Health Assessment Framework Research Articles

A data science framework for profit health assessment: development and validation

This study introduces a cutting-edge profit health assessment framework that merges signaling theory and agency theory within a data science context, leveraging both financial and nonfinancial indicators to provide a comprehensive, multidimensional evaluation of earnings quality in publicly traded companies. This model transcends conventional earnings management frameworks by focusing on the holistic earnings condition of firms and addressing challenges related to information asymmetry and managerial motives. The empirical analysis utilizes data from small and medium-sized private enterprises listed on China’s SME Board between 2018 and 2022, employing machine learning algorithms to rigorously validate the model’s effectiveness. The results reveal that 85.7% of the penalized firms by the China Securities Regulatory Commission from 2018 to 2023 exhibited low levels of profit health. This data science driven analysis deepens the understanding of corporate earnings health for regulators and investors. The proposed framework not only expands the theoretical underpinnings of earnings management but also serves as a novel evaluative tool for stakeholders, paving the way for broader application across diverse markets and sectors and rethinking earnings quality assessment methodologies through a data science lens.

Integrating ecosystem stress into the assessment of ecosystem health in karst areas and exploring its driving factors

Accurately evaluating the degree of ecosystem health and comprehending its drivers are of crucial importance for ecosystem management and restoration in fragile ecoregions. Adopting an ecosystem stress perspective, this investigation scientifically diagnosed ecosystem health in Guizhou Province, a typical karst fragile ecological area, by developing a “Vigor-Organization-Resilience-Stress” (VORS) regional ecosystem health assessment framework. The principal driving factors and their spatio-temporal heterogeneity influencing ecosystem health in karst regions were investigated using geographic detectors and geographically and temporally weighted regression. Our findings indicated that: (1) The ecosystem health in Guizhou province presents a spatial distribution pattern of lower in the western region and higher in the eastern region, and the average ecosystem health index (EHI) in 2000, 2010 and 2020 is 0.194, 0.246 and 0.254 respectively, indicating that the overall ecosystem health level in Guizhou province has improved with the passage of time. (2) The spatiotemporal variation of ecosystem stress in Guizhou Province is substantial; the pressure associated with land use is projected to increase, whereas the intensity of stress attributed to rocky desertification and soil erosion is anticipated to diminish over time. (3) The primary determinants influencing the health of ecosystems in karst environments are the social and economic elements. These factors progressively amplify their effect on ecosystem health as time passes. The new assessment framework we have established includes ecosystem integrity and ecological threats faced by the ecosystem, which provides a new perspective for understanding the complex internal characteristics of karst areas and the coupling relationship between human and natural ecosystems. The results of this study can provide scientific reference for land use and ecological management policies in fragile ecological areas.

Soil eDNA biomonitoring reveals changes in multitrophic biodiversity and ecological health of agroecosystems

Soil health is integral to sustainable agroecosystem management. Current monitoring and assessment practices primarily focus on soil physicochemical properties, yet the perspective of multitrophic biodiversity remains underexplored. Here we used environmental DNA (eDNA) technology to monitor multitrophic biodiversity in four typical agroecosystems, and analyzed the species composition and diversity changes in fungi, bacteria and metazoan, and combined with the traditional physicochemical variables to establish a soil health assessment framework centered on biodiversity data. First, eDNA technology detected rich multitrophic biodiversity in four agroecosystems, including 100 phyla, 273 classes, 611 orders, 1026 families, 1668 genera and 1146 species with annotated classification, and the relative sequence abundance of dominant taxa fluctuates tens of times across agroecosystems. Second, significant differences in soil physicochemical variables such as organic matter (OM), total nitrogen (TN) and available phosphorus (AP) were observed among different agroecosystems, nutrients were higher in cropland and rice paddies, while heavy metals were higher in fish ponds and lotus ponds. Third, biodiversity metrics, including α and β diversity, also showed significant changes across agroecosystems, the soil biota was generally more sensitive to nutrients (e.g., OM, TN or AP), while the fungal communities were mainly affected by heavy metals in October (e.g., Cu and Cr). Finally, we screened 48 sensitive organismal indicators and found significant positive consistency between the developed eDNA indices and the traditional soil quality index (SQI, reaching up to R2 = 0.58). In general, this study demonstrated the potential of eDNA technology in soil health assessment and underscored the importance of a multitrophic perspective for efficient monitoring and managing agroecosystems.

