Non-conventional Data Research Articles

Curbing the crises, gauging the gap: measuring the nexus between gender and the environment

Abstract Women and men interact with the environment differently. Although data on the gender and environment nexus is only recently starting to become more widely available, existing evidence shows that, in many countries, women and men’s livelihoods, roles at home, and decision-making actions are widely different, and thus so are their contributions to environmental conservation and degradation. Simiarly, climate change, pollution and biodiversity loss affect women and men in different ways, but the limited availability of related data still prevents a full understanding of the multiplicity of ways in which these differences manifest. In recent years, numerous methodological advancements have taken place, which are enabling the production of gender and environment statistics to better understand these connections. From dedicated surveys to uses of non-conventional data sources, empirical evidence on this topic is increasingly available. Still, substantial efforts are needed to fill existing data gaps and gain a thorough understanding of the connections. This paper showcases available evidence on various aspects of the gender-environment nexus, and highlights possible solutions to fill remaining data gaps.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections

Forecasting mortality rates is crucial for evaluating life insurance company solvency, especially amid disruptions caused by phenomena like COVID-19. The Lee–Carter model is commonly employed in mortality modelling; however, extensions that can encompass count data with diverse distributions, such as the Generalized Autoregressive Score (GAS) model utilizing the COM–Poisson distribution, exhibit potential for enhancing time-to-event forecasting accuracy. Using mortality data from 29 countries, this research evaluates various distributions and determines that the COM–Poisson model surpasses the Poisson, binomial, and negative binomial distributions in forecasting mortality rates. The one-step forecasting capability of the GAS model offers distinct advantages, while the COM–Poisson distribution demonstrates enhanced flexibility and versatility by accommodating various distributions, including Poisson and negative binomial. Ultimately, the study determines that the COM–Poisson GAS model is an effective instrument for examining time series data on mortality rates, particularly when facing time-varying parameters and non-conventional data distributions.

Reflexivity of participants of Football for Development project: experiential learning as delivery methodology of global education

The power of sport to affect social change is widely acknowledged. However, little empirical research has explored the use of sport in engaging young people in global education. This article fills the gap by exploring the role of sport using experiential learning tools as delivery methodology for global education in the context of Central Europe (Czechia). The study focuses on the experiential learning process of the study participants, a mixed intercultural team of Czech and Kenyan youth ambassadors participating in a Football for Development project with the aim of engendering cultural understanding, respecting diversity as positive and valuing differences as beneficial to society. The study benefited from a newly developed non-conventional data collection tool enhancing the research methodology and allowing for the facilitation of the participants’ ability to discover and process the experience-related learning through reflexivity. By showing the changes in knowledge, skills, values and attitudes, our findings indicate how the sport for development programme contributes to creating a semi-conscious learning environment for global education.

Assessing the quality of random number generators through neural networks

In this paper we address the use of Neural Networks (NNs) for the assessment of the quality and hence safety of several Random Number Generators (RNGs), focusing both on the vulnerability of classical Pseudo Random Number Generators (PRNGs), such as Linear Congruential Generators (LCGs) and the RC4 algorithm, and extending our analysis to non-conventional data sources, such as Quantum Random Number Generators (QRNGs) based on Vertical-Cavity Surface-Emitting Laser (VCSEL). Among the results found, we have classified the generators based on the capability of the NN to distinguish between the RNG and a Golden Standard RNG (GSRNG). We show that sequences from simple PRNGs like LCGs and RC4 can be distinguished from the GSRNG. We also show that sequences from LCG on elliptic curves and VCSEL-based QRNG can not be distinguished from the GSRNG even with the biggest long-short term memory or convolutional neural networks (CNNs) that we have considered. We underline the fundamental role of design decisions in enhancing the safety of RNGs. The influence of network architecture design and associated hyper-parameters variations was also explored. We show that longer sequence lengths and CNNs are more effective for discriminating RNGs against the GSRNG. Moreover, in the prediction domain, the proposed model is able to deftly distinguish between the raw data of our QRNG and data from the GSRNG exhibiting a cross-entropy error of 0.52 on the test data-set used. All these findings reveal the potential of NNs to enhance the security of RNGs, while highlighting the robustness of certain QRNGs, in particular the VCSEL-based variants, for high-quality random number generation applications.

