Empirical Trends Research Articles

A framework based on multivariate distribution-based virtual sample generation and DNN for predicting water quality with small data

Deep Neural Network (DNN) is a powerful tool for predicting and monitoring water quality. However, its application is only limited to well-monitored zones where the availability of data for training and validation phases. In this study, we attempt to develop a novel framework based on Multivariate distributions (MVD) (elliptical copulas)-based Virtual Sample Generation (VSG) method to broaden the application of DNN to predict water quality even with a small dataset. This framework is evaluated to predict the Entropy Weighted Water Quality Index (EWQI) using DNN and Electrical Conductivity, Temperature, and pH as input variables, in Berrechid and Chaouia aquifer systems, Morocco. Validation results showed that the virtual samples generated from 400, 50, 30, and 20 original samples improved the NSE from 0.88 to 0.92, from 0.53 to 0.91, from 0.42 to 0.91, and from 0.24 to 0.87, respectively. Besides, sensitivity analysis of the methodology to the virtual data sizes and the original samples showed that the RMSE and NSE of the DNN models have limits in function to virtual data sizes according to the first order Exponential Decay and logistic trends, respectively. These limits highly depend on original sample sizes. Such empirical trends are crucial for reproducing the proposed methodology in other sites to determine optimal virtual datasets. Overall, the proposed methodology provided new insights to improve the DNN model performances in predicting water quality with small datasets. Hence, it is useful to manage water quality in order to supply clean water for the population in poorly monitored zones.

Whither rentierism following the 2014 oil price decline: Trajectories of policy adjustment in the Arab Gulf

This paper looks at key trajectories of policy adjustment across member states of the Gulf Cooperation Council (GCC)—a region shaped by state-controlled income from exporting hydrocarbons—since 2014, the year in which the price of crude oil dropped significantly. We argue that despite a wide range of policy responses, no deep, structural reform has been initiated in any of these countries. We highlight, however, the importance of several crucial trends shedding light on the future of reforms in the region. First, while coordination between oil producers was successful at the global level in the context of OPEC+, similarly gainful policy coordination has been largely absent within the GCC. Second, migrant workers from the Global South have been the biggest losers from post-2014 adjustment policies, while citizens of the Arab Gulf states—most of whom are employed in the well-paid public sector—were able to maintain their privileges. Third, as typical for rentier states, adjustment policies were characterized by institutional weakness. Differences between GCC countries can be explained by looking at distinct institutional configurations relevant to policy formulation and decision-making. Fourth, adjustment across public hydrocarbon industries reveals initial preparations to cope with an energy transition while there is no indication that ruling families have softened their traditional control over economic activities. Based on the discussion of these four empirical trends, we emphasize the importance of state-class relations and the role of institutions in explaining policy responses during periods of declining hydrocarbon income within rentier states.

Analyzsng the variation in farmers’ perceptions of climate change impacts on crop production and adaptation measures across the Ganges’ Tidal Floodplain in Bangladesh

ABSTRACT Bangladesh is highly vulnerable to climate change (CC) and consequent agricultural productivity repercussions. This study aimed at exploring the difference in farmers' perceptions of CC impacts on crop production and variation in adaptation measures across the Ganges' Tidal Floodplain (GTF) region. To unlock the local level context-specific nature of adaptation, the study area covered two distinct zones of the GTF: interior zone, surrounded by the Sundarbans mangrove forest, and exterior zone being more exposed to the Bay of Bengal. An analysis of the differences in empirical climate parameter trends and farmers' adaptations to adjust to these changes in the two sample study areas (one from each zone) was conducted. In the exterior zone (Patuakhali), delayed monsoon and increasing intensity of post-monsoon rainfall coupled with poor water drainage resulted in delayed winter vegetable production and late harvest of rainy season rice (aman), eventually leading to a loss of winter rice season (boro). Rainfall in the interior zone (Satkhira) was found erratic and unevenly distributed causing unexpected loss of harvest. Farmers in both areas reported a high impact of CC on crop production. Farmers reported optimisation of fertiliser and pesticide use and planting date adjustment as the most practiced adaptations in both areas. Other preferred adaptation options were rice-fish farming in Satkhira and growing of high-value cash crops in Patuakhali. In terms of weighted average index scoring, Satkhira farmers on average had medium adaptation scores, while most of Patuakhali farmers had medium to low adaptation scores. Living in the same agroecological zone, the difference in farmers' perceptions, climate variability, and adaptation practices call for context- and location-specific adaptation planning and policy.

