An Integrated Data Acquisition Approach for the Structural Health Monitoring and Real-Time Earthquake Response Assessment of a Retrofitted Adobe Church in Peru.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

Assessing the relationship between levator palpebrae superioris and thyroid-associated ophthalmopathy using the Dixon-T2WI sequence.

Background: The current clinical practice lacks sufficient objective indicators for evaluating thyroid-associated ophthalmopathy (TAO). This study aims to quantitatively assess TAO by evaluating levator palpebrae superioris (LPS) using Dixon-T2WI. Methods: The retrospective study included 231 eyes (119 patients) in the TAO group and 78 eyes (39 volunteers) in the normal group. Dixon-T2WI provided data on maximum thickness of LPS (LPS_T) and signal intensity ratio (LPS_SIR) between the muscle and ipsilateral brain white matter. TAO diagnosis and assessment of its activity and severity were quantitatively determined using LPS_T and LPS_SIR. Results: In the TAO group, LPS_T and LPS_SIR were higher than those in the normal group (p < 2.2e-16). The upper lid retraction (ULR) ≥ 2 mm group exhibited higher LPS_T and LPS_SIR compared to the ULR < 2 mm and normal groups. Optimal diagnostic performance was achieved with an AUC of 0.91 for LPS_T (cutoff: 1.505 mm) and 0.81 for LPS_SIR (cutoff: 1.170). LPS_T (p = 2.8e-07) and LPS_SIR (p = 3.9e-12) in the active phase were higher than in the inactive phase. LPS_T and LPS_SIR showed differences among the mild, moderate-to-severe, and sight-threatening groups (p < 0.05). ROC showed an AUC of 0.70 for LPS_T (cutoff: 2.095 mm) in judging the active phase, and 0.78 for LPS_SIR (cutoff: 1.129). For judging the moderate-to-severe and above, AUC was 0.76 for LPS_T (cutoff: 2.095 mm) and 0.78 for LPS_SIR (cutoff: 1.197). Conclusion: The maximum thickness and SIR of LPS provide imaging indicators for assisting in the diagnosis and quantitative evaluation of TAO.

Drivers and human impacts on topsoil bacterial and fungal community biogeography across Australia.

Soil microbial diversity mediates a wide range of key processes and ecosystem services influencing planetary health. Our knowledge of microbial biogeography patterns, spatial drivers and human impacts at the continental scale remains limited. Here, we reveal the drivers of bacterial and fungal community distribution in Australian topsoils using 1384 soil samples from diverse bioregions. Our findings highlight that climate factors, particularly precipitation and temperature, along with soil properties, are the primary drivers of topsoil microbial biogeography. Using random forest machine-learning models, we generated high-resolution maps of soil bacteria and fungi across continental Australia. The maps revealed microbial hotspots, for example, the eastern coast, southeastern coast, and west coast were dominated by Proteobacteria and Acidobacteria. Fungal distribution is strongly influenced by precipitation, with Ascomycota dominating the central region. This study also demonstrated the impact of human modification on the underground microbial community at the continental scale, which significantly increased the relative abundance of Proteobacteria and Ascomycota, but decreased Chloroflexi and Basidiomycota. The variations in microbial phyla could be attributed to distinct responses to altered environmental factors after human modifications. This study provides insights into the biogeography of soil microbiota, valuable for regional soil biodiversity assessments and monitoring microbial responses to global changes.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces.

