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Decisions about which processes to include in a population model can have substantial impact on estimates of population trends and extinction risk. This is particularly important for species of conservation concern, whose conservation status is decided in part on the basis of these models.In their recent paper, Meehan and Crossley (2023) Insect Conservation and Diversity, 16, 566–573 revisit a time series of overwintering monarch butterfly abundances, which previous assessments had characterised as rapidly declining and at risk of extinction. The authors, in contrast, reported no evidence for declines in the past 10 years and characterised extinction risk as low.A primary reason for the difference between these conclusions was that Meehan and Crossley used a more complex model that included a parameter for density dependence. While negative density dependence may play a role in monarch population regulation, there are a variety of unresolved issues with how and if density dependence should be included in models of monarch populations, including widely known issues with separating observation error from density dependence in noisy time series and taxon‐specific issues with fitting models to data surveyed every fourth generation and pooled at continental scales.These issues make the conclusions of Meehan and Crossley about monarch population viability much less robust than implied in their article. They do not provide convincing evidence that density dependence reduces extinction risk in monarch butterflies. Our commentary supports their general conclusion that population viability projections depend on model assumptions, but the ways in which monarch butterfly populations are regulated remains an open question. [ABSTRACT FROM AUTHOR]

This study presents a novel status assessment method for district heating (DH) pipelines in operation, which we call "Pipeopsy" (a biopsy for pipes). The method evaluates adhesion strength between the service pipe and polyurethane (PUR) insulation, which is a crucial property for the durability of DH pipes and the extent of degradation of PUR foam closest to the service pipe. This method is based on three parts: (1) measuring adhesion strength and taking samples of the foam, (2) analyzing the foam in a laboratory using Fourier-transform infrared (FTIR) spectroscopy, and (3) restoring pipeline by replacing the foam and sealing the casing by welding polyethylene plugs in the holes. Temperature dependence and measurement accuracy of the shear strength test method have also been examined, as well as correlation with the standard axial shear strength test method. The shear strength of the aged pipes shows no temperature dependence, while the quotient between the value produced with the plug method and axial method is 3.1. Compared with the standard test methods, the advantages of Pipeopsy involve small cost, less damage to pipes, and the use of simple mobile tools for taking samples and performing measurements. Importantly, testing can be performed without shutting down the operation of DH pipelines. Furthermore, the method provides not only the information on adhesion strength but also information on the extent of chemical degradation in PUR. This combination of information provides a more detailed picture of the status of pipes and can be used to make a prediction about the remaining lifetime. Pipeopsy produces many results in a short time, and better statistics, which provide a solid basis for decision-makers focused on the maintenance of DH pipes or for applying artificial intelligence. Practical Applications: District heating (DH) pipes consist of a steel pipe surrounded by insulating foam and a casing. This design requires satisfactory adhesion between the components to achieve good long-term performance. The use of DH pipes in a reliable and cost-effective way as long as possible requires planned and selective replacement of worn-out parts. A prerequisite to accomplish this is reliable information about the functional status and remaining service life of different parts of existing networks. Pipeopsy is a new field test method that provides valuable information on whether adhesion strength between insulation foam and steel pipe is at a satisfactory level. In addition, chemical analysis provides information on the degree of degradation of the foam closest to the steel pipe. If a similar return pipe can be evaluated, conclusions can be drawn about the extent of reduction in adhesion and whether it is due to degradation of the insulation foam. With this information, the remaining lifetime of the supply pipe can be estimated. Using the Pipeopsy method, testing can be performed at a small cost, using simple mobile tools and without shutting down the operation. Therefore, this method is the only one of its kind that can be used on a large scale. [ABSTRACT FROM AUTHOR]