Cognitive forces shape the dynamics of word usage across multiple languages

The analysis of thousands of time series in different languages reveals that word usage presents oscillations with a prevalence of 16-year cycles, mounted on slowly varying trends. These components carry different information: while similar oscillatory patterns gather semantically related words, similar trends group together keywords representative of cultural and historical periods. We interpreted the regular oscillations as cycles of interest and saturation, whose behavior could be captured using a simple mathematical model. Driving the model with the empirical trends, we were able to explain word frequency traces across multiple languages throughout the last three centuries. Our results suggest that word frequency usage is poised at dynamical criticality, close to a Hopf bifurcation which signals the emergence of oscillatory dynamics. Crucially, our model explains the oscillatory synchronization observed within groups of words and provides an interpretation of this phenomenon in terms of the cultural context driving collective cognition. These findings contribute to unravel how our use of language is shaped by the interplay between human cognition and sociocultural forces. SignificanceThe frequency with which words are used presents regular oscillations of 16 years. We propose that these oscillations arise from a basic cognitive mechanism common to other cultural objects with life cycles, such as fashion. The words that belong to a topic of interest increase their frequency, which is then inhibited by saturation until interest is regained. Here we set up a simple mathematical model for the interaction of this cognitive mechanism and the sociocultural context, which explains the occurrence frequencies of thousands of words in different languages during the past three centuries. We show that oscillations are tuned to a critical point and are synchronized within word communities.

An adaptive linear filter model of procedural category learning.

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

Centrifugal breakout reconnection as the electron acceleration mechanism powering the radio magnetospheres of early-type stars

ABSTRACT Magnetic B-stars often exhibit circularly polarized radio emission thought to arise from gyrosynchrotron emission by energetic electrons trapped in the circumstellar magnetosphere. Recent empirical analyses show that the onset and strength of the observed radio emission scale with both the magnetic field strength and the stellar rotation rate. This challenges the existing paradigm that the energetic electrons are accelerated in the current sheet between opposite-polarity field lines in the outer regions of magnetized stellar winds, which includes no role for stellar rotation. Building on recent success in explaining a similar rotation-field dependence of H α line emission in terms of a model in which magnetospheric density is regulated by centrifugal breakout (CBO), we examine here the potential role of the associated CBO-driven magnetic reconnection in accelerating the electrons that emit the observed gyrosynchrotron radio. We show in particular that the theoretical scalings for energy production by CBO reconnection match well the empirical trends for observed radio luminosity, with a suitably small, nearly constant conversion efficiency ϵ ≈ 10−8. We summarize the distinct advantages of our CBO scalings over previous associations with an electromotive force, and discuss the potential implications of CBO processes for X-rays and other observed characteristics of rotating magnetic B-stars with centrifugal magnetospheres.

Holistic life-cycle accounting of carbon emissions of prefabricated buildings using LCA and BIM

Prefabricated concrete building (PCB) can save resources, reduce construction, improve quality, and reduce pollution. Understanding PCB environmental benefits versus traditional buildings could reinforce relevant policies and adoption. This study aimed to develop a holistic life-cycle accounting system based on BIM technology to calculate carbon emissions of PCB accurately and efficiently, and verify the effects of energy saving and emission reduction. Existing international and national databanks allowed the compilation of carbon emission factors. Five representative residential PCB projects were enlisted to calculate carbon footprints in six individual life-cycle construction stages. In most stages, carbon emissions were reduced with an increasing prefabrication rate. In the operation and maintenance stage, carbon emissions accounted for approximately 91% of the total, and the building materials production stage accounted for about 11%. The “negative carbon emission” in the demolition and recycling stage was about −4%. Carbon emissions of the remaining three stages were minimal at about < 2%. This study established the whole life-cycle carbon-emission indices of residential prefabricated buildings, with average annual carbon emission per unit area at around 105.88 kgCO2/(m2.a), and computed values for individual building stages. For comparison, a completely cast-in-place building was studied, with average annual carbon emission per unit area at around 130.79 kgCO2/(m2.a), significantly higher than the PCB. The study contributed to knowledge by furnishing the holistic carbon footprint calculation method and establishing the empirical patterns and trends of carbon emissions of PCB. The findings could inform construction projects' carbon-emission control and achieve the construction industry's green and low-carbon goals.

