurring in the drivers.

Using mostly theoretical models and traditional risk/uncertainty measures (VIX index, panic, precaution, scary bad news, etc.), the current literature tries to clarify the risk/uncertainty-deleveraging pattern. The findings are not sufficient to explain the dynamic empirical relationship between modern risk/uncertainty indicators and leverage. We fill this gap in the literature by using US quarterly data, from 1985:1 to 2018:4, Granger causality tests, and a structural vector autoregression model. We find that commercial bank leverage rises when geopolitical risk and macroeconomic, policy, and equity uncertainty increase. Client-based business relationships of banks and high government borrowing from banks during crises periods are responsible for this relationship. We find that the leverage of broker-dealers and shadow banks declines when Chicago risk and macroeconomic, policy, financial, and equity uncertainty increase. We argue that the vulnerability of broker-dealers and shadow banks to the risk/uncertainty of the entire market system is responsible for this relationship., Highlights • Client-based business makes commercial banks less vulnerable to risk and uncertainty. • Commercial bank leverage does not decline when risk and uncertainty increase. • The value of broker-dealer and shadow bank asssets is prone to risk and uncertainty. • Broker-dealer and shadow bank leverage falls significantly when risk/uncertainty rise. • The Fed should provide clear signals about its future actions to prevent deleveraging.

We investigate the empirical relationship between liquidity costs and Post Earnings Announcement Drift, using a sample of 93 zero-leverage firms listed on the FTSE 350 index over the time period 2000–2015. We discover that illiquidity levels are significantly enhanced around both the earnings and post announcement period, in both the short and long run. Once we decompose the bid-ask spread components during the earnings announcements, we observe that the adverse selection costs are significantly increased, whereas the inventory holding and order processing costs remain unchanged. We conclude that Post Earnings Announcement Drift is related with information asymmetry for zero-leverage firms.

ASTM C1678 describes the state-of-the-art's fractographic techniques to estimate the fracture strength of glasses and ceramics through empirical, strength vs. fracture mirror length relationships. However, the methodology is subjective and only applicable to a few loading scenarios and relatively pristine fracture surfaces. This work presents a semi-automated, alternative approach to objectively estimate the strength of silicate glasses for ampler loading and geometric scenarios. The proposed method relies on a baseline set of fracture surface profilometry-scans gathered on samples of known strengths. A computer vision-based algorithm compares relevant, topological features extracted from the baseline set to the features on the fracture surfaces investigated. An empirical relationship based on over 2,100 fractured silicate specimens is used to compute the strength of the trial sample. The proposed scheme could accurately estimate the strength of specimens beyond the capacity of ASTM C1678, such as in chemically strengthened glasses and fracture surfaces displaying significant damage.

The empirical relationship between annual daily maximum temperature (ADMT) and annual daily maximum rainfall (ADMR) was investigated. The data were collected from four weather stations located in Adelaide, South Australia, from 1988 to 2017. Due to the influence of sea surface temperature on rainfall and temperature, the distance from the weather station to the sea was considered in the selection of weather stations. Two weather stations near the sea and two inland weather stations were selected. Three non-parametric statistical tests (Kruskal–Wallis, Mann–Whitney, and correlation) were applied to perform statistical analysis on the ADMT and ADMR data. It was revealed that the temperature and rainfall in South Australia varies according to weather station location. The distance from the sea to the weather station was found to have limited influence on temperature and rainfall. Meanwhile, with the 0.05 level of significance, the association between ADMT and ADMR near sea stations is not as significant as the association between the two inland weather stations. It is relatively unrealistic to use ADMR to predict ADMT, or vice versa, since their correlation is not statistically significant (Spearman’s rank correlation coefficient: −0.106). Refereed/Peer-reviewed

The Ergun equation is commonly used to predict velocity-pressure drop behaviour in packed beds. For many particular systems, the measurements deviate significantly from this relationship. Because of this, the equation is often treated as a purely empirical relationship, and its factors as fit parameters. This paper shows that the physics-based model underlying the Ergun equation should be treated by careful investigation of the included parameters. It is shown that discrepancies between experiment and theory for particle collectives in the millimetre range and above are caused by insufficient methods to determine the specific surface area. This effect is especially large for coarse particles with very rough surfaces. As a 2D measurement method, dynamic image analysis can not provide a good measure of specific surface area. It does not account for both macroscale particle shape and microscale surface roughness, which is confirmed by an analysis of 3D particle measurements from X-ray microscopy.

In this paper, a rational method for estimating the unit weight of cement-treated clay is proposed, which takes into account the relationship between the various phases and changes in these phases during curing. The simplest unit weight estimate is the unreacted unit weight which may be used as a lower-bound estimate of the cured unit weight. More accurate estimates of the unit-weight are possible by considering the effects of hydration of the cement-water paste during curing period, using the “drained” and “undrained” estimates of the unit weights. The drained and undrained assumptions refer to the drainage conditions during curing. Both are consistently closer to the measured unit weights than the unreacted unit weight, with undrained assumption having a slightly better agreement. An empirical relationship is also proposed for estimating the effect of the change of volume arising from consolidation during curing. This may be significant if the treated soil layer lies deep in the ground, where the effective stress levels are high. Comparison with unit weight measurements from a soil mixing project indicate that the calculated unit weight agrees well with the range of unit weight measurements for soft clay. For the loose sand, the calculated unit weight is higher than the measured unit weight, possibly owing to the dilation of sand when sheared, ahead of the mixing shaft.

Encouraging multimodality as a tool to reduce exclusive car use is seen as a key ingredient of transport policies aimed at reducing greenhouse gases emissions, such as CO2. These policies are based on the assumption that increasing multimodality will contribute to a reduction in emissions. Yet, hardly any scientific attention has been paid to the empirical relationship between multimodality and CO2 emissions. This article addresses this unexplored question at both the individual and trip level using the English National Travel Survey. We find that the level of multimodality is only weakly associated with CO2 emissions. It is only when controlling for levels of travel activity (trip frequency, total distance travelled) that a moderate association in the expected direction is observed (i.e. that higher levels of multimodality correspond with lower CO2 emissions). This suggests that greater levels of travel activity among multimodals tend to offset the benefits derived from their more diverse modal choices. Similar patterns emerge from the trip-level analyses: higher emissions are found for (the typically longer) multimodal trips compared to unimodal trips, even when the only mode used is the car. However, for trips over similar distances, multimodal trips do have lower emissions. While there is merit in encouraging greater multimodality, this can hardly be the only or primary goal of climate mitigation policies in the transport sector. More attention needs to be paid to the key role of high levels of travel activity, and how these could be reduced.