Voltage instability, power imbalance, and unreliability are caused mainly by equipment failure in the distribution system, so it is important to accurately and quickly assess the status of distribution network equipment. However, it is challenging to detect equipment failures, the traditional XGBoost algorithm is unsuitable because some evaluation indices are incompetent to quantify. To address these issues, we propose a fast evaluation method for the state of electrical distribution equipment based on fuzzy decision-making. Firstly, key indices are selected from the multi-source equipment information. Secondly, this paper constructs the mapping between key indices and equipment status scores by combining the fuzzy iterative method and the XGBoost algorithm. Finally, the proposed assessment model is confirmed by using the distribution transformers as an example. The result shows that the proposed multi-source information assessment method can quickly and accurately determine the operation status of electrical distribution equipment, and the proposed method has better accuracy than the traditional method. [ABSTRACT FROM AUTHOR]

To improve the maintenance capability of key traction power supply equipment, a traction transformer service health management system suitable for railways has been designed. The advantages of MATLAB in scientific calculation and C# in system interface development were complemented. The basic data ledger was established by using Mysql database and cloud server, and the state evaluation model of main transformer mechanism and experience was constructed by driving the historical test data of transformer in traction substation. The mechanism model adopts the analytic hierarchy process (AHP)-entropy weight method to determine the subjective and objective weights, chooses the cloud model to determine the membership degree of each index, and finally carries out fuzzy operation to get the appropriate comments. Principal component analysis-whale optimization algorithm-support vector machines(PCA-WOA-SVM) was selected for joint classification evaluation, and synthetic minority over-sampling technique(SMOTE) was to oversampling the dataset to balance the integrity of the sample. The results show that the recognition accuracy after dataset equalization is 96. 67%, which is 5% higher than processing before. After verification, the hierarchical joint evaluation provides a new approach for conducting condition assessment and maintenance strategy formulation of traction transformers, reflecting the reliability and accuracy of the model and system. [ABSTRACT FROM AUTHOR]

Voltage instability, power imbalance, and unreliability are caused mainly by equipment failure in the distribution system, so it is important to accurately and quickly assess the status of distribution network equipment. However, it is challenging to detect equipment failures, the traditional XGBoost algorithm is unsuitable because some evaluation indices are incompetent to quantify. To address these issues, we propose a fast evaluation method for the state of electrical distribution equipment based on fuzzy decision-making. Firstly, key indices are selected from the multi-source equipment information. Secondly, this paper constructs the mapping between key indices and equipment status scores by combining the fuzzy iterative method and the XGBoost algorithm. Finally, the proposed assessment model is confirmed by using the distribution transformers as an example. The result shows that the proposed multi-source information assessment method can quickly and accurately determine the operation status of electrical distribution equipment, and the proposed method has better accuracy than the traditional method.

The intensive use of non-renewable production resources and the associated growing environmental pollution forces us to look for new methods of halting these negative trends. Circular economy is one such method. This phenomenon is the subject of numerous studies attempting to assess its condition at both the micro and macro levels, the implementation of circular economy strategies, its environmental impact, the context of waste generation, etc. On the other hand, little attention is paid to assessing its efficiency. Technological efficiency, i.e., the relationship between material footprint and waste generation, is of particular importance, since the amount of waste generated primarily depends on the technology used at the time of production. Thus, this paper aims to analyse and assess the development of technological efficiency over a certain period of time. Further, it is necessary not only to build on the results of development at the end of this period, but also to assess the extent of the changes that have taken place during it in order to reflect the actual state of affairs. Thus, the final result takes into account both the intensity of the development of technological efficiency and the importance of its current state to the overall development process. [ABSTRACT FROM AUTHOR]

Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

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In the condition evaluation of the high-voltage SF6 circuit breaker, contact resistance and mass loss have a significant impact on the arc contact. To that end, this paper proposes a method based on quantum particle swarm optimization and support vector regression (QPSO-SVR), the implementation of which can effectively predict the contact resistance increment and mass loss of the circuit breaker arc contacts under different arc current conditions, and the best support vector regression (SVR) algorithm training parameters are obtained through experimental data. To validate the proposed method’s accuracy, it is compared to other prediction methods, and the results show that the QPSO-SVR method has good predictive ability for experimental data under different discharge parameters. The relative error of prediction for contact resistance increment is 3.023%, and the relative error of prediction for mass loss is 4.61%, indicating good accuracy and robustness. It can serve as a reference for the maintenance of high-voltage SF6 circuit breakers, which is useful. It is of great significance to the maintenance of SF6 circuit breaker.