A Survey on Characterizing the Empirical Analysis, Proposed Approaches, and Research Trends for Architectural Decay

This decay does not happen overnight, but rather it's cumulative impacts over time. Even though the prior works covered various aspects concerning maintaining architecture from erosion, however, the common factor in the existing studies does not provide full detail about the empirical analysis, perceptions of the proposed approaches, and trendy track of the architectural decay research. There are existing review studies on analysis techniques and approaches of various architectural decay, but to the best of our knowledge, insufficient focus was considered on precisely the description of the empirical analysis, techniques, approaches, and trends for studies that have been evaluated concerning architectural decay. This study provides a comprehensive survey and detailed investigation on the empirical analysis regarding research design and its method, existing approaches analysis to identify used strategies, and trendy research to clarify trendy track for researchers and what evolves from a meaningful essential idea concerns the researchers within the architectural degradation field.

The cartel party - the end of democratic party evolution?

ABSTRACT Contrary to some interpretations, the cartel party model was never assumed to be an equilibrium end-state. Rather, it was postulated, on the one hand, that cartelization would lead to its own opposition in the form now identified as anti-party-system parties, and on the other hand, that this development would lead to further adaptation on the part of the cartel parties themselves. But is this continuing evolution leading to parties that are more democratic, either in their internal operation or in their contribution to the wider political system? While a number of empirical trends are evident, whether they represent ‘democratic evolution’ depends in large measure upon how one understands democracy itself.

Condors in space: an individual-based population model for California condor reintroduction planning

ContextExisting demographic models of California condors have not simultaneously considered individual condor movement paths, the distribution and juxtaposition of release sites, habitat components, or the spatial distribution of threats.ObjectivesOur objectives were to develop a dynamic spatially explicit and individual-based model (IBM) of California condor demography and to evaluate its ability to replicate empirical data on demography and distribution from California (1995–2019).MethodsWe built a female-only spatially explicit California condor IBM in HexSim, using a daily timestep that allowed us to simulate the foraging behavior of condors, changes in food distribution and availability, and the ephemeral threat of lead in decaying food resources.ResultsSimulated population size was highly correlated with annual population census data once the population became established with > 50 females (r2 = 0.99). Mean simulated fecundity and mortality estimates were not significantly different from empirical trends (p > 0.05), although empirical data had higher interannual variability. The geographic distribution of modeled condors was similar to the empirical distribution with an overall accuracy of 79%, a commission error of 27%, and an omission error of 9%. Simulated movement density corresponded moderately well to the density of observed GPS locations (weighted kappa = 0.44).ConclusionsWe developed a spatially explicit California condor IBM that is well-calibrated to empirical data from California. Given its mechanistic underpinnings and flexibility to incorporate a variety of spatial and demographic inputs, we expect our model to be useful for assessing the relative risks and benefits of future condor reintroduction and management scenarios.

Uneven Growth: Automation's Impact on Income and Wealth Inequality

The benefits of new technologies accrue not only to high‐skilled labor but also to owners of capital in the form of higher capital incomes. This increases inequality. To make this argument, we develop a tractable theory that links technology to the distribution of income and wealth—and not just that of wages—and use it to study the distributional effects of automation. We isolate a new theoretical mechanism: automation increases inequality by raising returns to wealth. The flip side of such return movements is that automation can lead to stagnant wages and, therefore, stagnant incomes at the bottom of the distribution. We use a multiasset model extension to confront differing empirical trends in returns to productive and safe assets and show that the relevant return measures have increased over time. Automation can account for part of the observed trends in income and wealth inequality.

Indicative findings from a study of information behaviour in digital business ideation: insights from the developing world

nt="Indicative findings from a study of information behaviour in digital business ideationr" /&gt; &lt;meta name="description" content="Though a wealth of information behaviour research has been undertaken in various contexts over the years, less has been done on entrepreneurship. In particular, there is a lack of literature around the ideation component in the early stages of business formation. This study seeks to address the theoretical and empirical gap within this research stream, bringing together information, innovation, and creativity theory, as lenses through which to explore the phenomenon. Twelve semi-structured interviews were conducted with business founders. Participants operated in a variety of segments of the digital technology spectrum. Critical Realism and Grounded Theory were used as theoretical and practical data techniques, using abduction in the theoretical redescription of concepts (codes) identified in the empirical data, and retroduction to identify the necessary contextual conditions for a particular causal mechanism to take effect and result in the observed empirical trends. Indicative results show several societal and personal factors play a role in shaping the information behaviour of digital entrepreneurs. These factors include but are not limited to poverty, parenting, and unequal power between mentors and mentees. While this research project is ongoing, early findings emerge in previously unexplored aspects of information behaviour such as the importance of reading passion and early exposure to digital devices amongst digital entrepreneurs.