The scale effect of rock joint shearing is of great significance in rock engineering. Most existing shear constitutive models could describe the pre- and post-peak deformation of rock joints, but only in one particular scale, that is, those existing models will fail to depict the rock joint shearing in different length scales. Therefore, this study aims to establish a constitutive relationship for rock joints with considering the scale effect. Based on the assumption of a random statistical distribution of rock material strength and statistical mesoscopic damage theory, damage variables are defined as the ratio of the number of damaged elements to the total number in the shear process. Together with the nonlinear relationship between the microelement failure and the joint scale, an empirical statistical constitutive relationship for joint is established. And then, the determination method of constitutive relationship parameters and the variation laws with the scale are discussed. Results show that the predicted results of the proposed empirical relationship not only agree well with the experimental results but also fully describe nonlinear deformation, pre-peak softening, post-peak softening, residual stage, and other mechanical properties of the shear deformation of joint with different dimensions, thereby demonstrating the rationality of the constitutive relationship. The physical meaning of the constitutive relationship parameters is clear, and the expressions of the constitutive relationship parameters can be deduced from the experimental results. In addition, the influence of scale effect on the shear deformation of rock joints can be quantified using parameters, which help accurately describe the action form of scale effect.

Suction in subgrade layer reaches an equilibrium condition several years after construction of the pavement. The equilibrium suction has a direct influence on the resilient modulus of subgrade soil. In the current mechanistic-empirical pavement design guide, equilibrium suction is determined based on the empirical relationship with water table depth. However studies have shown that other critical factors also influence equilibrium suction. In this study, a new mechanistic-empirical model is presented to predict equilibrium suction considering the effects of physical properties of the soil and climatic factors. A simple regression model is also generated to predict the equilibrium suction from readily available parameters i.e., Thornthwaite moisture index (TMI), plasticity index (PI), and dry suction value at surface ( u dry ). In the final section of this paper, the effects of TMI and vegetation on equilibrium suction are evaluated for various soil classes and TMI ranges, respectively. A contour map of the equilibrium suction values for the entire continental United States has been generated using the geographic information system (GIS) platform and followed by the model predictions.

This paper presents experimental results on temporal developments of local scour below a partially buried pipeline under oblique currents/waves. The effects of the flow incident angle (α ≤ 45°, α = 0° denotes that the currents/waves are perpendicular to the pipeline) and the embedment-to-diameter ratio (e/D ≤ 0.5, e/D = 0 denotes no embedment) on the equilibrium depth and time scale of scour were quantitatively investigated. The experimental results indicate that with the increase of the embedment-to-diameter ratio from 0 to 0.5, the equilibrium depth of scour decreases by up to 30 and the time scale increases by approximately 5 times. With the increase of the flow incident angle from 0° to 45°, the equilibrium depth decreases by approximately 25% and the time scale increases by approximately 0.7 times. A uniform empirical relationship between the equilibrium depth for arbitrary conditions (S, i.e., for α > 0° or e/D > 0) and that for a conventional case (S0, for α = 0° and e/D = 0) is newly proposed for both current and wave conditions based on theoretical derivation and curve fitting to the experimental data, where S0 can be easily predicted from existing empirical formulae. The correlation analysis indicates that the experimental results of the equilibrium depth can be well represented by the newly proposed formula. For predicting the time scale of scour process, a theoretical approach based on the erosion rate of sediment, which was originally proposed for a conventional case, is expanded to arbitrary conditions. It is indicated that the time scales of scour obtained from the theoretical approach are well coincident with the experimental values. Finally, an empirical relationship between the time scale for arbitrary conditions (T) and that for a conventional case (T0) was established based on the theoretical derivation. The newly proposed empirical formula is found to provide satisfactory predictions of the time scale compared with the present experimental results and those published.

The main goal of this paper is to analyze the empirical relationship between economic development and intergenerational education mobility. By exploiting large regional variation in the level of economic development across Turkey, we find that educational outcomes of daughters who grow up in more developed regions depend less on their parents’ educational outcomes. We do not find evidence for a similar relationship between development and mobility for males. Moreover, the development level of place of residence during childhood has a stronger positive association with intergenerational education mobility compared to the development level of place of residence during adolesence. We find that regions that feature higher school availability, greater level of social capital, more favorable cultural attitudes toward women, higher level of home resources, and lower degree of educational inequalities also tend to feature higher degree of intergenerational mobility.

Numerous attempts have been conducted to construct a reliable model to relate petrophysical and geomechanical characteristics of shale to geochemical properties. In this study, continuous logs were built to predict the thermal maturity of the Bakken Formation from a set of petrophysical logs. First, dynamic modulus (Ed) was created from shear and compressional sonic and, density logs and then converted to Ei (nanoindentation based Young's modulus) using the existing experimental relationship between Ed and Ei. Next, continuous TOC logs were generated for the Bakken Shales based on an empirical relationship between well log derived Ei and TOC contents that were measured in the lab. There was found an acceptable difference between estimated log-based TOC and measured values in three separate wells. Solid bitumen reflectance as a reliable indicator of thermal maturity was measured on all samples from the same wells in the scarcity/absence of vitrinite maceral. The relationships between GR/NPHI/RHOZ wireline logs versus solid bitumen reflectance (BRO%) as the maturity index was then investigated in twelve wells. Based on the strength or weight of the relationship between each log and BRO%, a new parameter “Ʈ” was defined to represent thermal maturity from well logs. A good agreement between parameter Ʈ and BRO% values was observed which provided an empirical equation to make the estimation of BRO% values for the whole length of the shale members possible. Ultimately, the continuity of BRO% values, would enable us to establish a relationship between BRO% and measured Tmax values to generate continuous Tmax logs for the Bakken Shale members. Using these newly developed well logs, it would become possible to make a 3D property models for BRO% and Tmax (the thermal maturity property cube) for an accurate petroleum system evaluation based on GR/NPHI/RHOZ logs.

