A metaheuristic-based framework for index tracking with practical constraints

Recently, numerous investors have shifted from active strategies to passive strategies because the passive strategy approach affords stable returns over the long term. Index tracking is a popular passive strategy. Over the preceding year, most researchers handled this problem via a two-step procedure. However, such a method is a suboptimal global-local optimization technique that frequently results in uncertainty and poor performance. This paper introduces a framework to address the comprehensive index tracking problem (IPT) with a joint approach based on metaheuristics. The purpose of this approach is to globally optimize this problem, where optimization is measured by the tracking error and excess return. Sparsity, weights, assets under management, transaction fees, the full share restriction, and investment risk diversification are considered in this problem. However, these restrictions increase the complexity of the problem and make it a nondeterministic polynomial-time-hard problem. Metaheuristics compose the principal process of the proposed framework, as they balance a desirable tradeoff between the computational resource utilization and the quality of the obtained solution. This framework enables the constructed model to fit future data and facilitates the application of various metaheuristics. Competitive results are achieved by the proposed metaheuristic-based framework in the presented simulation.

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Islam as Future Religion: What Western Community Say

The paper focuses on Islam as a Future Religion with a Western View of Society. This research is qualitative with an international literature review that discusses Islam as a great religion and the future. Data analysis was carried out by collecting related references and reviewing them article by article to get answers. This study finds that Islam is a highly developed religion with various obstacles and challenges, but Islam is a serious concern for the West.

Teacher Exit Survey Development Through a Researcher–Practitioner Partnership: A Collaborative Approach

This article details the community-engaged research process employed by a researcher–practitioner partnership (RPP) to develop and pilot a common exit survey of teachers from participating school districts at the end of the 2018–2019 school year. This development occurred with input from school district representatives serving on a study team as well as through ongoing conversations with district human resource directors. There were three goals for this process: (1) to develop a common exit survey relevant to local needs with a strong conceptual framework, (2) to increase response rates and establish consistent administration practices in the region, and (3) to inform future data collection and analysis related to the broader RPP study on teacher retention. The resultant instrument articulated nine common categories of reasons for leaving based on analysis and adaptation of regional exit surveys: retirement, personal reasons, teacher preparation, compensation and benefits, career advancement/switch or higher education, community context, district context, school context, and testing and accountability context. Exit survey items are provided with reliability and validity information, and theoretical and practical implications are discussed.

Mental health symptoms during the first months of the COVID-19 outbreak in Norway: A cross-sectional survey study

Background: The COVID-19 pandemic has led to major social and economic changes that could impact public mental health. The main aim of the current study was to investigate mental health in Norway during the COVID-19 outbreak (since the first confirmed case on 26 February 2020). Methods: The results are from the first wave of the data collection (1 April–2 June 2020), which took place during the outbreak along with its initial restrictions. A total of 19,372 (11,883 students) people participated in a cross-sectional web-based survey. Results: A total of 21.8% scored above the cut-off for depression and 23.7% for anxiety. Severity of symptoms was associated with the accumulation of risk factors, such as possible/confirmed infection for oneself or one’s family, female/other sex, students, having mental health problems, increased use of tobacco, increased use of alcohol, less exercise, losing one’s job, suffering economic impact and lower education. Conclusions: COVID-19 could have a negative association with public mental health, especially for certain risk groups. Future data-collection waves will provide further insight into the development of symptoms following the pandemic.

Transformation of <i>n</i>-alkanes from plant to soil: a review

Despite the importance of soil organic matter (SOM) in the global carbon cycle, there remain many open questions regarding its formation and preservation. The study of individual organic compound classes that make up SOM, such as lipid biomarkers including n-alkanes, can provide insight into the cycling of bulk SOM. While studies of lipid biomarkers, particularly n-alkanes, have increased in number in the past few decades, only a limited number have focused on the transformation of these compounds following deposition in soil archives. We performed a systematic review to consolidate the available information on plant-derived n-alkanes and their transformation from plant to soil. Our major findings were (1) a nearly ubiquitous trend of decreased total concentration of n-alkanes either with time in litterbag experiments or with depth in open plant–soil systems and (2) preferential degradation of odd-chain length and shorter chain length n-alkanes represented by a decrease in either carbon preference index (CPI) or odd-over-even predominance (OEP) with depth, indicating degradation of the n-alkane signal or a shift in vegetation composition over time. The review also highlighted a lack of data transparency and standardization across studies of lipid biomarkers, making analysis and synthesis of published data time-consuming and difficult. We recommend that the community move towards more uniform and systematic reporting of biomarker data. Furthermore, as the number of studies examining the complete leaf–litter–soil continuum is very limited as well as unevenly distributed over geographical regions, climate zones, and soil types, future data collection should focus on underrepresented areas as well as quantifying the transformation of n-alkanes through the complete continuum from plant to soil.