Evaluating tuberculosis treatment outcomes in Haiti from 2018 to 2019: A competing risk analysis

ObjectivesThis study assesses tuberculosis (TB) treatment outcomes in Haiti. MethodsData from drug-susceptible patients with TB (2018-2019) were analyzed using the Fine & Gray model with multiple imputation. ResultsOf the 16,545 patients, 14.7% had concurrent HIV coinfection, with a 66.2% success rate. The median treatment duration was 5 months, with patients averaging 30 years (with an interquartile range of 22-42 years). The estimated hazard of achieving a successful treatment outcome decreased by 2.5% and 8.1% for patients aged 45 and 60 years, respectively, compared with patients aged 30 years. Male patients had a 6.5% lower estimated hazard of success than their female counterparts. In addition, patients coinfected with HIV experienced a 35.3% reduction in the estimated hazard of achieving a successful treatment outcome compared with those with a negative HIV serologic status. ConclusionsIntegrated health care approaches should be implemented, incorporating innovative solutions, such as machine learning algorithms combined with geographic information systems and non-conventional data sources (including social media), to identify TB hotspots and high-burden households.

Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data

Fully or partially fare-free public transport (FFPT) is a measure to make public transport (PT) more attractive and affordable. Cities worldwide are experimenting with a variety of fare discount policies, which will lead to spatiotemporal changes in mobility patterns, including crowding in PT stations. However, the response of PT stations to these policies can vary due to the heterogeneity in their surrounding built environment and other factors. Non-conventional data sources could be used to better model and understand these spatiotemporal dynamics. In this study, we propose a three-step methodological framework to understand the impact of FFPT or extreme fare discount interventions on PT demand patterns, specifically focusing on crowding patterns in PT stations, using crowdsensing data. First, we design a busyness-based similarity measure that leverages the histogram method to capture changes in crowding patterns. Then, we employ a Gaussian Mixture Model (GMM) to cluster PT stations based on their crowding pattern deviations at different stages of policy implementation. This clustering step enables the identification of distinct station types based on their response to the policy. Finally, we train a LightGBM model to learn the relationship between crowding pattern changes and the spatial–temporal characteristics of PT stations, using the busyness-based station types identified by GMM as labels. We apply our methodology to a public transport experiment in Germany during the summer of 2022 when the country introduced a monthly “9-EUR” ticket valid on local and regional PT nationwide. The clustering results show three station types: unaffected, mildly stimulated, and intensely stimulated stations. Furthermore, the classifier indicates that the station’s location, activity options near the station, and population within the adjacent area of the station, and the crowding patterns under normal operations (before policy implementation) play a significant role in the heterogeneity of the 9-EUR ticket’s impact. The insights gained from our study can help planners better understand and manage the crowding at PT stations during fare interventions.

Predicting Infectious Disease Outbreaks with Machine Learning and Epidemiological Data

Over the past several years, there has been a notable shift in the international public health arena, mostly driven by the use of machine learning methodologies and epidemiological data for the purpose of forecasting and controlling outbreaks of infectious diseases. This study explores the changing paradigm of disease outbreak prediction by examining current advancements and emerging patterns in the field of machine learning and epidemiology. In this paper, we explore the complex procedure of forecasting infectious disease outbreaks, a task of significant significance for global public health authorities. This paper examines the crucial role of machine learning algorithms in this undertaking, elucidating their capacity to analyze extensive and heterogeneous datasets in order to produce significant insights and predictions Our inquiry spans multiple facets of this complex topic. This study examines the transformative impact of machine learning models, namely deep learning and ensemble approaches, on the field. The individuals in question have exhibited remarkable proficiency in recognizing patterns, establishing correlations, and formulating predictions by utilizing past data. Consequently, this has greatly contributed to the prompt identification and readiness for potential outbreaks. Moreover, our study involves the incorporation of epidemiological data, including case reports, genetic sequencing, and population dynamics, into the machine learning architecture. This study investigates the enhanced predictive accuracy and improved comprehension of disease dynamics resulting from the integration of data-driven models and expert knowledge from the field of epidemiology. The integration of different approaches is of utmost importance when it comes to effectively tackling the distinct characteristics and problems presented by diverse infectious illnesses. Additionally, the research emphasizes the significance of incorporating a wide range of data sources, including not only data related to human health, but also environmental factors, socio-economic metrics, and patterns of human mobility. Non-conventional data sources provide essential contextual information for comprehending the dynamics of disease transmission, hence enhancing the robustness and comprehensiveness of forecasts.