Underwater video cameras can be used for fisheries, aquacultures, and marine ecology to monitor the existence of fish. In order to assess marine structures, monitoring is also important to understand the effects on the local fish community. Underwater vision is sometimes degraded due to the condition of the water and sunlight. In particular, horizontal visibility is not easily obtained with regard to water quality. So far, an empirical relationship with horizontal visibility has been described according divers' vision, and it states that divers' horizontal visibility is approximately equal to 0.7 times the Secchi depth (Wright and Colling, 1995). If the video camera has a high resolution, visibility may be similar, however, if the camera has a low resolution, detectable distance can deteriorate. For practical usage, a video camera with low resolution has the advantage of reducing storage of data and it is easy to transmit video remotely, in other words, real-time monitoring. Real-time data will provide a prompt understanding of aquatic environmental changes. In order to obtain such data, a low-resolution video camera is still required for usage in a field. There are few models that describe horizontal visibility using a camera in water, therefore this study aims to model one. This paper showed a new approach to model horizontal visibility by water depth and Secchi depth, which are practical and obtainable parameters for underwater monitoring. In addition to this, global solar radiation is adopted for modeling. The model is expressed as relative contrast between target and background. Based on two observations near the sea surface (less than Secchi depth), the model is created. The model showed plausible horizontal visibility using an underwater video camera for real-time monitoring of fish and marine environment.

This paper examines the empirical relationship between cash holding and investment behavior when problem directors are on the Board. We argue that problem directors provide lower quality (weak) corporate governance which encourages excess cash holdings. The findings show consistent evidence that firms with at least one problem director hold more cash. In addition, evidence is found that firms with higher cash holdings engage in over-investment and such behavior is more pronounced when problem directors are on the board. This study contributes to the limited research on the professional history of directors and provides evidence of the financial effects for firms served by problem directors.

The estimation of leaf biochemical constituents is of high interest for the physiological and ecological applications of remote sensing. The multispectral lidar (MSL) system emerges as a promising active remote sensing technology with the ability to acquire both three-dimensional and spectral characteristics of targets. The detection wavelengths of the MSL system can be geared toward the specific application purposes. Therefore, it’s important to conduct the wavelength selection work to maximize the potential of the MSL system in vegetation monitoring. Traditional strategies of wavelength selection attempt to establish an empirical relationship between large quantities of observed reflectance and foliar biochemical constituents. By contrast, this study proposed to select wavelengths through the radiative transfer model PROSPECT. A five-wavelength combination was established to estimate leaf chlorophyll and water contents: 680, 716, 1104, 1882 and 1920 nm. The consistency of the wavelengths selected were tested by running different versions of PROSPECT model. Model inversion using simulated and experimental datasets showed that the selected wavelengths have the ability to retrieve leaf chlorophyll and water contents accurately. Overall, this study demonstrated the potential of the MSL system in vegetation monitoring and can serve as a guide in the design of new MSL systems for the application community.

Whether intraplate earthquakes have different average source properties, compared to interplate events, has been long debated. It has been proposed that intraplate events tend to rupture smaller areas with higher stress drops, compared to the average interplate earthquake. Here we estimate the rupture lengths of several Brazilian earthquakes by accurately locating their immediate aftershocks. The sparsity of stations in low-seismicity regions, such as Brazil, hinders accurate epicentral determination. We use cross-correlation of P , S and Lg waves to accurately locate the aftershocks relative to a reference event. In several cases, it was possible to infer the rupture length by the distribution of the early aftershocks; with the later aftershocks tending to span a larger area. We studied six different aftershock sequences using regional stations up to several hundred km distance. The mainshock occurs close to the foreshocks, which act as triggers to the main rupture. The immediate aftershocks tend to occur in a circle around a central (presumably stress-free) zone, which we interpret as the rupture of the mainshock. Published data from other events, based mainly on local networks, were added to provide an empirical relationship between rupture length and magnitude. These data suggest that stress-drops in Brazil vary mostly between 0.1 and 10 MPa, a similar range to many other studies worldwide. However, the mean stress drop (about 1 MPa) is smaller than the mean values of both interplate and intraplate events globally (mostly between 2 and 10 MPa). A possible dependence of stress drops with hypocentral depth may explain this difference: Brazilian intraplate earthquakes tend to be shallower than most other mid plate regions giving rise to smaller stress drops, on average. This result has important implications for seismic hazard estimation when GMPE equations from other intraplate regions are used in Brazil.

Now-a-days, most of the investigating research works are focused on developing the material that can substitute the metal that are excellent building materials because of their high toughness, high strength, high melting temperature and chemical reactivity and for this reason, metal is preferred for constructional applications. So far, in this research work a magnesium based new Nanocomposite (MgAZ91D+CNT) were produced by the emerging stir casting method. And also the produced Nanocomposite has been welded by the Friction Stir Welding (FSW) process in order to understand its strength of the joints. Finally, the empirical relationship was developed to predict the maximum strength of the FSW joints of produced Nanocomposite using Design of Expert.

Characterizing the small-strain shear modulus (G0) of sand with fines is of importance in geotechnical applications since natural sand is usually not clean but contains a certain amount of fines. This paper presents an experimental study to investigate G0 values of several sand-fines mixtures, formed by mixing clean quartz sands of different sizes with crushed silica fines of varying quantity. Focus of the study is on the possible interplay between the influence of particle size disparity and the influence of fines contents for which current understanding is not adequate. By defining the particle size disparity as D50/d50, where D50 is the mean size of base sand and d50 is the mean size of fines, a critical range of size disparity is found to be approximately between 4 and 7. When the size disparity is smaller than 4, the role of fines is manifested mainly by fines content; when the size disparity is beyond 7, the contribution of fines to the load transfer gradually becomes negligible because in this case fine grains tend to roll into the voids. A new concept, referred to as combined size disparity, is proposed to capture the influence of fines content and the influence of size disparity in a collective manner. By adopting this concept, an empirical relationship is proposed for estimating G0 values of sand-fines mixtures. The predictive performance of the relationship is then examined using literature data and a reasonably good agreement between prediction and measurement is obtained.

We examine the empirical relationship between the exposure to earthquake hazard and civic capital in Italian municipalities. Drawing on the Italian National Institute of Geophysics and Volcanology, we find that earthquake hazard increases civic capital. We decompose the effect of earthquake hazard variation along four dimensions – frequency, space, magnitude, and timing – and observe that the effect is mostly explained by high-magnitude seismic events in the past. Our results are in line with the intuition that cooperative social norms build over a very long time span.