Concrete surface cracks serve as early indicators of potential structural threats. Visual inspection, a commonly used and versatile concrete condition assessment technique, is employed to assess concrete degradation by observing signs of damage on the surface level. However, the method tends to be qualitative and needs to be more comprehensive in providing accurate information regarding the extent of damage and its evolution, notwithstanding its time-consuming and environment-sensitive nature. As such, the integration of image analysis techniques with artificial intelligence (AI) has been increasingly proven efficient as a tool to capture damage signs on concrete surfaces. However, to improve the performance of automated crack detection, it is imperative to intensively train a machine learning model, and questions remain regarding the required image quality and image collection methodology needed to ensure the model's accuracy and reliability in damage quantitative analysis. This study aims to establish a procedure for image acquisition and processing through the application of an image-based measurement approach to explore the capabilities of concrete surface damage diagnosis. Digitizing crack intensity measurements were found to be feasible; however, larger datasets are required. Due to the anisotropic behavior of the damage, the model's ability to capture crack directionality was developed, presenting no statistically significant differences between the observed and predicted values used in this study with correlation coefficients of 0.79 and 0.82.

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review.

The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.

Development of an instrument to assess the mental health of university students: validation of the Outcome Questionnaire-45 in a Hungarian sample.

The Outcome Questionnaire is a self-report questionnaire developed mainly for treatment impact assessment and monitoring of status change because it can measure the cross-sectional condition very accurately by being sensitive to small changes. The present study aimed to psychometrically evaluate and validate the instrument on a sample of Hungarian university students. 7,695 higher education students (28.6% male, 68.8% female, 1% other, M = 23.7, SD = 6.78) participated in the study and completed a questionnaire package (OQ-45, Beck Depression Inventory, WHO Well-being Questionnaire-5, Connor-Davidson Resilience Scale, MOS-Social Support Survey, Maslach Burnout Inventory-SS) online, developed to measure general and more specific mental health conditions. The Hungarian version of the questionnaire has a high internal consistency (Cronbach's alpha = 0.951). Based on the confirmatory factor analysis, the original three-factor version of the instrument (due to inadequate fit indicators) did not gain support in our sample. Five subscales were identified and subjected to content analysis in the exploratory factor analysis. Our final questionnaire consists of 39 items. The full scale and the subscales show a high correlation with other questionnaires measuring similar constructs. The psychometric indicators of the questionnaire are adequate and, therefore, considered reliable. The separation of the five factors was confirmed by construct and convergent validation. The questionnaire's psychometric properties may be worth testing in the future on a clinical sample and a sample of adults from a wider age range. The use of the measurement tool has important implications in research areas beyond therapeutic impact assessment, as it may offer a bridging solution to the methodological problems encountered in the construction of complex questionnaire packages consisting of several instruments. International findings suggest that some items in the questionnaire are particularly sensitive to cultural context, so it is crucial to use a measure adapted to the region of the study sample. Other strengths of the questionnaire include its ability to address subclinical and clinical symptoms in one dimension and provide a comprehensive cross-sectional picture of the bio-psycho-social status of individuals, which allows systematic monitoring of a large and heterogeneous population (higher education students).

Correction Factors for Sclerometric Test Results in the Technical Assessment of Timber Structural Elements under Diverse Conditions.

Research on existing wooden structures relies on non-destructive and semi-destructive techniques. One of the methods enabling the estimation of the physico-mechanical characteristics of wood in building structures based on established correlational relationships is the sclerometric method. The challenge in utilizing these known correlational relationships is the lack of data regarding the impact of frequently occurring factors in objects on sclerometric test results. This paper presents the influence of selected factors on the results of sclerometric tests, such as temperature, the direction of testing in relation to annual growth rings, and the physical orientation of the measuring device. The research was conducted on pine, spruce, and fir elements, each subjected exclusively to the influence of one of these factors. The study indicates that these factors should not be overlooked in assessing technical conditions using sclerometric testing methods. The impact of temperature on sclerometric test results is relatively small; a change in temperature of 10 °C results in an average test outcome change of approximately 3%. Conversely, changing the orientation of the measuring device from horizontal to vertical can alter the test result by up to 10%. The direction of testing relative to the annual increments of wood also has a significant impact on the test results, but incorporating this factor into practice seems to be quite difficult, and in the case of elements with substantial cross-sections, it is also not required. The obtained results enable the application of established correlational relationships in the structural analysis of wooden elements for which access is challenging, especially under temperature conditions different from the reference, 20 °C.