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies. [ABSTRACT FROM AUTHOR]

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transmission line is an important channel for the electric energy transmission in the power system. Whether the stable transmission of electric energy is related to key issues such as people’s livelihood and industry, so it is of great significance to evaluate the operation status of the transmission line. Aiming at the shortcomings of the current evaluation methods for the operation status of transmission lines, this paper proposes a comprehensive evaluation method for the operation status of transmission lines based on the improved combination weighting evaluation model. By constructing a transmission line status indication system based on transmission line standards and specifications, comprehensively considering subjective weights and objective weights, the operational health status of the 6 transmission lines in actual operation is calculated, and the calculation results are compared with the evaluation results of the national grid industry standards. In contrast, this method fully combines the advantages of the improved AHP method and the CRITIC method. While retaining the subjective opinions of experts, it can well avoid the phenomenon of unbalanced weights. Compared with the traditional standard evaluation method, it is more in line with objective reality. The evaluation and early warning of the operation status of transmission lines are of great guiding significance.

The accurate condition assessment of wind turbines greatly influences the refined asset management and maintenance scheduling of wind farms. To address the challenges of existing assessment methods in selecting the reliability value and determining wind turbine status levels of being in transition, this study proposes a wind turbine condition evaluation method based on asymmetric proximity. Firstly, the state evaluation index system consisting of the wind turbine performance and output state indices is constructed, and the weighting factors are calculated comprehensively by integrating the subjective and objective weights. Then, the membership function of the index layer is established based on the set pair analysis, and the membership of the target layer is deduced by the weighted average operator. Finally, the proximity degrees between status levels and target membership degrees are calculated, and the wind turbine state is determined based on the proximity principle. Case studies demonstrate that the accuracy rate of the proposed method is up to 97%, which is 6% and 8% higher than the maximum membership principle and the reliability criterion, respectively.

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The equipment in railway station is complicated and diverse, and the health status assessment and prediction of equipment is crucial to the safe and stable operation of stations. Graph Neural Networks (GNNs) effectively combine graph data with deep learning technology, which has stronger data and knowledge representation capability and can efficiently handle some non-Euclidean spatial data problems with irregular station equipment associated network structure. Based on this, this paper takes the automatic gate machine and X-ray security checker as an example and proposes a health status assessment and prediction scheme for railway passenger station equipment based on Graph Long Short-Term Memory (G-LSTM) neural network. This paper first analyzes the main factors affecting the health status of passenger station equipment, as well as the correlation between the equipment. Then, the initial graph network structure of the passenger station equipment is constructed, and the G-LSTM model is used to evaluate and predict the health status of the passenger station equipment. Finally, this paper takes the automatic gate machine and X-ray security checker of a high-speed railway station in Beijing as an example to verify the proposed method. The experimental results show that all evaluation metrics perform well, indicating that the G-LSTM model has high accuracy in assessing and predicting the health status of automatic gate machine and X-ray security checker. This paper realizes the health status assessment and prediction of railway passenger station equipment, which can provide some reference for the Prognostics and Health Management (PHM) of equipment in railway stations.