Using a parsimonious model, this paper analyzes a dockless bikesharing (DLB) system in a city, where the DLB service competes with walking and a generic motorized mode. The DLB system operator chooses a fleet size and a fare schedule that dictate the utility of its service. The access time to the service---a crucial component in the utility---is modeled as a function of the number of unique bike locations, which is estimated from the number of idle bikes through an empirical relationship. The market equilibrium is formulated as a solution to a nonlinear equation system, over which three design problems are formulated to maximize (i) profit; (ii) ridership; and (iii) social welfare. We calibrate the model against empirical data collected in Chengdu, China and test three counterfactual strategies, each for one of the three design problems. We find: (i) the current fleet limit set by Chengdu should be cut by roughly two thirds in order to avoid severe oversupply and waste; (ii) maximizing ridership keeps the fare at an affordable level to maintain revenue neutrality, while raising ridership and social welfare close to system optimum; (iii) for a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator's objective. When it focuses on profit, limiting price is a much more effective policy than limiting fleet size. If, instead, the operator aims to grow its market share, then setting a limit on fleet size becomes a dominant strategy.

To study the applicability of the Porter hypothesis to Chinese manufacturing enterprises from a property rights perspective, we match the financial data of all A-share manufacturing companies listed in Shanghai and Shenzhen Stock Exchanges of China from 2008 to 2016 with the marketization index compiled by Wang et al. (2017), and test the empirical relationship between environmental regulation and corporate financial performance using a fixed effect model. We find that: First, the Porter hypothesis per se is not supported in China’s manufacturing sector, and environmental regulation often lowers manufacturing enterprises’ financial performance. Second, property rights protection has a positive impact on corporate financial performance, and a good property rights institutional environment can mitigate the negative impact of environmental regulation on corporate financial performance by inducing corporate innovations. Third, property rights protection exhibits differential extent of its moderating effect on the relationship between environmental regulation and enterprise performance across different types of enterprises in terms of their ownership natures and the regional economic development level of their geographic locations. All of our main results survive several robustness checks that we performed. We confirm that Porter hypothesis in general is not applicable for China’s manufacturing enterprises at present, but the moderating effect of property rights protection is steering the relationship between environmental regulations and corporate financial performance towards the path of Porter hypothesis, thereby in the future a win-win situation of environmental protection and corporate financial performance improvement may be achieved.

New Zealand’s public insurer, the Earthquake Commission (EQC), provides residential insurance for some weather-related damage. Climate change and the expected increase in intensity and frequency of extreme weather-related events are likely to translate into higher damages and thus an additional financial liability for the EQC. We project future insured damages from extreme precipitation events associated with future projected climatic change. We first estimate the empirical relationship between extreme precipitation events and the EQC’s weather-related insurance claims based on a complete dataset of all claims from 2000 to 2017. We then use this estimated relationship, together with climate projections based on future greenhouse gases concentration scenarios from six different dynamically downscaled Regional Climate Models, to predict the impact of future extreme precipitation events on EQC liabilities for different time horizons up to the year 2100. Our results show predicted adverse impacts that vary — increase or decrease over time and space. The percent change between projected and past damages — the climate change signal — ranges between an increase of 7% and 8% higher in the period 2020 to 2040, and between 9% and 25% higher in the period 2080 to 2100. We also provide detail caveats as to why these quantities might be mis-estimated. The projected increase in the public insurer’s liabilities could also be used to inform private insurers, regulators, and policymakers who are assessing the future performance of both the public and private insurers that cover weather-related risks in the face of climatic change.

The very few studies on the empirical relationship between time preference and migration have been developed in small samples or without controlling for individuals’ cognitive skills. This study uses data from a large, nationally representative survey with information on time preferences and cognitive skills to investigate whether lifetime cross-region migrants in Spain are less impatient than individuals who choose to remain in their birth region. The empirical model incorporates predicted probabilities of misclassifying the migrant status. Results suggest that the effect of impatience on the likelihood of ever migrating is negative though diminishing, and that it is smaller than the effect estimated for international migrants.

To understand the weak empirical relationship between human capital and macroeconomic performance, we present a model in which human capital is allocated to three activities: production, tax collection (bureaucracy), and public education. The effective tax rate is low in poor countries because tax collection requires human capital, which is scarce. Throughout the transition, the effective tax rate rises, which involves a diversion of human capital from production to bureaucracy and public education. Consequently, human capital has a weak effect on production, even when human capital is efficiently allocated. Differences in institutional quality may involve a spurious negative correlation between gross domestic product and human capital.

Objectives The purpose of this study is to investigate whether the often-debated association between physical disorder and crime exists longitudinally and operates through a specific spatial process. Methods We combine four administrative and official databases to analyze patterns of crime occurring each month in New Orleans, LA, between 2012 and 2018 at the block group level. We adopt a generalized additive model (GAM) framework to efficiently account for potential spatial, temporal, and spatiotemporal autocorrelation to investigate this association. Results The resident-driven self-report measure of physical disorder (i.e., 311 calls) consistently predicts neighborhood criminal activities in New Orleans. The spatial effects of 311 calls in adjacent areas on drug violations, property crime, and violence in a focal neighborhood are also observed. In contrast, official reports of physical disorder, captured with code violation, is either weakly related to crime or lacking an empirical relationship. Conclusion Overall, our findings suggest that the association between physical disorder and neighborhood crimes is robust over time. In addition, this association is conditioned by how physical disorder is operationalized, with dimensions such as residential report and official report of physical disorder playing a unique, and occasionally important role in understanding overall patterns of neighborhood criminal activity.

How should one use (quasi-)experimental evidence when choosing policies such as tax rates, health insurance copay, unemployment benefit levels, and class sizes in schools? This paper suggests an approach based on maximizing posterior expected social welfare, combining insights from (i) optimal policy theory as developed in the field of public finance, and (ii) machine learning using Gaussian process priors. We provide explicit formulas for posterior expected social welfare and optimal policies in a wide class of policy problems. The proposed methods are applied to the choice of coinsurance rates in health insurance, using data from the RAND health insurance experiment. The key trade-off in this setting is between transfers toward the sick and insurance costs. The key empirical relationship the policy maker needs to learn about is the response of health care expenditures to coinsurance rates. Holding the economic model and distributive preferences constant, we obtain much smaller point estimates of the optimal coinsurance rate (18% vs. 50%) when applying our estimation method instead of the conventional “sufficient statistic” approach.