Research on 18th-Century Building Structures in Terms of Static Scheme Changes.

The evaluation of the technical condition of historic buildings that have operated for several hundred years is a complicated issue. Even buildings that are in very poor condition must be checked and assessed in terms of their further repair, strengthening, or compliance with conditions that allow the facility to be safely operated. Most 18th-century buildings have not survived to this day retaining their original arrangements and structural elements. Renovations and repair work in the past were often carried out using materials of uncertain quality, with repair work of different qualities and without detailed analysis or methodology, based only on the experience of the former builders. In historic structures, the character of the work of individual structural elements has often changed due to significant material degradation, the poor quality of repair work, or the loss of adequate support. When load transfers change, internal forces are redistributed, and, as a result, the static scheme changes. This article presents an overview of identified defects affecting the change in static schemes in historical building structures built in the 18th century, using the example of a historic building with a large number of aforementioned defects. The process of assessing the technical condition of the facility is presented, in which non-destructive testing (NDT) methods were used. Detailed computational analyses were carried out for the wooden roof truss structure, which had partially lost its support.

Exploitation Perspective Index as a Support of the Management of the Transformer Fleet.

This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters.

Accurate Assessment of Moisture Content and Degree of Polymerization in Power Transformers via Dielectric Response Sensing.

Power transformers are essential apparatuses used to transfer electrical energy from one voltage-level circuit to another. For reliable systems, preventive maintenance of the transformers is required to ensure good services of all mechanical, electrical, and insulation parts. Oil-immersed paper is most often used for transformer insulation. To ensure such good insulation performance and for assessing insulation conditions, advanced transformer sensing, monitoring, and effective assessment techniques are required. This paper introduces an effective technique for assessing the insulation conditions in power transformers, which are crucial for ensuring reliable energy transfer. The method utilizes advanced transformer sensing and monitoring, focusing on oil-immersed paper insulation commonly used in transformers. The technique employs dielectric response sensing, obtained from frequency-domain spectroscopy tests, to estimate degrees of polymerization (DP) and percentages of moisture content (PMCs) in the oil-immersed paper insulation. These parameters are well-known indicators of insulation performance. The approach is based on the weighted k-nearest neighbor regression, using a database of dielectric loss factors at low frequency and oil conductivities. To overcome limited data availability, linear interpolation and extrapolation techniques are applied to enlarge the database. Experimental verification and comparison with a previously developed method demonstrate the proposed technique's superiority in accuracy and complexity. The maximum deviations of DP and PMC in the validation cases are 6.2% and 18.7%, respectively. In addition, to evaluate the validity of our proposed method in the case of a real power transformer, a comparative analysis of the DP and PMC values determined by the proposed method with those obtained through a previously developed and complicated approach was performed. The predicted results indicate that the DP and PMC values of the oil-immersed insulation fall within the ranges of 800 to 1000 and 1.5 to 2.0, respectively, which agree with the results determined by the complicated approach and closely align with real conditions. By offering a reliable and advanced means of assessing insulation conditions, this technique contributes to the preventive maintenance and overall efficiency of power transformers.

Assessment of the Technical Condition of Timber Structural Elements Using Sclerometric Tests.

The technical assessment of wooden elements is the primary step in their repair and reinforcement design. Normative requirements currently mandate additional tests, including semi-destructive ones, beyond traditional visual assessment. Despite the growing feasibility of semi-destructive tests for qualitative assessments, there remains a paucity of data enabling quantitative assessments. This study investigated the hardness of structural timber, specifically pine, spruce, and fir, from Central Europe using sclerometric methods. The outcomes of these tests were compared with those of conventional destructive tests and correlational relationships were established. A strong correlation was found between the sclerometric tests and density (r = 0.62 ÷ 0.82), while a range of strong to moderate correlations was found (r = 0.40 ÷ 0.70) for mechanical characteristics (bending and compressive strength). The correlation strength varied among different wood species, with the strongest for pine and the weakest for spruce. All established relationships were compiled into 40 functions to facilitate their future utilization in quantitative assessments during the technical evaluation of wooden objects. The study also examined the influence of wood defects on the derived correlations by considering the knot index. Sclerometric methods accurately reflect the physico-mechanical properties of elements with a small or medium defect content. However, for wood with a high proportion of defects (knots), the correlations are very weak (r = 0.23 ÷ 0.52, including statistically insignificant results). This research offers new insights into the potential of semi-destructive methods in the structural evaluation of wooden elements, highlighting the need to account for wood species and defect content.

