Search

The article is devoted to the problem of underutilization of the country's labor potential in the face of women with young children. The relevance of research in the field of support for families with young children and the development of additional measures to support them is further explained. The authors propose to use a mathematical model based on soft computing for predictive estimation of the number of women with children from 0 to 3 years old, who are ready to go to work, taking into account the age of the child. The calculation of numerical indicators is based on the prediction of the Federal Statistics Service of the Russian Federation for the estimated population of the Russian Federation in the context of ages up to 2035. [ABSTRACT FROM AUTHOR]

Digital government comprises all means to enable governments to interact with their constituents digitally. The metaverse provides a virtual reality environment where various activities can be carried out without physically visiting the places of interest, including the public authorities. Yet, how governments can use the metaverse is unknown. This paper aims to extend the understanding of the metaverse architecture requirements and their suitability for digital public services provision. We used the systematic literature review, experts' assessment using the Delphi method, and quantitative analysis to attain this goal. Our research contributes to the literature by eliciting the structure and composition of the functional and non-functional requirements. The contributions include (1) identification and classification of 50 functional and 16 non-functional metaverse-related architecture requirements, (2) determination and relevancy of 15 most important functional and 6 non-functional requirements for digital public services provision, and (3) suitability assessment of the 21 services recommended for provision in the EU's metaverse platform with the highest potential to attract users. These findings show that governments pose unique requirements on the metaverse. Not all types of services are suitable for providing in the metaverse. Those focused on empowering citizens and helping them to develop are most important. • Metaverse has potential to enhance existing digital public services of e-government. • New users such as disabled people can profit from metaverse services. • The most important functional and non-functional requirements are provided. • Digital public services with the highest potential to attract users are recommended. [ABSTRACT FROM AUTHOR]

Comprehensive marine management approaches, such as ecosystem based management and marine spatial planning, would benefit from quantitative, spatially explicit estimates of the cumulative impact of human activities on marine ecosystems. In this study, a method to map and quantify cumulative impact was applied to estimate the combined impact of multiple stressors on Tauranga Harbour, a large estuary in New Zealand. The impact of eight stressors on seven ecosystems was assessed at a 100 m resolution, using New Zealand-specific expert judgement on the vulnerability of different ecosystems to each stressor. Estimated cumulative impact tended to be highest in the southern basin and inner estuaries, corresponding with sensitive ecosystems and multiple stressors and reflecting what is known about the distribution of pressures in Tauranga Harbour. Using benthic community data as an independent estimate of ecological condition, we had the novel opportunity to validate cumulative impact predicted from the model. Only a weak relationship was found between the estimated cumulative impact and measured ecological condition and several reasons for this are considered. Substitution of a more realistic sediment layer improved model outputs, highlighting the importance of accurate input data, particularly for stressors or ecosystems with high impact weights. Different standardisation methods did not greatly affect the spatial distribution of cumulative impact patterns in the harbour. The study highlights some fundamental issues for consideration when using this cumulative impact mapping approach, such as the importance of involving the expert panel throughout the course of the study and the availability and quality of the data used to construct the model. [ABSTRACT FROM AUTHOR]

The purpose of this study was to work systematically through all known measures for preventing railway suicides and to consider their suitability for the Finnish railway environment. The research method included a selection and grouping of measures, definition of assessment criteria, a literature review and compilation of assessment forms, and a workshop for experts in the field. We assessed 21 measures based on 12 specified criteria. Specifically, the aim of these criteria was to support the identification and structuring of the available information on each measure to be in easily exploitable format for railway stakeholders. The measures were listed in order of priority in three categories based on final assessments from the workshop. The measures categorised as top priority with the highest potential to reduce suicides on Finnish railways included training of railway personnel to identify suicidal people (also called Gatekeeper training), camera surveillance, detection systems (radar, movement sensors, etc.), collaboration between organisations, learning from international experience, cooperation between railway organisations, police and fire and rescue services, and training of mental health providers. This prioritisation, together with the information included in the assessment forms and expert's views related to each measure, support the Finnish railway stakeholders in selecting measures and defining implementation strategies to prevent railway suicides on Finnish railways. The insights of Finnish experts on the effectiveness and potential implementation of these different measures are valuable information also for railway stakeholders in other countries when selecting appropriate measures to prevent railway suicides. The results of this study support the safe and effective functioning of the railway system by adding knowledge that will help effectively prevent railway suicides and loss of life, delays to train traffic, and work-related stress and trauma to railway staff, rescue personnel and eyewitnesses. [ABSTRACT FROM AUTHOR]