This study investigated the empirical relationship between measures of construction safety climate dimensions and safety performance. A comprehensive review of existing literature of construction safety climate was conducted to: (1) review the questionnaires used to measure safety climate dimensions in the construction industry; (2) identify the salient dimensions of safety climate; and (3) establish a consistent definition of each safety climate dimension. Then, a statistical meta-analysis of the empirical relationship between construction safety climate dimensions and safety performance was performed. 107 studies were reviewed, and 11 studies were included in the meta-analysis. The review indicated that 14 construction safety climate dimensions were commonly used to assess safety climate. Of the 14 dimensions, five— supervisor’s safety role (r = 0.30, 95% CI = 0.07 to 0.50), management commitment to safety (r = 0.27, 95% CI = 0.23 to 0.31), safety rules and procedures (r = 0.25, 95% CI = 0.12 to 0.37), individual responsibility to health and safety (r = 0.23, 95% CI = 0.17 to 0.31, and training (r = 0.10, 95% CI = 0.03 to 0.17)—were identified as commonly used predictors of injury rates. The results can be used by researchers and practitioners in this burgeoning field to standardize the assessment of safety climate and to validate the use of safety climate as a predictor of safety performance.

Laboratory experiments have been carried out to investigate the erosion behaviour of a number of marine sediments which were reconstituted and sieved in various ways to ensure a large variation in soil properties such as (i) particle size distribution (including median grain size and fines content), (ii) bulk density, and (iii) hydraulic permeability. Based on the experimental results, it was found that for finer marine sediments the erosion rate at a given shear stress was sensitive to changes in these soil properties. No unique relationship was found between changes in the erosion rate and changes in the bulk density or fines content. More specifically, we find that the erosion rate at a given shear stress reduces with increases in density and fines content, but by an amount that is different for different sediments. In contrast, we show that there appears to be a unique relationship between permeability and erosion rate, such that permeability may be used to predict the erosion rate for the marine sediments at any density. By reinterpreting existing experimental results in the literature, we find that this same relationship between permeability and erosion rate is apparent for quartz sediments. We propose an empirical relationship, which fits well the erosion rate behaviour of finer sediments close to the threshold shear stress.

Particulate matter (PM) pollution and its health effects are receiving more attention. Green space can provide critical ecosystem services, and increasing the supply of green space can reduce PM pollution, but effects of green spaces on PM at different scales are not clear. Based on the traffic and meteorological factors from 50 monitoring stations, principal composition cluster analysis (PCA) and hierarchical cluster analysis (HCA) were implemented. Daily PMs (PM10 and PM2.5) concentrations were measured at 50 monitoring stations in Zhejiang (1 Feb 2015 to 28 Feb 2017) to quantify the spatiotemporal change of PM concentration and its empirical relationship with green spaces and landscape structure. The result shows: (1) At 5-km, or smaller, scale, the correlation between green space and PM2.5 is stronger than PM10; (2) at 2-km scale or less, the total edge length has more impact on PMs than green cover area, while at 3–5-km scale, the influence of green cover is more dominated; (3) In the mountain and hilly area in Zhejiang that are dominated by meteorological factors, we can establish the PM2.5 forecast model by 3–4-km scale green spaces, while in those basins or low lands, we can build the PM2.5 forecast model by using 1–2-km scale green spaces. The results are of great importance for urban green space planning, especially when it comes to the size and shape of the green space. In addition, it can provide guidance to the future application of LUR model in Zhejiang area.

In India, a number of coal seams are being extracted by using the continuous miner (CM). There is a need to standardise some of the major parameters like cut-out distance (COD) and the convergence of the roof strata for safe operation of the CM. The production and productivity of this system mainly depend on the COD, i.e. the length of the stable drivage made in the coal seam by the CM at a time without installing any reinforcement/support. This factor is determined by assessing the convergence of the roof strata. Therefore, prior to driving of the roadways by the CM, the estimation of the convergence of the roof strata is necessary for safe and stable driving of the roadways. No suitable empirical relationship is found between the convergence and the COD. This paper deals with the assessment of the roof convergence during development of the coal seams by the CM. Variations of the convergence with the major influencing parameters, i.e. the COD, the rock mass rating and the width of the gallery, are obtained by the parametric study through elasto-plastic numerical modelling . The trend of the convergence data with respect to the input parameters is found non-linear. Thus, a non-linear multivariate model is framed by putting the constant and exponents in the input parameters. These constant and exponents are determined through regression analysis to develop a predictive model. The coefficient of determination of the model is around 0.98. The model is validated with the monitoring data of different mines. This model can be applied for the assessment of the convergence of the roof in underground coal mines for safe driving of the roadways by the CM. Alternatively, optimum COD of the CM can be designed by fixing the threshold limit of the convergence value for a particular geomining condition.

Climate change involves long term changes in climate including increase in temperature, elevated CO2 and uneven distribution of rainfall quantity and periodicity. Though daily mean temperature (Tmean) is considered a widely useful index of assessment of climate change, averaging can obscure some of the variations expected in diurnal temperature range (DTR). The aim of this study was to evaluate the effectiveness of DTR relative to Tmean as a metric for predicting millet yield using a combination of a historical dataset (1980–2010) and two climate model (MIROC5 and GFDL) projections for 2017–2046 under RCP 4.5 and 8.5 across two different environments (arid, Layyah and semi-arid, Faisalabad) of Punjab, Pakistan. Provincial datasets of pearl millet yields were collected and checked for an empirical relationship between Tmean, DTR and crop yield. The mean of projections showed increasing DTR relative to baseline in both environments. Projected Tmax and Tmin were highly correlated (0.90–0.99) for both environments and climate models. MIROC5 predicted Tmax and Tmin well and GFDL performed efficiently in predicting precipitation by 2046. The data also showed more hot days in future decades and erractic rainfall pattern by 2046 in both environments. The Genetic Algorithm (GA) appeared to be a good approach to assess climate change impact on pearl millet yield in Punjab, Pakistan, predicting negative yield impacts (11–12%) due to future warming. We suggest broadening tests of this method to other cases around the world, with similar climate regimes.

Diversion flows from rivers or main channels affect bed morphology and cause erosion and sedimentation at the diversion channel junction. In a diversion channel flow system, the scour depth and scour length are considered important parameters and should be taken into account during the project construction stage because it affect the stability of main channel banks and crossing structures. In this study, the scour depth produced by diversion flow in a main channel was investigated using a physical sand bed model. The investigations considered five diversion angles (30°, 45°, 60°, 75°, and 90°), three bed width ratios (29%, 38%, and 48%), and five total discharges (7.25, 8.5, 9.75, 11, and 12.25 L/s). Results indicated that the scour depth in the main channel reduced as the diversion angle reduced. Empirical relationship to demonstrate relative scour depth (Kds) for different diversion angles and bed width ratios was proposed. Relative scour depth can be defined as a relative scour depth in case of a diversion angle of θ° to that with 90° for the same flow condition and bed width ratio. Empirical relationships to estimate the scour depth and scour length with the governing hydraulic parameters were also established with a good accuracy. Testing the proposed relationships gave reasonable mean errors of 3.46% and 10.3% in predicting scour depth and scour length, respectively.