Condition Assessment of Natural Ester-Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties.

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.

Predicting the performance of existing pre-cast concrete pipes using destructive and non-destructive testing techniques.

One of the most significant and critical urban assets for a sustainable community is the sewer pipeline network and water distribution system. Water sewer networks and distribution systems have a definite service life span to provide continuous facilities to end users. Therefore, it is pertinent to continuously evaluate the condition of water and sewer concrete pipelines to ensure the reliable, sustainable, and cost-efficient transport of water and sewerage for the safety of society. The condition assessment is commonly carried out by visual observations followed by some non-destructive testing methods. However, it is the need of the hour to shift assessment methods to advance assessment techniques to save time and money for our community. Currently, in this project, the condition assessment of pre-cast concrete pipes was carried out by destructive and non-destructive methods. Different test trials i.e., ultra-sonic pulse velocity, Schmidt hammer also known as rebound hammer test, visual inspection, three edge bearing test, and core cutting test on the old buried and new concrete pipes were performed. It was observed that concrete used for the construction of existing precast concrete pipes still has better quality indices after 20 years as compared to that of concrete of new pipes. However, steel has deteriorated with time and clear corrosion of steel was identified in existing pre-cast concrete pipes. At the same time, it was observed that there should be an automated mechanism to continuously asses the condition of pre-cast existing pipes which will address the sustainable development goals (SDG 6, 9, 11). Consequently, it can be said that condition assessment of pre-cast concrete pipes will lead to sustainable societies and infrastructure.

Fusing Expert Knowledge with Monitoring Data for Condition Assessment of Railway Welds.

Monitoring information can facilitate the condition assessment of railway infrastructure, via delivery of data that is informative on condition. A primary instance of such data is found in Axle Box Accelerations (ABAs), which track the dynamic vehicle/track interaction. Such sensors have been installed on specialized monitoring trains, as well as on in-service On-Board Monitoring (OBM) vehicles across Europe, enabling a continuous assessment of railway track condition. However, ABA measurements come with uncertainties that stem from noise corrupt data and the non-linear rail-wheel contact dynamics, as well as variations in environmental and operational conditions. These uncertainties pose a challenge for the condition assessment of rail welds through existing assessment tools. In this work, we use expert feedback as a complementary information source, which allows the narrowing down of these uncertainties, and, ultimately, refines assessment. Over the past year, with the support of the Swiss Federal Railways (SBB), we have assembled a database of expert evaluations on the condition of rail weld samples that have been diagnosed as critical via ABA monitoring. In this work, we fuse features derived from the ABA data with expert feedback, in order to refine defection of faulty (defect) welds. Three models are employed to this end; Binary Classification and Random Forest (RF) models, as well as a Bayesian Logistic Regression (BLR) scheme. The RF and BLR models proved superior to the Binary Classification model, while the BLR model further delivered a probability of prediction, quantifying the confidence we might attribute to the assigned labels. We explain that the classification task necessarily suffers high uncertainty, which is a result of faulty ground truth labels, and explain the value of continuously tracking the weld condition.

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring.

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure's impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle's primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture.

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today's Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.

Analogical Assessment of Train-Induced Vibration and Radiated Noise in a Proposed Theater.