Rising global temperatures are a threat to the current state of the Arctic. In particular, permafrost degradation has been impacting the terrestrial cryosphere in many ways, including effects on carbon cycling and the global climate, regional hydrological connectivity and ecosystem dynamics, as well as human health and infrastructure. However, the ability to simulate permafrost dynamics under future climate projections is limited, and model outputs are often associated with large uncertainties. A model structured on a Bayesian Network is presented to address existing limitations in the representation of physically complex processes and the limited availability of observational data. A strength of Bayesian methods over more traditional modeling methods is the ability to integrate various types of evidence (i.e., observations, model outputs, expert assessments) into a single model by mapping the evidence into probability distributions. Here, we outline PermaBN, a new modeling framework, to simulate permafrost thaw in the continuous permafrost region of the Arctic. Pre-validation and expert assessment validation results show that the model produces estimations of permafrost thaw depth that are consistent with current research, i.e., thaw depth increases during the snow-free season under initial conditions favoring warming temperatures, lowered soil moisture conditions, and low active layer ice content. Using a case study from northwestern Canada to evaluate PermaBN, we show that model performance is enhanced when certainty about the system components increases for known scenarios described by observations directly integrated into the model; in this case, insulation properties from vegetation were integrated to the model. Overall, PermaBN could provide informative predictions about permafrost dynamics without high computational cost and with the ability to integrate multiple types of evidence that traditional physics-based models sometimes do not account for, allowing PermaBN to be applied to carbon modeling studies, infrastructure hazard assessments, and policy decisions aimed at mitigation of, and adaptation to, permafrost degradation. • Bayesian networks can integrate various types of evidence into a single model. • Model performance is enhanced when certainty about the system components increases. • Model estimates of permafrost thaw depth consistent with current research. [ABSTRACT FROM AUTHOR]

• Smart city data portals' compliance with the transparency requirements is investigated. • Four levels of maturity are defined to assess transparency of smart city data portals. • Expert assessment is used to assess the transparency of smart city data portals. • Smart city portals are ranked determining their level of transparency maturity. • Open data ecosystem is conceptualized and 5 types of current ecosystems are identified. This paper focuses on the issue of the transparency maturity of open data ecosystems seen as the key for the development and maintenance of sustainable, citizen-centered, and socially resilient smart cities. This study inspects smart cities' data portals and assesses their compliance with transparency requirements for open (government) data. The expert assessment of 34 portals representing 22 smart cities, with 36 features, allowed us to rank them and determine their level of transparency maturity according to four predefined levels of maturity - developing, defined, managed, and integrated. In addition, recommendations for identifying and improving the current maturity level and specific features have been provided. An open data ecosystem in the smart city context has been conceptualized, and its key components were determined. Our definition considers the components of the data-centric and data-driven infrastructure using the systems theory approach. We have defined five predominant types of current open data ecosystems based on prevailing data infrastructure components. The results of this study should contribute to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities. [ABSTRACT FROM AUTHOR]

Abstract: Three hundred and twelve Important Plant Areas (IPAs) have been identified in Italy using a grid-based ranking system to pinpoint areas of high richness and conservation value, alongside the more frequently used expert-based selection of sites. Important Plant Areas are defined as the most important places in the world for wild plant diversity and need to be identified according to common criteria. The main methodological challenges are the lack of recent, easily accessible data for species and habitats and the definition of practical boundaries. The Global Strategy for Plant Conservation (GSPC–CBD) aims to protect 50% of the most important areas for plant diversity and to conserve in situ 60% of the threatened species by 2010. To measure the extent to which the GSPC targets were fulfilled, we assessed the level of protection afforded to the IPAs and species. We identified 351 top ranking cells, which yielded a total of 312IPAs, covering approximately 15% of Italy. More than 80% of the IPAs currently have some form of legal protection and over 60% of the selected species are included in the existing protected areas. The method we are proposing may be used where no systematic data collection program exists. The transition from grids to polygons is an “expert-based passage” that exploits different sources of information, such as species point locality data, vegetation maps and expert-based indications. IPAs fit into a wider conservation context and may be applied to the design of ecological networks, the zonation of existing protected areas and the definition of key biodiversity areas. [ABSTRACT FROM AUTHOR]

Abstract: In engineering system control, human beings can play various key roles, in particular concerning measurement, global assessment and decision. This paper focuses on methods that allow the representation and aggregation of heterogeneous data (sensory evaluations, physical measurements, outputs of mathematical models, etc.) used in a global dam assessment process. It is acknowledged that in such complex systems many of the variables involved are evaluated with uncertainty. We propose a possibility theory-based approach to deal with all the different uncertain pieces of information and propagate them in aggregation models for global dam assessment. Finally, decision-making and communication applications relating to dam safety are presented. [ABSTRACT FROM AUTHOR]