Fractal Scaling Properties in Rainfall Time Series: A Case of Thiruvallur District, Tamil Nadu, India

In the present study, the features of rainfall time series (1971–2016) in 9 meteorological regions of Thiruvallur, Tamil Nadu, India that comprises Thiruvallur, Korattur_Dam, Ponneri, Poondi, Red Hills, Sholingur, Thamaraipakkam, Thiruvottiyur and Vallur Anicut were studied. The evaluation of rainfall time series is one of the approaches for efficient hydrological structure design. Characterising and identifying patterns is one of the main objectives of time series analysis. Rainfall is a complex phenomenon, and the temporal variation of this natural phenomenon has been difficult to characterise and quantify due to its randomness. Such dynamical behaviours are present in multiple domains and it is therefore essential to have tools to model them. To solve this problem, fractal analysis based on Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis were employed. The fractal analysis produces estimates of the magnitude of detrended fluctuations at different scales (window sizes) of a time series and assesses the scaling relationship between estimates and time scales. The DFA and (R/S) gives an estimate known as Hurst exponent (H) that assumes self-similarity in the time series. The results of H exponent reveals typical behaviours shown by all the rainfall time series, Thiruvallur and Sholingur rainfall region have H exponent values within 0.5 < H < 1 which is an indication of persistent behaviour or long memory. In this case, a future data point is likely to be followed by a data point preceding it; Ponneri and Poondi have conflicting results based on the two methods, however, their H values are approximately 0.5 showing random walk behaviour in which there is no correlation between any part and a future. Thamaraipakkam, Thiruvottiyur, Vallur Anicut, Korattur Dam and Red Hills have H values less than 0.5 indicating a property called anti-persistent in which an increase will tend to be followed by a decrease or vice versa. Taking into consideration of such features in modelling, rainfall time series could be an exhaustive rainfall model. Finding appropriate models to estimate and predict future rainfalls is the core idea of this study for future research.

Ethnic inequalities in health in later life, 1993–2017: the persistence of health disadvantage over more than two decades

Ethnic inequalities in health and wellbeing across the early and mid-lifecourse have been well-documented in the United Kingdom. What is less known is the prevalence and persistence of ethnic inequalities in health in later life. There is a large empirical gap focusing on older ethnic minority people in ethnicity and ageing research. In this paper, we take a novel approach to address data limitations by harmonising six nationally representative social survey datasets that span more than two decades. We investigate ethnic inequalities in health in later life, and we examine the effects of socio-economic position and racial discrimination in explaining health inequalities. The central finding is the persistence of stark and significant ethnic inequalities in limiting long-term illness and self-rated health between 1993 and 2017. These inequalities tend to be greater in older ages, and are partially explained by contemporaneous measures of socio-economic position, racism, and discrimination. Future data collection endeavours must better represent older ethnic minority populations and enable more detailed analyses of the accumulation of socio-economic disadvantage and exposure to racism over the lifecourse, and its effects on poorer health outcomes in later life.

Data breach recovery areas: an exploration of organization's recovery strategies for surviving data breaches

PurposeData breaches are an increasing phenomenon in today's digital society. Despite the preparations an organization must take to prevent a data breach, it is still necessary to develop strategies in the event of a data breach. This paper explores the key recovery areas necessary for data breach recovery.Design/methodology/approachStakeholder theory and three recovery areas (customer, employee and process recovery) are proposed as necessary theoretical lens to study data breach recovery. Three data breach cases (Anthem, Equifax, and Citrix) were presented to provide merit to the argument of the proposed theoretical foundations of stakeholder theory and recovery areas for data breach recovery research.FindingsInsights from these cases reveal four areas of recovery are necessary for data breach recovery – customer recovery, employee recovery, process recovery and regulatory recovery.Originality/valueThese areas are presented in the data recovery areas model and are necessary for: (1) organizations to focus on these areas when resolving data breaches and (2) future data breach recovery researchers in developing their research in the field.