Background: Medical ethics is an applied branch of ethics that deals with ethical challenges in medical and health environments, and its related topics have a long history, but its education as an academic subject in universities has received a lot of attention in the last 40 years. In recent years, medical universities have made extensive efforts to expand medical ethics education, which has been accompanied by significant progress, but given that the modern medical ethics education system is a growing and nascent structure, more research is needed. Therefore, this study aims to assess the status of the medical ethics education system at Tehran University of Medical Sciences to identify its gaps so that the current situation can be improved with proper planning. Methods: The type of this research is descriptive quantitative-qualitative and it was collected from December 2018 to September 2019 at Tehran University of Medical Sciences. First, the educational curriculum of 165 educational levels at Tehran University of Medical Sciences was reviewed and described in terms of the existence of a medical or professional ethics course in the educational curriculum. Then, a semistructured interview was conducted with the 13 professors in charge of teaching this course in all faculties, and content analysis was performed to describe and identify the obstacles in its effectiveness. Results: The results showed that in 53% of the educational levels, there was no separate medical or professional ethics course in the educational curriculum and the most educational coverage of this course took place in the faculties of medicine, dentistry, nursing and midwifery. The obstacles in the effectiveness of teaching this unit were categorized into five main themes of educational curriculum, hidden curriculum, teaching methods, teachers and education management. Conclusion: The results show that the content of curricula needs to be revised and education should be inclusive. Creating a coherent educational organization and monitoring the hidden curriculum are other issues that should be considered to increase the effectiveness of this education. [ABSTRACT FROM AUTHOR]

The decarbonisation and the transformation of existing District Heating (DH) systems requires high investments. For the DH utilities the implementation must take place during ongoing operation, while ensuring security of supply. In this context, it is becoming increasingly important to know the remaining service life of DH pipes when developing transformation strategies and planning the future DH systems. The pipelines of the heating networks in Germany are designed for a minimum service life of 30 years and can be operated even longer, depending on the pipe system and operational conditions. The remaining service life of buried preinsulated bonded pipes is carried out using material testing according to EN 253 (2018) combined with calculating material’s fatigue according to EN 13941-1 (2018) and AGFW FW 401 (2007). In this research work the remaining service life of four sections of preinsulated pipes in the DH network in Duisburg is determined comparing the results of material testing with the calculations of material’s fatigue. Besides the material tests according to EN 253 (2018), thermogravimetric analysis (TGA) and the apparent activation energy determined from TGA is used to estimate the remaining service life of the rigid polyurethane foam (PUR). For the pipes investigated, the service life is determined by the decrease in the properties of the PUR. The results imply that sections can be operated for another 6 up to 14 years and reach a service life of remarkable 50 years under local conditions. Status assessment and the remaining service life of DH pipes are a key element of DH utilities transformation strategies. In order to gain reliable results there is a need to improve knowledge on determining the remaining service life. The TGA is a promising approach for improving the PUR’s service life analysis especially since the previously valid methods of the old EN 253 have been withdrawn. The described approach using current standards and improved new methods in the field will accelerate the use of research results to DH utilities.