This paper applies the empirical methodology of panel Granger-causality tests to test the relationship between bank diversification and liquidity creation, employing the panel vector autoregression models in a generalized method of moments framework. We find that an increase in the degree of bank diversification between traditional bank activities generating net interest income and non-traditional bank activities generating non-interest income reduces bank liquidity creation. However, an increase in the degree of bank diversification within non-traditional bank activities leads to an increase in bank liquidity creation. In the context of China's banking sector, the positive and negative aspects of bank diversification coexist. In addition, we do not find evidence of reverse causality between banks' diversification and liquidity creation. Furthermore, we examine whether the investigated empirical relationship will change for heterogeneous banks and find that the relationship between bank diversification and liquidity creation exhibits heterogeneity for banks with various characteristics.

The cohesive energy, formation enthalpies, and elastic constants of AgAl, AuAg, AuPd, AuPt, CuNi, CuPt, NiPb, NiPd, NiPt, and PtPd alloys are calculated by molecular dynamics simulation and first-principle calculation. It is found that the calculated formation enthalpies generally agree with the availible experimental data. The Debye temperatures of these alloys with different compositions are computed from the obtained elastic constants. An empirical relationship between Debye temperature and cohesive energy is developed, which is confirmed by both the simulation and calculation.

In order to evaluate different modeling techniques for Unglazed Transpired Collectors (UTC), not only mathematical modeling method for UTC based on heat transfer expressions to estimate the various heat transfer coefficients for the UTC components and empirical relationship, but also grey modeling (GM) approach, Artificial Neural Networks (ANN) and Adaptive Network based Fuzzy Inference System (ANFIS) methods have been designed and introduced, in this study. Thermal performance experiments of UTC have been carried out on an optimized experimental setup. Firstly, obtained experimental results have been compared with the mathematical model. To constitute a common point, output temperature of the UTC has been selected as the output variable. Secondly, the GM(1,1) approach has been used to forecast the output temperature with higher accuracy with the aid of simple mathematical equations. Then, an ANN has been designed to estimate the output temperature using measured inputs variables. Next, ANFIS has been designed and used to predict the output temperature. Finally, obtained results have been compared and comparison results have been illustrated in both graphical and tabular form. GM(1,1) is the simplest method to forecast the output temperature with high accuracy, while ANFIS technique will be the best solution to predict the output temperature.

Issues with conventional depillaring techniques resulted in unscientific development of a number of coal seams into rectangular/square shaped pillars, mainly, located at shallow depths in different coalfields of India. Continuous miner based mechanised depillaring (MD) has been introduced to extract these coal pillars which is the main stage of production. The practise of leaving a proper sized rib/snook is a legal requirement as it decides the efficiency and safety of the MD operation. However, MD of square/rectangular shaped pillars created irregular-shaped-heightened-rib/snook. Conventional empirical formula is not capable to estimate strength of such ribs/snooks. These ribs/snooks should be of sufficient size to protect the adjacent slicing operation and junction as well as it should fail in a controlled manner when shifted inside the goaf. Field studies conducted at 39 panels of 7 different mines found a number of factors affecting the design of rib/snook. Proper assessment of performance of different sizes and shapes of ribs/snooks in field is difficult due to the complex and challenging underground mining environment of depillaring. These challenges prohibit a detailed study to be carried out in field. Therefore, parametric study on calibrated numerical model is conducted to estimate the strength of rib/snook with variation in sizes and heights of rib/snook and depth of cover. Further, an empirical relationship is developed to estimate the strength of rib/snook based on multivariate regression analysis . The values of strength estimated by the developed relationship is found to be least when compared with the popular pillar strength formulations due to effect of pillar/rib/snook spalling observed during field investigation with increase in depth of working. Results of field and simulation studies are presented and discussed in this paper for the design of irregular-shaped-heightened-rib/snook.

Discrimination between fluid saturation and pore pressure-related changes using 4D seismic response is a challenging issue in reservoir studies. The main objectives of this study are the estimation of pore pressure and water saturation for baseline and monitor seismic surveys as well as determination of bypassed hydrocarbons location. To achieve this goal, we propose two practical workflows. The workflows originate from the fact that changes in pressure and saturation result in different trends on a Vp/Vs ratio versus P-Impedance cross-plot. The fact that an increase in hydrocarbon saturation results in a decrease in the Vp/Vs ratio and that an increase in pore pressure causes an increase in the Vp/Vs ratio plays a key role in this study. In this approach, after rock physics modeling and seismic data conditioning, elastic seismic inversion is performed for baseline and monitor surveys. The Bayesian probability classification technique is used to differentiate pay zones from non-pay zones which is then incorporated for prediction of fluid saturation changes. In the pore pressure prediction workflow, the Bowers equation is replaced with an empirical relationship in which a consistent match is attained between the calculated effective pressure and Vp/Vs ratio at injected water zones. Pore pressure prediction reveals noticeable anomalies around the injector wells but pressure depletion around the producers remains uncertain due to the insignificant pore pressure drop and the poor quality of seismic data near the platform area. However, at most of the producers' locations, the predicted pore pressure shows a depletion trend confirmed by the field-measured pressure. In this case study, the unswept oil and injected water areas are clearly outlined, which helps to improve sweep efficiency and update EOR scenarios.