Health status and characterisation of Gibraltar’s maerl beds

Maerl beds form heterogeneous, three-dimensional habitats for numerous species, housing high levels of biodiversity and species richness across the globe. Despite the importance of maerl’s ecological and carbon-regulating role; there is still a lack of information on methods to characterise its health. Therefore, this research set out to develop a standardised health assessment framework to assess the overall health of maerl a) structural function and b) habitat function. The framework was tested in an area of Gibraltar that has historically been severely impacted from various pressures including water quality. The maerl health is in a critical status, as over 92 % of it is deemed dead, with high levels of surface erosion and unusual colourations observed, and rhodoliths were generally small in size (mean: 1.35 cm ± 0.19 cm). The habitat health was labelled as intermediate, based on the species richness values, signs of nursery function, high number of Terebellidae (n = 233), distribution and coverage, and type of sediment composition. The maerl-forming species morphologically identified as Lithophyllum cf. racemus and Lithothamnion cf. corallioides were found to be present in the study site, which are first records within Gibraltar’s waters.

Registered general nurses' health assessment practices in a tertiary hospital: A focused ethnography study.

To explore the assumptions and values that influence nursing health assessment practices among registered general nurses in general medical and surgical wards. The study was designed as a focused ethnography. A semi-structured interview guide was used to explore prevailing nursing health assessment practices of 13 registered general nurses in an attempt to explore the assumptions and values influencing health assessment practices in the study setting. Data were analysed inductively using an interpretive qualitative content analysis method. Nursing health assessment practices, and underlying assumptions and values were underpinned by a central theme of a culture of low expectation relating to nursing health assessment. The culture of low expectation was highlighted in five themes: (1) Unsystematic Assessment of Health Status, (2) Purpose of Nursing Health Assessment, (3) The Role of Nursing Educational and Regulatory Institutions, (4) Ward Ethos and (5) The Role of Organizational and Ward Leadership. The adoption of a holistic nursing health assessment framework with a clearly defined purpose of aiding nursing diagnoses can guide patient-centred care delivery and facilitate early recognition of physiological deterioration. Thirteen registered general nurses were interviewed, and the initial findings returned to them for validation. The potential contribution of nursing health assessment to nursing practice and patient outcomes may not be fully realized if nursing health assessment is not situated within a holistic health assessment model with a clearly defined purpose for nursing practice.

Health technology assessment for sexual reproductive health and rights benefits package design in sub-Saharan Africa: A scoping review of evidence-informed deliberative processes

Background Health technology assessment uses a multidisciplinary approach to support health benefits package design towards universal health coverage. The evidence-informed deliberative process framework has been used alongside Health technology assessment to enhance stakeholder participation and deliberations in health benefits package design. Applying the evidence-informed deliberative framework for Health assessment could support the morally diverse sexual reproductive health and rights (SRHR) benefits package design process. However, evidence on participation and deliberations for stakeholders in health technology assessment for SRHR benefits package design has not been curated in sub-Saharan Africa. This study synthesises literature to fill this gap. Methods This scoping review applies the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews, and deductive analysis following the evidence-informed deliberative processes framework. The search strategy uses the Guttmacher–Lancet Commission-proposed comprehensive definition of SRHR and the World Health Organisation’s universal health coverage compendium of SRHR interventions to generate search terms. Six databases and biographical hand searches were used to identify studies in Sub-Saharan Africa from 1994. Results A total of 14 studies met the inclusion criteria. Evidence for yearly public budgets and explicit SRHR health technology assessment processes was not found. In 12 of the studies reviewed, new advisory committees were set up specifically for health technology assessment for SRHR priority-setting and benefits package design. In all decision-making processes reviewed, the committee member roles, participation and deliberations processes, and stakeholder veto powers were not clearly defined. Patients, the public, and producers of health technology were often excluded in the health technology assessment for the SRHR benefits package design. Most health technology assessment processes identified at least one decision-making criterion but failed to use this in their selection and appraisal stages for SRHR benefits design. The identification, selection, and scoping stages in health technology assessment for SRHR were non-existent in most studies. In 11 of the 14 processes of the included studies, stakeholders were dissatisfied with the health policy recommendation from the appraisal process in health technology assessment. Perceived benefits for evidence-informed deliberative processes included increased stakeholder engagement and fairness in decision-making. Conclusion To support the integration of diverse social values in health technology assessment for fairer SRHR benefits package design, evidence from this review suggests the need to institutionalise health technology assessment, establish prioritisation decision criteria, involve all relevant stakeholders, and standardise the process and assessment methodological approaches.