Damages and ISDS Reform: Between Procedure and Substance

Abstract Monetary damages is the ordinary remedy in investor-state dispute settlement (ISDS). As such, arbitral practice relating to damages has direct, practical relevance for states and investors. The size of damages awards is also amongst the core critiques of ISDS. It is somewhat surprising, then, that the issue of damages has not figured prominently in discussions on reform of investment treaties and the ISDS mechanism, including those currently underway in Working Group III of the United Nations Commission on International Trade Law (UNCITRAL). In this context, this article makes three contributions. First, it provides an overview of empirical trends in damages in ISDS. Secondly, it considers the extent to which tribunals’ approaches to damages raise the sorts of concerns with ISDS identified by UNCITRAL Working Group III, focusing specifically on concerns of correctness, consistency, legal and expert costs, and independence and impartiality. Thirdly, it identifies a range of possible procedural and substantive reform options that might alleviate these concerns.

China's outward investment activity: Ambiguous findings in the literature and empirical trends in greenfield investments

AbstractWith the rapid increase of China's outward foreign direct investments (OFDIs) since the early‐2000s, a growing body of literature has developed that investigates investment processes and their underlying motivations and tendencies. Three important findings emerge from this literature. First, it has been noted that the generation of market and resource access have been key drivers of investment activity. Second, China's OFDIs have accordingly focused on mature manufacturing and natural resource sectors. Third, a large proportion of OFDIs is assumed to have been directed to neighboring countries in East Asia or other developing economies, for instance in Africa. However, a literature review reveals limitations in prior studies with respect to measurement biases, database incompatibilities, the neglect of a knowledge perspective, and a lack of sectoral differentiation. Descriptive analysis based on a comprehensive firm‐level data set of greenfield investments shows that previous findings are only partial. According to fDi Markets data from 2003 to 2016, OFDIs from China are more diversified and widespread than assumed. Many recent investments have a distinct knowledge motivation, are focused on high‐tech and business service sectors and non‐manufacturing functions, and are directed toward developed economies.

Majority Voting, Progressive Taxation, and Income Inequality

ABSTRACT A challenge for the political economy literature is why the average tax rate becomes less progressive while wage inequality increases with the skill premium. This paper contributes to the literature by developing a majority voting equilibrium model in which households choose their most preferred tax schedule under increasing inequality. We find that majority voting favors a marginal tax rate to fund transfers, where the average tax rate increases with income, when the median skill level lies below the mean skill level. The average tax rate becomes less progressive when required government revenue relative to mean skill increases. These findings reconcile the literature with recent empirical trends and are robust to relaxing the assumptions of exogenous government spending and endogenous labor supply.

On the Role of Serial Correlation and Field Significance in Detecting Changes in Extreme Precipitation Frequency

AbstractStatistical trend analyses of observed precipitation (P) time series are key to validate theoretical arguments and climate projections suggesting that extreme P will increase in a warmer climate. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered. Here, we investigate these two aspects focusing on extreme P frequencies derived from 100‐year daily records of 1,087 worldwide gauges of the Global Historical Climatology Network. For this aim, we perform Monte Carlo experiments based on count time series generated with the Poisson integer autoregressive model and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (a) Empirical autocorrelations are consistent with those of uncorrelated and stationary or nonstationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; incorporating the impact of serial correlation in trend tests on extreme P frequency has then limited impacts on tests' performance. (b) Accounting for field significance improves interpretation of test results by limiting type‐I errors, but it also decreases test power; results of local tests could complement field significance outcomes and help identify weak trend signals where several trends of coherent sign are detected. (c) Based on these findings, evident patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Eurasia, and central Australia (southwestern America, southern Europe, and southern Australia). The methodological insights of this work support trend analyses of any hydroclimatic variable.