This study presents the analogical assessment of the train-induced vibration and radiated noise in a proposed theater. The theater is to be constructed in a region with crowded metro lines, and the assessment is implemented in an analogical building with comparable structural type and metro condition. Prior to the assessment, the comparability of the analogical building with the theater is validated using the train-induced ground vibration. With the same horizontal distance from the metro line, the train-induced vibration level in the analogical building is 9 dB higher than that in the construction site of the theater. Such results indicate that the lack of soil layers may lead to a dramatic increase in train-induced vibration in the building. In the staircase of the analogical building, the train-induced radiated noise reached 55 dB (A), which is 10 dB (A) higher than the daytime allowable level. As the most important indicator, the noise rating number in the cinema of the analogical building is NR-43, which put forward an enormous challenge on the construction of the theater with a denoise demand of 23 dB. The analogical method applied in this study provides an effective and practical way for the assessment of train-induced vibration and radiated noise in proposed vibration-sensitive buildings. The assessment results that provide necessary reference and support for the anti-vibration design will help guarantee the stage effect of the theater.

Diagnostics of Large-Panel Buildings-An Attempt to Reduce the Number of Destructive Tests.

Structural condition diagnostics provides the basis for decision making regarding the possibility of continued safe operation, necessary reinforcement, repair work, and in extreme cases, dismantling of the structure. The most reliable results concerning the condition and strength of materials are provided by destructive testing. However, these tests are very time-consuming, costly, and difficult to perform on in-service facilities. In addition, they involve the need to obtain the consent of the occupants of the premises and subsequent renovations. This article focuses on presenting an opportunity to reduce the number of destructive tests necessary to reliably assess the condition of large-panel structures, which constitute a significant housing stock in Europe. Based on tests carried out on a real building, the risk factors associated with obtaining reliable results by non-destructive methods were determined. Areas where destructive testing is necessary were identified. In addition, reference was made to standard recommendations and guidelines from a reputable research institution. Practical guidelines were formulated regarding the diagnostics of large-panel structures, resulting in a reduction in the number of destructive tests required.

The role of employee psychological stress assessment in reducing human resource turnover in enterprises.

In this paper, the model is constructed by measuring the psychological stress condition of employees; the psychological stress condition measurement model analyzes and tests the reasons for reducing human resource turnover in enterprises. In this paper, through the research related to the problem of talent loss in enterprises, we found that enterprises of other ownership often use the talent loss early warning model, to measure the possibility of talent loss in enterprises and issue an early warning, which enables enterprises to solve the talent loss crisis in time and minimize the negative impact of talent loss on enterprises. In this paper, we analyze the causes of human resource attrition risk from a theoretical point of view, construct a system of human resource attrition risk indicators for enterprises, and explain the content of each technical indicator and its measurement method. The PLS structural equation of the HR attrition risk evaluation model is established based on the constructed risk index system. In addition, the analysis of the causes of human resources turnover risk in the company also proposes strategies to avoid and prevent the threat. The PLS structural equation evaluation model of HR turnover risk is applied to various situations of human resource management to analyze the case of the comprehensive evaluation of HR turnover risk to guide the practical application of the model. Therefore, the research results of this paper have significant reference value for enterprises to solve similar human resource attrition problems. At the same time, it will provide a reference for enterprises to improve their human resource diagnostic capability and promote the development of human resource management.

Opportunities, approaches and challenges to the engagement of citizens in filling small water body data gaps.

Monitoring the condition (water quality, biodiversity, hydromorphology) of small water bodies presents a challenge for the relevant authorities in terms of time and resources (labour and financial) due to the extensive length of the stream network or the sheer number of small standing water bodies. Citizen science can help address information gaps, but the effort required should not be underestimated if such projects are to generate reliable and sustained data collection. The overall aim of this paper is to propose a framework for operationalisation of citizen science targeting collection of data from small water bodies. We first consider the data gaps and the elements (water chemistry, ecology, hydromorphology) to be addressed, in order to define where citizen science could best make an impact. We review examples of tools and methods that are appropriate for small water bodies, based on experience from a selection of freshwater citizen science projects, and the support that is needed for effective and sustained small water body projects across Europe.