Copyright of Avian Conservation & Ecology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effective wildlife management requires accurate and timely information on conservation status and trends, and knowledge of the factors driving population change. Reliable monitoring of wildlife population health, including disease, body condition, and population trends and demographics, is central to achieving this, but conventional scientific monitoring alone is often not sufficient. Combining different approaches and knowledge types can provide a more holistic understanding than conventional science alone and can bridge gaps in scientific monitoring in remote and sparsely populated areas. Inclusion of traditional ecological knowledge (TEK) is core to the wildlife co-management mandate of the Canadian territories and is usually included through consultation and engagement processes. We propose a status assessment framework that provides a systematic and transparent approach to including TEK, as well as local ecological knowledge (LEK), in the design, implementation, and interpretation of wildlife conservation status assessments. Drawing on a community-based monitoring program for muskoxen and caribou in northern Canada, we describe how scientific knowledge and TEK/LEK, documented through conventional monitoring, hunter-based sampling, or qualitative methods, can be brought together to inform indicators of wildlife health within our proposed assessment framework. Atuttiaqtut angutikhat aulatauni piyalgit nalaumayumik piyarakittumiklu tuhagakhat nunguttailininut qanuritni pitquhitlu, ilihimanilu pityutit pipkaqni amigaitnit alanguqni. Naahuriyaulat munarini angutikhat amigaitni aaniaqtailini, ilautitlugit aaniarutit, timai qanuritnit, amigaitnitlu pitquhit hiamaumanilu, atugauniqhauyut pitaqninut una, kihimik atuqtauvaktut naunaiyaiyit munariyauni kihimik amihuni naamangitmata. Ilaliutyaqni allatqit pityuhit ilihimanitlu qanuritni piqarutaulat tamatkiumaniqhanik kangiqhimani atuqtauvaktuniunganit naunaiyaiyit munarinit ahiniittut akuttuyunik amigaitni inait. Ilaliutyaqni pitquhit uumatyutit ilihimani (TEK) qitqanittut angutikhat aulaqataunit havariyaqaqtai tapkuat Kanatamiuni nunatagauyut ilaliutivakniqhatlu atuqhugit uqaqatigikni piqataunilu pityuhiit. Uuktutigiyavut qanuritnia naunaiyaqni havagut piqaqtitiyuq havagutikhainik hatqiumanilu pityuhit ilautitlugit Pitquhit Uumatyutit Ilihimanit (TEK), tapkualuttauq nunalikni uumatyutit ilihimanit (LEK), hanatyuhikhaini, atuqpaliani, tukiliuqnilu angutikhat nunguttailini qanuritnit naunaiyaqni. Pivigiplugit nunaliuyuningaqtut munaqhityutit havagutit umingmaknut tuktutlu ukiuqtaqtuani Kanata, unnirtuqtavut qanuq naunaiyaiyit ilihimani tapkuatlu TEK/LEK, titiqhimani atuqhugit atuqtauvaktut munaqhityutaunit, angunahuaqtumingaqtut naunaiyagat, uvaluniit nakuuninut pityuhit, atauttimuktaulat tuhaqhitninut naunaipkutat angutikhat tahamani uuktutauyuq naunaiyaqni havagutai.

Documenting and interpreting trends in the abundance and distribution of bird populations is critical to monitoring their status and setting conservation priorities. This process requires standardized monitoring and robust analytical techniques, which can resolve trends at spatial scales of management interest while disentangling the influence that various data collection protocols can have on the interpretation of results. We used a 19-year citizen-science-collected dataset (2001–2019), the Nocturnal Owl Survey, to assess abundance trends in Barred Owl (Strix varia), Northern Saw-whet Owl (Aegolius acadicus), and Great Horned Owl (Bubo virginianus) at both fine and broad-scales. To achieve this, we used a spatially explicit modeling approach that facilitates the borrowing of information across spatial boundaries, allowing for more robust trend estimates at finer spatial scales. Further, we assessed the potential influence of the call-playback protocol on trend estimates. At fine spatial scales, we found that a data collection protocol that includes call playbacks provided more precise results to assess relative changes in abundance (i.e., reduced uncertainty). At broader spatial scales, trend estimates were unaffected by data collection methodology (i.e., silent listening versus call playback). Specifically, at the scale of the region or province, we found that populations of focal owl species in the Maritimes of Canada have remained stable over the past 19 years. However, at finer scales, trends are more variable and may create opportunities to test alternative hypotheses about drivers of population change and the effects of management actions at scales amenable to conservation action. The statistical analyses are anticipated to form a national, publicly accessible framework for status assessments of owls in Canada and will provide resource managers and researchers a base from which to evaluate the influence of land management and conservation practices on owl populations across the nation.

The Striped Hyaena, Hyaena hyaena, extends its distribution in the north into Turkey, where it has always been rare. In a status assessment conducted by Kasparek et al. in 2004, it was shown that the species is at the brink of extinction. We compiled information from different sources including a press survey and camera trapping in different parts of the country. The situation has even deteriorated since then. The population has strongly decreased, in particular, in the Marmara and Mediterranean regions, and presently the species survives in Western Anatolia only in some small relict populations in Manisa, İzmir, Muğla, and Bilecik provinces. The situation is different in southeastern Turkey, including the province of Hatay. The species could survive there and regular reports in the press (e.g., road kills) indicate that the species is still widespread and not extremely rare. Several animals were confirmed to live in a nature reserve established for the protection of Striped Hyaenas in the Hatay province. Altogether, the present-day distribution range of the Striped Hyaena in Turkey is split into two parts: a highly threatened relict populations in the west and a larger, slightly less threatened population in the southeast. This means that the species has lost about half of its distribution area in Turkey during the last three or four decades. If serious protective precaution measures are not taken, the Striped Hyaena population in western Anatolia will be completely lost in the near future. [ABSTRACT FROM AUTHOR]