Revisiting the global weights of the integrated watershed health assessment framework and Weyib watershed health analysis: Ethiopia's policy prospects

AbstractWatershed health study has been considered as a global issue embodied in the United Nations' 2030 agenda toward sustainable water resources development, paving the way for integrated water resources management (IWRM) application, and visualizing intervention techniques to comprehend existing challenges. The watershed faces tremendous pressure due to erratic rainfall patterns, drastic water demand increase, hydrological extremes, and natural‐anthropogenic activities. This study aims to re‐evaluate the recently developed Integrated Watershed Health Assessment Framework (IWHF) global weights, Weyib watershed health assessment, and Ethiopia's policy implications using the integration of numerous data and multiple tools. The analytic hierarchy process (AHP) was used to estimate aggregate results from experts' survey responses, which depicted 50.89% priority for ecological conditions, followed by economic 23.30%, policy 13.82%, and 12% for socio‐cultural attributes. A slight discrepancy was exhibited when global weights were re‐evaluated, and the variables with substantial effects on IWHF in developing countries like Ethiopia were integrated. The majority of Weyib watershed health index (WWHI) indicators unveiled promising results except for stakeholders' collaboration and social obligation to restoration activities, information and educational campaigns, hydrology, wetland, and natural land‐cover. The water‐quality parameters laboratory findings were depicted as suitable for irrigation purposes. In general, the WWHI was in good health status, and a medium‐level priority was required for intervention activities to address deteriorated variables. Moreover, enhancing IWRM's application will contribute to tackling bottlenecks such as boost community awareness and stakeholder collaboration, legal and administrative gaps, overlapped mandates, and unpermitted water use resulting in undermining downstream demands and environmental low‐flow.

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.

A new framework for assessing ecosystem health with consideration of the sustainable supply of ecosystem services

ContextThe establishment of an ecosystem health assessment framework from a human–environment view is vital to landscape sustainability. Although several studies have improved the assessment framework by integrating ecosystem services (ESs) supply or demand, consideration of the sustainable supply of ESs is lacking.ObjectivesThe objective of this paper is to improve the current methodological framework by integrating ecological integrity and the sustainable supply of ESs to establish an ecosystem health assessment framework.MethodsAn improved assessment framework, including four indicators, vigor, organization, resilience, and ecosystem services supply rate, was established from the perspective of human–environment systems. Then, the performance of the improved assessment framework was demonstrated in a case study in China from 2000 to 2020.ResultsFrom 2000 to 2020, the overall spatial pattern of ecosystem health values in China was high in the southern and southeastern coastal regions, and low health values were mostly located in the western region, parts of Inner Mongolia, and metropolitan areas, with a descending trend from southeast to northwest. The imbalance between the potential and actual supply of ESs greatly contributed to the deterioration of regional ecosystem health. During the study period, the regional ecosystem in China was found to be in a more unhealthy state than in traditional Vigor-Organization-Resilience-Ecosystem services (VORES) evaluation.ConclusionsThe improved assessment framework that incorporates the ecological integrity and sustainable supply of ESs provides a new perspective for understanding the complex inherent characteristics of ecosystems and the regional human-nature connectedness in coupled human–environment systems. Our results could serve as a scientific reference for practical landscape governance in a changing world to achieve landscape sustainability.

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings.

Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

Spectral Reflectance Measurements.