From the theater to the hippodrome: A critique of Jeffrey Green’s theory of plebiscitary democracy and an alternative

The article argues that the theory of plebiscitary leader democracy (PLD), originally developed by Max Weber, is in its somewhat rejuvenated version a helpful framework in interpreting longer-term and more recent empirical trends in contemporary democracies, such as the growing personalization of politics, the emergence of populist leaders, rising levels of polarization, and the growing importance of social media. However, to realize the potential of the theory, it should be detached from Jeffrey Green’s most original, yet insufficiently realistic elaboration of plebiscitary democracy that he made a decade ago. The article argues that instead of a passive and unifiable entity, the citizenry should be thought of as reactive and deeply divided, a setting which can be characterized by the metaphor of the infamous Byzantine chariot races rather than that of the theater, implicit in Green’s theory. Plebiscitary democracy should be thought of as representational, where popular control is manifested as the veto power of the popular voice. Additionally, despite its realist minimalism, the theory we propose may still have some critical potential, because it adopts the refurbished ideal of competition. The article closes by identifying further avenues of theoretization leading towards a more elaborate view of PLD.

Human trafficking indicators: A new dataset

ABSTRACT This article describes the Human Trafficking Indicators (HTI) dataset, a new resource for research on the causes of, and policy responses to, human trafficking. HTI includes country-year level information on forty-six variables for up to 184 countries from 2000 to 2017. It is the first dataset to broadly capture different trafficking types and disaggregated measures of government responses. It includes seven types of trafficking including forced labor, sexual exploitation, domestic servitude, and debt bondage. The HTI also includes eighteen measures of a government’s prosecution, protection, and prevention efforts. This paper presents an overview of the dataset, explains how it differs from other sources, describes several empirical trends, and highlights HTI’s potential uses with a brief empirical example.

A Scoping Review of Health Outcomes Among Transgender Migrants.

Transgender people constitute diverse populations who experience a range of adverse health outcomes. Despite increasing awareness of adverse health outcomes among migrant populations, there has been a dearth of studies focused on the health of transgender migrants. The goal of this scoping review was to describe common themes and empirical trends in research on the health of transgender migrants and identify gaps for future research and programming. Using a systematic review protocol, we searched PubMed, Embase, Scopus, PsycINFO, CINAHL, and Web of Science with a combination of terms to identify empirical articles that examined health outcomes among transgender migrants. The search included studies published as of May 2019. Twenty of 1666 identified records met inclusion criteria. All studies were cross-sectional, and 50% were quantitative designs, 45% were qualitative designs, and 5% were mixed-methods designs. The majority reported on sexually transmitted infections (55%), violence (40%), and mental health (35%). Qualitative studies were generally high quality, while many quantitative studies had high risk of bias. While some adverse health outcomes may abate, stigmatizing social conditions continue to impact transgender migrant's health postmigration. Anti-transgender and anti-immigrant stigma may contribute to adverse health outcomes for transgender migrants. Additional research using rigorous inclusive methods to survey a broader range of health domains is needed. The lives of transgender migrants are continuously upended by oppressive policies; therefore, it is vital to continue to expand the breadth of transgender health research.

Unlocking the Origins of Ultracool Dwarf Radio Emission

Abstract Empirical trends in stellar X-ray and radio luminosities suggest that low-mass ultracool dwarfs (UCDs) should not produce significant radio emission. Defying these expectations, strong nonthermal emission has been observed in a few UCDs in the 1–10 GHz range, with a variable component often attributed to global aurorae and a steady component attributed to other processes, such as gyrosynchrotron emission. While both auroral and gyrosynchrotron emission peak near the critical frequency, only the latter radiation is expected to extend into millimeter wavelengths. We present Atacama Large Millimetre/Submillimeter Array (ALMA) 97.5 GHz and Very Large Array 33 GHz observations of a small survey of 5 UCDs. LP 349-25, LSR J1835+3259, and NLTT 33370 were detected at 97.5 GHz, while LP 423-31 and LP 415-20 resulted in nondetections at 33 GHz. A significant flare was observed in NLTT 33370, which reached a peak flux of 4880 ± 360 μJy, exceeding the quiescent flux by nearly an order of magnitude and lasting 20 s. These ALMA observations show bright 97.5 GHz emission with spectral indices ranging from α = 0.76 to α = −0.29, suggestive of optically thin gyrosynchrotron emission. If such emission traces magnetic reconnection events, then this could have consequences for both UCD magnetic models and the atmospheric stability of planets in orbit around them. Overall, our results provide confirmation that gyrosynchrotron radiation in radio-loud UCDs can remain detectable into the millimeter regime.