French dairy herds were selected on a national basis through the "Zero Mastitis Objective" Program (ZMP) to display hygiene and milking practices characterizing very low somatic cell score (SCS) herds. The herds selected were stratified in 2 groups. The first group (LOW) included 187 herds within the first 5 percentiles of herds regarding mean SCS for the 36 mo preceding ZMP (36mo SCS). The second group (MED) included 117 herds within the 50 to 55th percentile of herds regarding 36mo SCS. Mean milk SCS was 3.09 in the MED herds vs. 1.99 in the LOW herds, which corresponded to 265,000 and 135,000 cells/mL respectively. Moreover, LOW and MED herds did not change from one SCS category to another during ZMP. Potentially explanatory variables, collected by formally trained dairy management experts through observations from attendance at milking and farm visits, were analyzed using multistep logistic regression models. According to final model and expert observations, the probability for a herd to belong to the LOW group was maximized when: 1) winter cleanliness of dry cow shed was good; 2) use of teat spraying was carried out; and 3) California Mastitis Tests were performed at milking. Moreover, the herd probability of belonging to the MED group was maximized when: 1) air admission at teat cup attachment was observed during milking; 2) winter cleanliness of dry cow shed was poor; and 3) the milker spent time during milking to feed calves. Finally, the study highlighted milking and hygiene variables and attitudes appearing as key practices to control herd SCS through precise and safe milking and more attention paid to individual animals and cleanliness of dry cow shed. [ABSTRACT FROM AUTHOR]

Operators' self-assessment has received limited interest within process control or human-system evaluation. Research on self-assessment has been criticised for poor assessment methodology, and consequently, its status is unclear. This study hypothesised that, given adequate assessment methods (such as task-specific assessment items and scenario replay), we could observe relatively accurate self-assessment results. Eighteen licensed operators and two experts assessed team performance in six nuclear control room scenarios. The results reveal an overall agreement between operators and experts, measured by the intraclass correlation coefficient, ranging from 0.60 to 0.70, which lies close to the intraclass correlation coefficient of 0.75 for the experts. This demonstrates potential for achievement of relatively accurate operator self-assessment for complex work. The agreement varied in a similar manner for both expert agreement and operator-expert agreement across eight performance dimensions. In addition, the operators' self-assessment provided additional information beyond observer assessment in identifying non-acceptable performance items. [ABSTRACT FROM AUTHOR]

• Users of urban parks and beaches differed in age, patterns and frequency of use. • Sampled beaches and parks elicited moderate to large experiences of restoration in their users. • Physical/design features were limitedly associated to psychological restoration. • Place attachment and place identification were the greatest predictors of restoration. Urban beaches and parks are providers of numerous ecosystem services. In the cultural sphere, place bonding and psychological restoration might significantly contribute to the health and well-being of citizens. In this manuscript, we present a study aimed to evaluate the extent to which three urban beaches and three urban parks offered these advantages to a sample of users (n = 429) in the city of Donostia-San Sebastián (Spain). A second aim was to build a predictive model of restoration through both objective and subjective measures. We assessed the design and physical features of the settings using the Natural Environment Scoring Tool (NEST) and gathered a range of information about the users via a paper & pencil questionnaire. The survey included socio-demographics, questions regarding the frequency and patterns of use, and four different psycho-environmental scales: Perceived Restorativeness Scale (PRS), Place Attachment and Identification Scale, and Restoration Outcome Scales (ROS). We found differences regarding the profile of users and the activities carried out in each of the settings. Users of beaches reported higher levels of attachment, identification, and experienced restoration than the participants surveyed in urban parks (p <.001). Regression analyses revealed that the main predictors of experienced restoration where perceived restorativeness (β = 0.49), attachment (β = 0.22), and identification (β = 0.15), whereas the physical/design features of the environment and the routines of use made a negligible contribution in this regard. The results of the regression analyses were extended by conducting dominance and relative weight analyses. [ABSTRACT FROM AUTHOR]

Large-scale palaeoclimate reconstructions can be very sensitive to the proxy records they are based on, and hence to the criteria used to select proxy records. Data selection rarely follows objective criteria that are applicable to all types of proxies, including both low- and high-resolution records. Thus, there is a need for a uniform and transparent approach to assess the suitability of input proxy data for a reconstruction. Here, we develop classification criteria that are applicable to multiple proxy types and evaluate different selection strategies using a network of 62 millennium-long terrestrial hydroclimate proxy records from Monsoon Asia. Our results reveal that robust evidence for a coherent climate signal and high dating accuracy are important criteria for benchmarking the suitability of each proxy record. We determine these criteria by reviewing the literature for each record (rather than screening against instrumental data). We show that the proposed selection approach can yield a network with a stronger common signal. By evaluating the uncertainty and centennial variability of composite reconstructions, from differently selected subsets of the proxy network, it appears beneficial to use suitable proxies stemming from different archives, as well as having a dense network of proxy sites. We suggest that future large-scale palaeoclimate reconstructions might be improved by evaluating proxy networks according to the universal categories presented here and, if indicated, removing less suitable records. This will strengthen the climate signal in the final reconstruction, allowing more precise inferences about past climate variability and more robust comparisons with climate model simulations. • Suitability of proxy records should be estimated based on literature review. • Semi-quantitative criteria: evidence for climate signal and dating accuracy. • Proposed selection approach can yield a network with a stronger common signal. • Multi-proxy approach is beneficial to preserve various frequency domains. [ABSTRACT FROM AUTHOR]