This chapter provides a methodology for evaluating plant health and leaf characteristics using spectral reflectance. It provides a step-by-step guide to using spectrometers for high-resolution point measurements of leaf spectral reflectance and multispectral imaging for capturing spatial data, emphasizing the importance of consistent measurement conditions. The chapter further explores the intricacies of multispectral imaging, including calibration, data collection, and image processing. Finally, this chapter delves into the application of various spectral indices for the quantification of key traits such as pigment content, the status of the xanthophyll cycle, water content, and how to identify spectral regions of interest for further research and development. Serving as a guide for researchers and practitioners in plant science, this chapter provides a straightforward framework for plant health assessment using spectral reflectance.

MANILA BAY WATERSHED SCORECARD: A GIS-BASED QUANTITATIVE WATERSHED HEALTH ASSESSMENT

Abstract. Watershed health refers to the maintenance of the normal status of a watershed. With the use of Geographic Information Systems (GIS), a watershed health assessment framework was developed and applied to the Manila Bay Watershed (MBW) based on the sub-indices used by the US EPA and the Minnesota DNR. These three sub-indices were used and modified: geomorphology, connectivity, and hydrology. Geomorphology sub-index accounted for the effects of soil erosion using the Unit Stream Power-based Erosion/Deposition model (USPED). Connectivity sub-index considered the connection between habitats within three environments: terrestrial, aquatic, and riparian zone. Hydrology sub-index accounted for the effects of impervious cover and urbanization on the movement of water using three factors, namely, natural cover, tree cover, and loss of hydrologic storage. Land cover map was the most used dataset in this scorecard, where the maintained natural land covers generally received high scores and built-up areas received the lowest. The overall watershed health score of MBW is 75.713 from the mean of the three sub-indices. Pampanga River Basin, which is the largest river basin within the MBW, got the highest score of 79.462 since it consists of huge portions of maintained natural land cover. Manila River Basin, known to have dense built-up areas, got the lowest average of 60.773. On the provincial level, the province of Nueva Ecija got the highest score, and the National Capital Region (NCR) got the lowest. The developed framework successfully quantified a relative health score which can be used to rank and prioritize subwatersheds, and to measure in totality the improvement or degradation of subwatershed/s over time.

Optimization of unmanned aerial vehicle inspection strategy for infrastructure based on model-enabled diagnostics and prognostics

The use of unmanned aerial vehicles (UAVs) for structural health inspection has become a promising technique to perform labor-intensive, accessibility-challenged, and sometimes dangerous inspection tasks. This paper presents a novel physics-informed UAV inspection planning framework for infrastructure structural health assessment based on model-based diagnostics and prognostics enabled by physics-based probabilistic analysis. It bridges the gap between UAV mission planning and inspection with model-based probabilistic analysis, by allowing bidirectional information exchange, namely (1) structural damage state diagnostics using UAV inspection data and (2) UAV inspection optimization through model-based failure prognostics. Based on the bidirectional communication, the impacts of the three key UAV inspection parameters (i.e., inspection distance, inspection interval, and critical maintenance threshold) on structural life-cycle cost are analyzed. The optimization of key UAV inspection parameters is achieved by minimizing the cost per unit time (CPUT) through model-based pre-posterior analysis. In this analysis, synthetic observations are generated using predictive models according to the prior distributions of various uncertainty sources (e.g., detection rate, damage state evolution, etc.). The generated synthetic observations are then used to obtain the posterior distributions of uncertain parameters, enabling the integration of prior information and Bayesian model updating into inspection optimization through a cost function. The proposed model offers a robust method to accommodate the inherent uncertainties in failure prognostics, leading to a more effective optimization of the UAV inspection parameters. The practical application of the framework is demonstrated through a miter gate example. The results show that the proposed method is able to efficiently determine the optimal UAV inspection parameters and continuously update the information model.