Cheques or dating scams? Online fraud themes in hip-hop songs across popular music apps

How do hip-hop songs produced from 2017 to 2023 depict and rationalize online fraud? This study examines the depiction of online fraudsters in thirty-three Nigerian hip-hop songs on nine popular streaming platforms such as Spotify, Deezer, iTunes, SoundCloud, Apple Music, and YouTube. Using a directed approach to qualitative content analysis, we coded lyrics based on the moral disengagement mechanism and core themes derived from existing literature. Our findings shed light on how songs (a) justify the fraudulent actions of online fraudsters, (b) blame and dehumanize victims of online romance fraud, (c) glamorize online fraud, and (d) provide insights into prevailing attitudes, indigenous linguistics, and worldviews regarding cybercrime victimization. Specifically, our analysis shows the presence of moral disengagement mechanisms, including "dehumanization," “minimizing own agency,” and "displacement of responsibility." Moreover, we identify core contextual themes pertinent to online fraud in the Nigerian context: (a) "cyber spiritualism" and (b) "the North-South divide." This research underscores the global influence of musicians whose songs are easily accessible on diverse music platforms in trivializing the plight of online fraud victims. We argue that these artists' persuasive messages not only downplay but also satirize the victims' predicaments, thus contributing to the cultural worldviews and dimensions of online fraud. We conclude by underscoring these songs' capacity to actualize, reshape, and actively engage with the narratives of glamorization, justification, spiritualism, colonial legacies, gender dynamics, and societal implications. We recommend that future research endeavors explore non-conventional data sources adept at capturing the intricate fabric of cultural attitudes and dynamics.

A century of nitrogen dynamics in agricultural watersheds of Denmark

Intensive agriculture has been linked to increased nitrogen loads and adverse effects on downstream aquatic ecosystems. Sustained large net nitrogen surpluses have been shown in several contexts to form legacies in soil or waters, which delay the effects of reduction measures. In this study, detailed land use and agricultural statistics were used to reconstruct the annual nitrogen surpluses in three agriculture-dominated watersheds of Denmark (600–2700 km2) with well-drained loamy soils. These surpluses and long-term hydrological records were used as inputs to the process model ELEMeNT to quantify the nitrogen stores and fluxes for 1920–2020. A multi-objective calibration using timeseries of river nitrate loads, as well as other non-conventional data sources, allowed to explore the potential of these different data to constrain the nitrogen cycling model. We found the flux-weighted nitrate concentrations in the root zone percolate below croplands, a dataset not commonly used in calibrating watershed models, to be critical in reducing parameter uncertainty. Groundwater nitrate legacies built up in all three studied watersheds during 1950–1990 corresponding to ∼2% of the surplus (or ∼1 kg N ha yr−1) before they went down at a similar rate during 1990–2015. Over the same periods active soil nitrogen legacies first accumulated by approximately 10% of the surplus (∼5 kg N ha yr−1), before undergoing a commensurate reduction. Both legacies appear to have been the drivers of hysteresis in the diffuse load at the catchments’ outlet and hindrances to reaching water quality goals. Results indicate that the low cropland surpluses enforced during 2008–2015 had a larger impact on the diffuse river loads than the European Union’s untargeted grass set-aside policy of 1993–2008. Collectively, the measures of 1990–2015 are estimated to have reset the diffuse load regimes of the watersheds back to the situation prevailing in the 1960s.

In Search of Climate Crisis in Greece Using Hydrological Data: 404 Not Found

In the context of implementing the European Flood Directive in Greece, a large set of rainfall data was compiled with the principal aim of constructing rainfall intensity–timescale–return period relationships for the entire country. This set included ground rainfall data as well as non-conventional data from reanalyses and satellites. Given the European declaration of climate emergency, along with the establishment of a ministry of climate crisis in Greece, this dataset was also investigated from a climatic perspective using the longest of the data records to assess whether or not they support the climate crisis doctrine. Monte Carlo simulations, along with stationary Hurst–Kolmogorov (HK) stochastic dynamics, were also employed to compare data with theoretical expectations. Rainfall extremes are proven to conform with the statistical expectations under stationarity. The only notable climatic events found are the clustering (reflecting HK dynamics) of water abundance in the 1960s and dry years around 1990, followed by a recovery from drought conditions in recent years.