Various approaches and models have been proposed to remotely estimate surface pCO2 in the ocean, with variable performance as they were designed for different environments. Among these, a recently developed mechanistic semi-analytical approach (MeSAA) has shown its advantage for its explicit inclusion of physical and biological forcing in the model, yet its general applicability is unknown. Here, with extensive in situ measurements of surface pCO2, the MeSAA, originally developed for the summertime East China Sea, was tested in the northern Gulf of Mexico (GOM) where river plumes dominate water's biogeochemical properties during summer. Specifically, the MeSAA-predicted surface pCO2 was estimated by combining the dominating effects of thermodynamics, river-ocean mixing and biological activities on surface pCO2. Firstly, effects of thermodynamics and river-ocean mixing (pCO2@Hmixing) were estimated with a two-endmember mixing model, assuming conservative mixing. Secondly, pCO2 variations caused by biological activities (ΔpCO2@bio) was determined through an empirical relationship between sea surface temperature (SST)-normalized pCO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) 8-day composite chlorophyll concentration (CHL). The MeSAA-modeled pCO2 (sum of pCO2@Hmixing and ΔpCO2@bio) was compared with the field-measured pCO2. The Root Mean Square Error (RMSE) was 22.94 µatm (5.91%), with coefficient of determination (R2) of 0.25, mean bias (MB) of − 0.23 µatm and mean ratio (MR) of 1.001, for pCO2 ranging between 316 and 452 µatm. To improve the model performance, a locally tuned MeSAA was developed through the use of a locally tuned ΔpCO2@bio term. A multi-variate empirical regression model was also developed using the same dataset. Both the locally tuned MeSAA and the regression models showed improved performance comparing to the original MeSAA, with R2 of 0.78 and 0.84, RMSE of 12.36 µatm (3.14%) and 10.66 µatm (2.68%), MB of 0.00 µatm and − 0.10 µatm, MR of 1.001 and 1.000, respectively. A sensitivity analysis was conducted to study the uncertainties in the predicted pCO2 as a result of the uncertainties in the input variables of each model. Although the MeSAA was more sensitive to variations in SST and CHL than in sea surface salinity (SSS), and the locally tuned MeSAA and the empirical regression models were more sensitive to changes in SST and SSS than in CHL, generally for these three models the bias induced by the uncertainties in the empirically derived parameters (river endmember total alkalinity (TA) and dissolved inorganic carbon (DIC), biological coefficient of the MeSAA and locally tuned MeSAA models) and environmental variables (SST, SSS, CHL) was within or close to the uncertainty of each model. While all these three models showed that surface pCO2 was positively correlated to SST, the MeSAA showed negative correlation between surface pCO2 and SSS and CHL but the locally tuned MeSAA and the empirical regression showed the opposite. These results suggest that the locally tuned MeSAA worked better in the river-dominated northern GOM than the original MeSAA, with slightly worse statistics but more meaningful physical and biogeochemical interpretations than the empirical regression model. Because data from abnormal upwelling were not used to train the models, they are not applicable for waters with strong upwelling, yet the empirical regression approach showed ability to be further tuned to adapt to such cases.

Retrieving ground motion information for non-urban and semi-vegetated areas using differential Interferometric Synthetic Aperture Radar (InSAR) and Small Baseline Subset (SBAS) approaches with C-band satellite radar imagery is challenging due to temporal decorrelation. By exploiting six stacks of medium resolution ERS-1/2 SAR images acquired between 1992 and 2000 over four regions of interest in the UK, this paper demonstrates the performance of the recently developed processing method Intermittent SBAS (ISBAS). This approach builds upon the conventional low-resolution SBAS method and, by relaxing the approach to selecting image pixels to process and accounting for the intermittent nature of non-urban targets, is capable to extend the coverage of motion results across the full range of land cover types, even those typically unfavourable for InSAR. On average, the new ISBAS implementation provides 4 to 26 times more coverage than SBAS for the processed regions, with the spatial coverage of ground motion solutions increasing from only 4–12% land pixels with SBAS, to 39–99% with ISBAS. Despite relying only on temporal subsets of the networks of small baseline interferograms, intermittently coherent pixels show velocity standard errors of 0.8–1.4 mm/year on average, hence retain sub-millimetre to millimetre precision. The empirical relationships between intermittent coherence and standard errors in the estimated ground motion velocity are computed for each of the six datasets, and confirm that errors are controlled by the number of independent observations used for each image pixel to extract the ISBAS solution. In particular, velocity standard errors e vel for the intermittently coherent pixels are inversely proportional to the square root of the number of best coherence interferograms used, n i , and can be modelled as e vel = 11 / n i mm/year on average for the six datasets. The established empirical relationship also allows informed decisions on the ISBAS threshold for n i to be made. This is achieved by setting the maximum acceptable error in the velocity estimate e MAX , and then computing the corresponding minimum n i to accept an intermittently coherent pixel that will guarantee the desired precision. In the present era of ‘big SAR data’ and their derived ‘big InSAR data’, we discuss perspectives on the use of huge datasets of thousands or even millions of ground deformation time series – such as those produced using ISBAS, with a particular focus on the veracity of big data and the need for a quality assessment check-point in the ‘big InSAR data’ cycle.

Volcano-tectonic (VT) earthquake swarms are usual precursors to volcanic eruptions and their dynamic phenomenologies are an invaluable eruption forecasting tool. A power-law relationship between cumulative seismic moment release and intrusion volume has been proposed for both well injection sites and VT swarms. Here we compile data from 17 geodetically-studied VT swarms and use a Bayesian methodology to assess the relationship between cumulative moment release and subsurface volume change. We find that the empirical relationship derived from injection sites systematically underpredicts volume changes observed during VT swarms near volcanoes. A new relationship derived specifically from VT swarms provides a better fit, but large uncertainties mean that estimates of intruded volume range by three orders of magnitude (95% confidence) for a given cumulative seismic moment release. We further subdivide the dataset, and although the sample size is too small to meet conventional statistic hypothesis significance tests, qualitative inspection suggests that the seismic moment release is proportionally larger for unrest swarms which culminate in eruption. We conclude that the currently available dataset is too sparse and the uncertainties too large for use for reliably forecasting eruption magnitude; however, as the dataset of cases grows, further analysis along these same lines may provide improved diagnostic insights into eruption-related processes that may be driving seismic moment release in VT swarms.

A go-around is an aborted approach of an aircraft. We model go-around occurrence using Principal Component Logistic Regression (PCLR). This entails go-around detection, feature engineering, and model estimation. As a case study, we consider John F. Kennedy (JFK) International Airport arrivals, and model go-around occurrence based on information available when the subject flight is five nautical miles from its landing runway threshold. The PCLR model is based on Principal Component Analysis (PCA) for analyzing data that suffer from multi-collinearity. The model provides a representation of the empirical relationship between go-around occurrence and Principal Components (PCs) covariates, which encompass flight approach features, aircraft characteristics, flight lead-trail spacing, surface operation, go-around clustering effect, airport and weather conditions. We use factor loading analysis to reveal the relationship between variables and the PCs they formed. Coefficient estimates of PCs are also transformed back to the scale of the original variables by matrix operations. A counterfactual analysis is employed to assess the importance of different features, and reveals that the stability of an approach, flight lead-trail spacing, departure traffic, and ceiling are the most salient factors affecting go-around occurrence.