Exploring the Anemia Ecology: A New Approach to an Old Problem

Our ability to identify anemia and all its permutations demands an approach that integrates the key elements of a complex “ecology,” which intertwines biology and mechanistic aspects of nutrients with both the health status and underlying factors—physical, economic, social, behavioral, demographic, and environmental. The complexity of anemia demands an ecologic approach that appreciates systems biology, translates sensitive and specific assessment methodologies and interventions, and ultimately improves clinical and public health outcomes. This series of technical papers on anemia by the U.S. Agency for International Development (USAID) Advancing Nutrition Anemia Task Force (ATF) is a first step in translating our ecologic approach to anemia with a view toward balancing research with its translation to effective programs, interventions, and policy. This introductory overview describes the components of our ecologic approach—linking the biology of anemia with its assessment and using the learning from that confluence to devise context-specific interventions. This introductory review briefly discusses the topics that underlie the biology and primary etiologies of anemia and presents a framework for public health assessment of anemia, leading to appropriate public health interventions. The other 3 manuscripts in the supplement provide the details of the arguments laid out in the introduction.

Development of a watershed health assessment framework integrating ecological, social, cultural, economic and policy attributes

ABSTRACT Watershed health assessment is vital for effective resource management, necessitating an integrated framework that captures the multidimensional nature of watershed health and guides protection and restoration efforts. This study presents the development of the Integrated Watershed Health Assessment Framework (IWHF), incorporating ecological, social, cultural, economic and policy attributes to comprehensively assess watershed health as a socio-ecological system. By integrating primary and secondary data, including literature reviews and expert opinions, the IWHF captures the multidimensional nature of watershed health and provides a practical tool for sustainable watershed management. The results, evaluated through the Analytic Hierarchy Process (AHP) within the context of Multi-Criteria Decision Making (MCDM), prioritize ecological factors (48.80%), followed by economic considerations (21.90%), socio-cultural factors (16.91%) and policy aspects (12.39%). This highlights the critical importance of preserving ecological integrity, emphasizing sustainable economic practices, inclusive community engagement and well-defined policy frameworks. The IWHF's holistic and flexible approach, comprehensive integration, and enhanced decision support capabilities set it apart from other frameworks, contributing to the understanding, management, and preservation of watershed health while guiding policy direction and optimizing the utilization of watershed resources.

UAV-Based Computer Vision System for Orchard Apple Tree Detection and Health Assessment

Accurate and efficient orchard tree inventories are essential for acquiring up-to-date information, which is necessary for effective treatments and crop insurance purposes. Surveying orchard trees, including tasks such as counting, locating, and assessing health status, plays a vital role in predicting production volumes and facilitating orchard management. However, traditional manual inventories are known to be labor-intensive, expensive, and prone to errors. Motivated by recent advancements in UAV imagery and computer vision methods, we propose a UAV-based computer vision framework for individual tree detection and health assessment. Our proposed approach follows a two-stage process. Firstly, we propose a tree detection model by employing a hard negative mining strategy using RGB UAV images. Subsequently, we address the health classification problem by leveraging multi-band imagery-derived vegetation indices. The proposed framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 97.52% for tree health assessment. Our study demonstrates the robustness of the proposed framework in accurately assessing orchard tree health from UAV images. Moreover, the proposed approach holds potential for application in various other plantation settings, enabling plant detection and health assessment using UAV imagery.

Use of remote sensing-based pressure-state-response framework for the spatial ecosystem health assessment in Langfang, China.

Land use/land cover changes are occurring at an unprecedented rate and scale because of the economic development that has jeopardized the ecosystem's health. Ecosystem health should be studied and monitored at spatiotemporal scale to promote sustainable development and ecological civilization. The goal of this study was to assess the spatial ecosystem health of Langfang at the city and administrative levels using city's regional characteristics. Remote sensing-based pressure-state-response (PSR) framework, analytical hierarchy process (AHP), and principal component analysis (PCA) were utilized for spatial ecosystem health index (SEHI) formulation, indicator weighting, and indicator selection in several epochs (1990, 2003, 2013, and 2021), respectively. SEHI was formulated by combining subindices of pressure, state and response. The spatial ecosystem pressure index (SEIP) identified that the pressure was increasing on the ecosystem. In contrast, the spatial ecosystem state index (SEIS) pointed out an improvement in the state of the ecosystem since 1990. The worst state of the ecosystem was observed for the year 2013. The spatial ecosystem response index (SEIR) indicated that the response of the ecosystem towards the exerted pressures and states remained variable; however, it was reasonably good in 1990. All the administrative units of Langfang were associated with a healthy score for the spatial ecosystem health index (SEHI) for 1990 (pre-industrialization epoch), while the SEHI significantly reduced in 2013 (industrialization epoch) however improved for the later epochs (circular economy and ecological civilization epoch). The SEHI was moderately healthy for Dachang, Dacheng, Guan, Guangyang, and Yongqing while relatively healthy for the remaining administrative units in 2021. SEHI identified that spatial health has been improving since 2003 though not reaching the 1990's level for Langfang. Therefore, efforts should be focused on minimizing pressure and stabilizing the state to improve the spatial ecosystem health of Langfang. The developed SEHI can assist policymakers in analyzing regional health, identifying development strategies, driving environmental restoration, and quantifying needed changes.