Ecological indicator approaches typically compare the prevailing state of an ecosystem component to a reference state reflecting good environmental conditions, i.e. the desirable state. However, defining the reference state is challenging due to a wide range of uncertainties related to natural variability and measurement error in data, as well as ecological understanding. This study propose a novel probabilistic approach combining historical monitoring data and ecological understanding to estimate the uncertainty associated with the boundary value of an ecological indicator between good and poor environmental states. Bayesian inference is used to estimate the epistemic uncertainty about the true state of an indicator variable during an historical reference period. This approach replaces the traditional boundary value with probability distribution, indicating the uncertainty about the boundary between environmental states providing a transparent safety margin associated with the risk of misclassification of the indicator’s state. The approach is demonstrated by applying it to a time-series of an ecological status indicator, ‘Abundance of coastal key fish species’, included in HELCOM’s Baltic Sea regional status assessment. We suggest that acknowledgement of the uncertainty behind the final classification leads to more transparent and better-informed decision-making processes.

The global SDG indicator framework establishes a set of measurement tools to assess country performances in a comparable way, and helps governments to identify appropriate policy interventions to achieve the SDG targets. Five years into the implementation of the 2030 Agenda, however, still different methods are being used by leading international organizations for assessing whether the SDG targets will be achieved or not. This may lead to different results, sometimes contradictory, generating confusion among users and policy-makers, who therefore cannot base their policy decisions on solid and coherent assessments. This article describes some of the solutions proposed by leading international organizations to address two distinct measurement objectives: (i) monitor the “current” status of achievement of a SDG target, i.e. the situation as pictured by the latest available data, and (ii) assess whether the SDG targets can be achieved by 2030. These distinct objectives are then translated in various methodological approaches, that often include also a way for identifying the targets when not explicitly set, and the procedure to obtain regional and global aggregates (as well as, aggregates by target and goal). This article provides a critical overview of the different approaches and proposes a unified coherent statistical approach for progress and status assessments, highlighting its advantages over the alternative approaches, and demonstrate its application to a specific FAO indicator. The article focuses mainly on the assessment of (i) and (ii), while is not intended to investigate the issues related the aggregation of results at target/goal level, a topic that is beyond the scope of this work. [ABSTRACT FROM AUTHOR]

With the introduction of the Water Framework Directive, the relative importance of smaller waterways increased. This statement is particularly true for Hungary, where water-quality monitoring of most smaller rivers only began 12 years ago. Due to their large number, and the lack of historical data concerning their state, systematic monitoring is a challenge. In the current study, 101 creeks are characterized on the one hand by 13 physico-chemical quality parameters (pH, electric conductivity, chloride ion concentration, dissolved oxygen, oxygen saturation, biochemical oxygen demand, chemical oxygen demand, total organic carbon, ammonium nitrogen, total inorganic nitrogen, total nitrogen, orthophosphate and total phosphorus), on the other hand by their watershed's relief, land use, and point sources' pollution indicators. Euclidean distance between water bodies (henceforth WBs) is calculated according to normalized physico-chemical monitoring values. They are grouped into clusters using the hierarchical clustering method. Watershed characteristics are used to explain the clustering via linear discriminant analysis. The investigation revealed that the main driver of cluster group creation is related to human impact: diffuse agricultural and point-source pollution. The first of the three clusters involved water bodies with low or no human impact; the second cluster contained those with medium-level anthropogenic disturbance, while waters with high pollution values formed the third cluster. Mean distance between heavily polluted waters was 1.5 times higher than that between those showing no or low disturbance, meaning that pristine waters are more similar to one another than polluted ones. The current number of samples per river is twice as high in cluster 1 as in cluster 3, revealing that there is room for optimization of the monitoring system. This contribution uses Hungary as a case study. [ABSTRACT FROM AUTHOR]