The Use of Decision Support in Search and Rescue: A Systematic Literature Review

Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdependent assignments. To handle such complexity, decision support systems are used for decision-making and execution of plans within search and rescue operations. Advances in data management solutions and artificial intelligence technologies have provided better opportunities to make more efficient and effective decisions that can lead to improved search and rescue operations. This paper provides findings from a bibliometric mapping and a systematic literature review performed to: (1) identify existing search and rescue processes that use decision support systems, data management solutions, and artificial intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their research contributions to the investigated domain; and (3) investigate the potential for knowledge transfer between application areas. The main findings of this review are that non-conventional data management solutions are commonly used in land rescue operations and that geographical information systems have been integrated with various machine learning approaches for land rescue. However, there is a gap in the existing research on search and rescue decision support at sea, which can motivate future studies within this specific application area.

Enhancing Digital Health Services with Big Data Analytics

Medicine is constantly generating new imaging data, including data from basic research, clinical research, and epidemiology, from health administration and insurance organizations, public health services, and non-conventional data sources such as social media, Internet applications, etc. Healthcare professionals have gained from the integration of big data in many ways, including new tools for decision support, improved clinical research methodologies, treatment efficacy, and personalized care. Finally, there are significant advantages in saving resources and reallocating them to increase productivity and rationalization. In this paper, we will explore how big data can be applied to the field of digital health. We will explain the features of health data, its particularities, and the tools available to use it. In addition, a particular focus is placed on the latest research work that addresses big data analysis in the health domain, as well as the technical and organizational challenges that have been discussed. Finally, we propose a general strategy for medical organizations looking to adopt or leverage big data analytics. Through this study, healthcare organizations and institutions considering the use of big data analytics technology, as well as those already using it, can gain a thorough and comprehensive understanding of the potential use, effective targeting, and expected impact.

Android Based E-Perpus Application to Improve the Quality of Services in Universities

The development of the world of technology every year always increases and makes it easier to do anything in their respective fields. Libraries today must adopt technology this time, namely digital technology. A digital library is an electronic library whose information can be stored and retrieved through digital formats. Digital libraries include a group of interconnected workstations connected to a high-speed network. Previous research has developed a library system with non-conventional data processing and easy service for all library members. Based on the above background, the purpose of this research is to create an Android-Based E-Library Application to Improve Service Quality and Support Distance Learning in Higher Education. Place The object of this research is STMIK Pringsewu. The methodology in this study uses qualitative methods. In terms of data collection, there are several types of data collection methods in qualitative research, namely observation, visual analysis, literature review, and interviews. The results of the research are that the downloaded file is a file with a PDF extension, and the downloaded file is complete according to the books that have been registered in the General and System Categories, which have been tested and functioned properly. Suggestions for further research can be integrated with the Higher Education academic administration system and the need for the role of Lecturers to guide, supervise, and invite students to always be independent and patient in learning so that the knowledge gained can be well received.

Algorithms for Reconstruction of Impedance Spectra from Non-uniformly Sampled Step Responses.

Fast and reliable bioimpedimetric measurements are of growing importance in many practical applications. In this work we used a measurement method in time domain by processing the step response of the biological system under test. In order to decrease the data volume and computation time while retaining all relevant information the step response is sampled non-uniformly. Consequently, fast Fourier transform cannot be directly used for spectrum calculation and non-conventional data processing algorithms for transforming measured data into the frequency domain are required. In this paper we present corresponding computational methods. They are split into two groups. The first group is oriented on calculating the local approximation of the measured step response with a set of proper functions and calculating its spectrum via analytical Fourier transform, thus yielding a relatively versatile approach for estimating the impedance spectrum. In this case, the choice of approximating functions that suit known a priori properties of the measured signals are of great importance. A second group of methods relies on the evaluation of important signal parameters directly in the time domain. In this case we use a priori information about the measurement object in the form of an underlying model. After that the model is fitted to the measured data and thus, parameter values are extracted. Practical aspects, advantages and drawbacks of all considered data processing steps are revealed when applying them to the measurements made with real biological objects.