This paper addresses the empirical relationship between energy poverty and energy efficiency in developed and developing countries through various domains. The analysis is conducted using energy poverty indicators, country-wise GDP, energy efficiency, and social welfare by using data envelopement analysis (DEA) and entropy method through mediating role of econometric estimation by using . The findings show that energy poverty will reduce the gross domestic product from par to par. Moreover, the countries' social welfare may dramatically decrease if long-term energy poverty is consistent and not mitigated, negating the traditional theory that only gaining energy efficiency is more than enough for energy stakeholders. Based on the deducted results, this study suggests potential policy adjustments, and illustrates the value of productive energy supply. Results reveals that expenditure associated energy poverty has major share while electricity consumption based energy poverty is the second major factor. Moreover, there is an absence of modern electricity access in less energy efficient areas. SMoreover energy prices should also provide a quantitative assessment of actual costs associated with the rising concern of energy efficiency and social welfare to reduce energy poverty. On this, the study provides valuable information for policy makers and relevant stakeholders.

Although the European Union sets common objectives, each member country is in charge of its own energy policy. An exhaustive analysis of the power generation mixes of the EU member countries from 1990 to 2017 has revealed strong disparities in the evolution of their structure. Using key performance indicators, the paper analyzes the current situation in terms of carbon intensity, dependence, vulnerability and energy transition dynamics. The paper focuses on the transition dynamics for 12 emblematic EU countries. It is worthwhile to note that most of these countries have managed to reduce the carbon intensity of their electricity. The analysis revealed, for some countries, an empirical relationship between imports and the carbon intensity of power generation. The increase of the share of renewable energies induces a dependence of a country on its neighbors as long as a massive storage of electricity is not available. The increased electricity trade with neighboring countries induce costs and benefits. Only countries that have invested heavily in hydro and nuclear power benefit from both a low-carbon electrical production mix and a low dependence on imports. Finally, the paper suggests some avenues for public policies in the energy field.

For over 40 years Rock-eval pyrolysis data have been used to assess thermal maturity and source rock potential. There have been many published equations representing the transformation of Tmax to vitrinite reflectance (Ro) equivalence, but there has not been an attempt to investigate the empirical relationship using a large dataset. In this study, 33,732 approximately coincident measurements from 903 publications were utilized to test the applicability and practicality of Tmax, hydrogen index (HI), and production index (PI) as thermal maturity indicators. Additional sample metadata (sample type, lithology, organofacies, sample age, and other Rock-eval pyrolysis parameters) were also evaluated to ascertain if there were parameters that more frequently led to higher confidence measurements. There was a positive correlation between Tmax and Ro, but there is considerable scatter in the data. A stronger trend was revealed in the mean, median, and mode values for each Tmax versus Ro pair that can be represented by the equation Ro = (0.013 x Tmax) - 5.0 with a R2 value of 0.22, which may be an improvement upon previously published equations. There was a breakdown in the correlation between Tmax and Ro at the beginning of the dry gas window (Ro values ~1.5; Tmax ~500 °C), which was not unexpected since the S2 peak significantly decreases through the wet gas window. Tmax-derived thermal maturity estimates are therefore most reliable between the values of ~430 °C and ~500 °C. It was also observed that coals with high total organic content (TOC) and S2 values had the least amount of data scatter among the different source rock organofacies. This may be due to the higher initial S2 values and these samples having a more prominent S2 peak allowing them to yield more reliable Tmax determinations.

We study how digital adoption by firms—a precursor to automation—shapes the labor market structure and worker outcomes in developing countries. Using a large sample of developing countries, we document a strong and negative link between firm digital adoption and self-employment rates. This relationship persists even after controlling for the level of development and other factors associated with the distinct employment structure of developing countries. In contrast, there is no link between digital adoption and unemployment rates. We develop a model with equilibrium unemployment, self-employment, endogenous firm entry, and information-and-communications technology (ICT) adoption, and show that positive linkages between the cost of technology adoption (and therefore technology adoption itself) and salaried-firm entry costs are crucial for rationalizing the empirical relationship between firm digital adoption and self-employment.

Canopy clumping index (CI) characterizes the spatial distribution of leaves or needles within a vegetation canopy. CI is critical in determining the canopy radiation transfer, photosynthesis, and hydrological processes. This paper reviews the field measurement and remote sensing estimation methods, characteristics, and applications of CI. CI is generally estimated in the field using direct, indirect optical, and allometric methods. The basis for the indirect optical approach is the gap-fraction based method. Remote sensing of CI is carried out using passive optical and active LiDAR technology. Current CI products are mainly derived from an empirical relationship with the normalized difference hotspot and darkspot (NDHD) index. Further CI product validation studies should be conducted using enhanced field measurements. CI typically shows vertical, scale, directional, and temporal variations. The overall CI is calculated as the integration of the directional CI values. CI is a key parameter in leaf area index (LAI) estimation, and canopy reflectance and land surface modeling studies. Future studies should focus on the adoption of automated and wireless measurement methods, development of new remote sensing estimation methods, improving our understanding of the CI characteristics, and accounting for CI in land surface models.

Borehole breakout initiation stress is critical for mining and geotechnical engineering as it indicates horizontal stress magnitudes and rock quality in a specific layer. This stress can be significantly intensified under laboratory conditions where the borehole size is small, although the specimen sizes in previous studies varied. This may result in inaccurate experimental results as the sample itself is affected by the scale effect. In this paper, two series of normal compression tests (constant borehole–specimen ratio and constant specimen size) were conducted on various borehole radii (6 mm–12.5 mm) to study the influence of specimen size as well as borehole size on breakout initiation stress. Experimental results revealed that specimen size does not have an obvious influence on the breakout initiation stress when specimen to hole ratio is over 10, suggesting that data collected from previous studies in the literature are also valid and can be used for future breakout analysis. A comparative analysis of the stress averaging concept, failure criteria considering pressure-dependency and an empirical model was conducted to predict breakout initiation stress. Results showed that the stress averaging concept was not reliable for experimental data, whereas the linear fracture initiation criterion produced the most accurate results, although its parameters are dubious and difficult to obtain. An empirical relationship, derived from literature data under normal compression tests, also showed promising results in estimating breakout initiation stress. Due to its simplicity and reliability, it may be used in future studies with more validation.