One of key elements of an engineering component quality management system of process of provision of sports and improving services in gyms is the subsystem of diagnostics of physical client state. The aim of the study was to increase in efficiency of functioning of a quality management system of process of provision of sports and improving services in gyms due to development of techniques of incorrect estimation of the initial client state.The task of formation of a complex of indices of the initial status by criterion of necessary informtiveness is formulated. The principle of the functional interchangeability as a methodological basis for its decision is reasonable. The hierarchical structure of an integral assessment of the initial client state of gym is offered. On its basis the hierarchical model of rating of informtiveness of a complex of the single (measured) indices, the providing objectivity of an integral assessment of the initial status is developed. The correctness of model is provided at the expense of a formulation of a complex of assumptions and original technology of application of different methods of expert estimation.The result of simulation of informtiveness of an integral assessment of the initial client state for a specific type of service and the purposes of physical enhancement includes: 1) library of sets of single indices, for each of which the technique and a monitoring aid, and also an informtiveness assessment in points, 2) the rules of support of the functional interchangeability of alternative complexes of indices by criterion of sufficiency of informtiveness of a complex based on additive models and the accepted restrictions is defined.The concept of a technique of formation of a complex of the measured and (or) evaluated indices of the initial client state for the specific type of sports and improving service adapted under material opportunities of gym on the one hand, and responding to criterion of necessary informtiveness and the principle of the functional interchangeability, on the other hand is offered.

Heavy metal contamination in soils has worsened with rapid economic development. The combined method with principal component analysis/absolute principal component scores) and random forest models successfully reveals the total sources contribution structure and the specific influence process of industrial activities on heavy metals concentration in soils of the three urban agglomerations. Through statistical analysis, Cd in the Jing-Jin-Ji Metropolitan Region, Pb, Cu, Hg, Cd and As in the Yangtze River Delta, and Hg, As, Cd, Cu and Pb in the Pearl River Delta had relatively high mean concentrations and coefficient of variation (CV), which indicates that the contamination may be caused by human activities. Through PCA/APCS analysis, industrial activities contributed more than 60% of the Cd, Hg and Pb concentrations in soils in JJJ, YRD and PRD. A random forest simulation revealed that heavy metal pollution in soils is the combined result of natural processes and human activities in the three urban agglomerations. The heavy metals concentration in JJJ is mainly caused by industrial activities through land-based emission. The industrial activities presented more significant impact on heavy metals concentration in the soil of YRD compared with other two urban agglomerations. The elevation variation controlled the pattern of heavy metal concentration through influencing the spatial clustering feature of industrial activities. The distance from the sample location to the nearest industrial enterprise is the most important factor in determining the heavy metal concentration. The number of enterprises within a 5 km radius of the sample locations makes a greater contribution to the amount of Hg pollution than other heavy metals. The results of this study could provide support for better management of soil pollution prevention practices such as specific industrial governance and layout optimization. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 17 indicators were used to evaluate natural and social aspects of water cycle status. • A distributed hierarchical evaluation system was constructed for water cycle status. • A water resources carrying capacity model was applied based on water cycle status. • Water cycle status of the Beijing–Tianjin–Hebei region was scored as normal. • Approaches to sustainable development of regional water resources are discussed. Abstract Water cycles protect and maintain ecological environments, while enabling sustainable development of human societies. Various approaches have been proposed for maintaining optimal water cycles. This study developed an evaluation system based on both natural and social attributes of the water cycle. The system incorporated the four dimensions of water ecology, water quality, water quantity, and water use in a distributed hierarchical evaluation model. A water resources carrying capacity model was also constructed. Both models were evaluated using the Beijing–Tianjin–Hebei region as a case study. Statistical data from 13 prefecture-level cities were used to assess the water cycle status of this region. Overall, the water cycle status of the Beijing–Tianjin–Hebei region received a score of 3.33, indicating normal status (although this value varied throughout the region) and the standard deviation of the values was up to 0.48. Results suggested that although water use efficiency is close to ideal, insufficient water resources prevent the region from achieving an optimal regional water cycle. Feasible approaches to more sustainable water resource development within the Beijing–Tianjin–Hebei region include developing interregional cooperation, promoting population–industry relocation, and supplementing shortages with interbasin water transfers. [ABSTRACT FROM AUTHOR]