Use of management strategy evaluation to understand the value of citizen science in managing an iconic California recreational fishery

Data-limited fisheries present significant challenges to fisheries management around the world. Landings data represent the simplest and most common source of fishery information, but length data of harvested species can offer particularly useful insight into the strength of fishing pressure and the status of stocks. However, unbiased length data can be difficult and costly to collect, which highlights a need for understanding the utility of non-conventional data collection programs, such as those generated by community members and citizen science programs to improve management decision-making. In this study, our objective was to understand the value of citizen science for management using the iconic California recreational red abalone (Haliotis rufescens) dive fishery. To accomplish this, we employed management strategy evaluation (MSE) to simulate this benthic invertebrate fishery that was sampled by two different fishery independent survey protocols: a community science survey program led by trained volunteers and a professional science survey program led by a state agency. We subjected the data to a range of uncertainties, by including time-varying life history parameters, environmental variability, and effective sample size scenarios, to understand the management level consequences of each survey methodology. Our results show that community science program data collection can serve as a high-quality data source, can be linked directly to fisheries management decisions via a Harvest Control Rule (HCR), and can be a useful information source even in the absence of conventional data sources. Our results are specific to the California red abalone fishery, but they also highlight the potential broader value of community science data collection programs for improving information content of small-scale and recreational fisheries.

East Java Province GRDP Projection Model Using Night-Time Light Imagery

Economic growth, regional development, and human activities are some of the things that are very strongly related and influence each other. Approaches to forecasting the growth of the three are mostly carried out using both conventional and non-conventional data. Utilization of Nighttime Light Imagery satellite imagery is included in a non-conventional approach to forecasting Gross Regional Domestic Product. This study applies the use of satellite imagery to predict the regional development of East Java Province, to find patterns of agglomeration and the formation of clusters of economic development in the future.

The FAO Data Lab on statistical innovation and the use of big data for the production of international statistics

Data is an extremely important intangible good, but official data is not always available. It may be scarce for many reasons, among which: low statistical capacities, poor funding for data and statistics, weak data dissemination and use culture. A solution to fill data gaps needs to consider that there is data made available on the web, usually coming in an unstructured way, that can be combined with innovative methods to generate relevant information. National and international organisations need to engage with new data sources and methods considering the crisis of traditional data collection systems that causes data gaps. In this light, FAO created in 2019 the “Data Lab for statistical innovation” to fill such gaps by modernising the Organization’s statistical business, which means improving the timeliness and granularity of data collection, providing automated analysis, and capturing early warning signals. It does so through the use of cutting-edge technologies (such as web scraping, text mining, geo-spatial data analysis and artificial intelligence) and by introducing nonconventional data sources (social media, online newspaper articles). This article summarises the experience of the FAO Data Lab and how it has been useful for the Organization to fulfil its mandate.

Migration Responses to the COVID-19 Pandemic: A Case Study of New England Showing Movements down the Urban Hierarchy and Ensuing Impacts on Real Estate Markets

As the COVID-19 pandemic swept across the United States, the media began reporting stories of people leaving cities for rural destinations, setting off frenzied real estate activity in receiving communities. This article builds a case study of New England using nonconventional data collected from mobile devices as a proxy for population to explore the temporal and spatial patterns of movement down the urban hierarchy since the onset of the COVID-19 pandemic. Two research questions guide the analysis: (1) How have urban–rural migration systems in New England shifted since the onset of the COVID-19 pandemic? (2) In what ways have real estate markets been affected by these apparent migration shifts? The analysis reveals shifts of population away from metropolitan core areas of the region and into micropolitan and noncore counties. These population shifts were most pronounced in late summer and fall 2020 with loosening travel restrictions. By the end of 2020, migration systems in New England once more resembled prepandemic patterns. Further, these places down the urban hierarchy consistently showed more substantial increases in real estate activity as reflected in rising prices, reduced inventories, and increased sales volume. These real estate dynamics suggest urban to rural migration during the COVID-19 pandemic might be initiating new waves of rural gentrification.

Big Data in Cardiology: State-of-Art and Future Prospects.

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.