Determination of the public health concern about magnesium (Mg) in health and disease has been confounded by the lack of a practical measure of status. This has resulted in a lack of consistency in associating Mg deficiency with specific pathological conditions. Some attempts at associating Mg with a chronic disease have used the Dietary Reference Intakes (DRIs) as a status assessment measure. Use of current DRIs for Mg is problematic because recent evidence suggests that they should be updated and based on body weight. An evidence-based suggested Estimated Average Requirement (EAR) and Recommended Dietary Allowance (RDA) for a 70-kg individual is 175 and 250 mg/day, respectively. However, numerous dietary and physiological factors can affect the need for Mg and thus affect the use of the current or suggested new DRIs to assess Mg status. Calcium intakes above normal requirements can decrease Mg balance and exacerbate signs of Mg deficiency. Mg deficiency apparently occurs often in obesity because of increased need to counteract the inflammatory stress induced by adipose tissue dysfunction. Deficiency in anti-oxidant nutrients such as vitamin E and selenium can exacerbate a response to low dietary Mg indicated by increased oxidative stress which can lead to chronic disease. Dietary modifiers of Mg absorption and excretion affect balance and thus the need for Mg. Factors decreasing Mg balance include low dietary protein and non-fermentable fiber, while factors that can increase balance include fructose and fermentable fiber and fructose-containing oligosaccharides. Use of the DRIs to assess the Mg status of a population or group needs to consider their physiological characteristics and dietary habits and be aware that the DRIs may need updating. The DRIs only can be considered a component of a toolbox that presently includes serum Mg concentration and the daily urinary Mg excretion to assess the Mg status of an individual. [ABSTRACT FROM AUTHOR]

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines. [ABSTRACT FROM AUTHOR]

Since 2015/16, dieselgate has initiated transformations of the automotive industry towards the integration of sustainability into the product development process. The present study combines a qualitative approach, based on semi-structured expert interviews, with a quantitative online survey to identify the current status of this implementation. The objective of the paper is to provide an overview on how German automotive companies currently deal with challenges and chances of a sustainable product development. The study obtained the following core conclusions: (1) A common understanding of the term sustainability is missing and a company-wide definition would be beneficial. (2) There is no consensus on the optimal organizational embedment for sustainability into a company. A centralized responsibility plus distributed awareness was identified as the most promising solution. (3) As a matter of supply and demand, automotive companies will not over-satisfy the customers’ demand. As dieselgate has boosted public interest, possible competitive advantages were identified by transforming the product property “sustainability” to a unique selling point. (4) There is an unmanageable amount of methods for the implementation of sustainability in the product development process. For that reason, academically developed methods are only rarely used in industry. (5) Companies are retrospectively evaluating the results of the development rather than using assessment outcomes systematically to improve the product prospectively. (6) Major potential drivers for sustainable product development are laws & politics, customers and competition. Sustainability could become more attractive for industry and customers by creating win-win-solutions. Conceivable propositions are tax advantages or Pigovian taxes, the internalization of sustainability, the shift from tank-to-wheel- towards holistic well-to-wheel-analyses or product service-systems. [ABSTRACT FROM AUTHOR]