Maternal pesticide exposure and risk of preterm birth: A systematic review and meta-analysis

BackgroundMaternal pesticide exposure might be associated with adverse pregnancy outcomes through triggering inflammation and oxidative stress and disrupting endocrine functions. Yet the association between prenatal pesticide exposure and risk of preterm birth remains inconclusive. ObjectivesTo conduct a systematic review and meta-analysis of human observational studies using the Office of Health Assessment and Translation (OHAT) framework to explore the association of per ten-fold increase of pesticide concentrations in maternal biological samples during pregnancy with risk of preterm birth and length of gestational age at birth. Data sourceFive English (PubMed, Embase, Cochrane Library, Web of Science and Scopus) and 3 Chinese databases (China national knowledge infrastructure (CNKI), Wanfang Data, and Chinese Biomedical Literature Database (CBM)) were searched till Jan 18th, 2023. Study eligibility criteria, participants, and interventionsTo be included, pesticide exposure should be measured in maternal biological samples during pregnancy and in log-transformed forms. The primary outcome was preterm birth and the secondary outcome was gestational age at birth. Study appraisal, synthesis methods and confidence assessmentQuality of studies was evaluated using OHAT Risk of Bias Tool. Evidence was quantitatively synthesized with Correlated and Hierarchical Effects (CHE) model. The confidence rating in the body of evidence was done using OHAT. ResultsA total of 21 studies reported by 18 papers were included, with 7 studies for preterm birth and 19 for gestational age at birth. The meta-analysis found a ten-fold increase of pesticide concentrations was potentially associated with risk of preterm birth (pooled OR = 1.28; 95%CI: 0.93, 1.78) and shortened gestational age at birth (β = −0.10; 95%CI: −0.21, 0.01). Sampling biospecimens in different trimesters was identified as a potential modifier in the association between pesticide exposure and length of gestational age (F = 2.77, P < 0.05). For studies that collected samples at any time during pregnancy, pesticide exposure was found to be associated with shortened length of gestational age (β = −0.43; 95%CI: −0.81, −0.06). The confidence rating in the body of evidence was “moderate” and “very low” for preterm birth and gestational age at birth, respectively. ConclusionOur result suggested moderate evidence of an association between pesticide exposure and higher risk of preterm birth. Yet more studies are still needed with larger sample size and careful considerations of confounders and accuracy of outcome measurements. Attention is also required on other pesticide compounds in addition to organochlorine and organophosphorus pesticides, and on windows of susceptibility.

Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data

In the era of industrial big data, data-driven methods based on deep learning have made a splash in the field of mechanical PHM. However, the performance of deep learning models is limited by the depth of networks while previous supervised learning methods can only be applied to a small amount of labeled data while most of data acquired in the field are unlabeled, resulting in overfitting and low generalization of models. To address the above issues, a semi-supervised framework via temporal broad learning system embedding manifold regularization (TBLSMR) is proposed for machinery health assessment with unlabeled data. Specifically, linear and nonlinear temporal feature information is first learned from labeled and unlabeled data by TBLS. Then the Laplacian matrix is introduced embedding manifold regularization and used to construct objective function. Meanwhile, a specific ridge regression algorithm is designed to easily calculate the weights of network. Finally, an end-to-end mapping between the feature information layer and the label layer is established. The effectiveness of the proposed method is verified by two case studies. The comparison results with traditional models show that TBLSMR obtains high prediction accuracy while achieving training consumption savings, making it a promising approach for machinery health assessment that meets industrial needs.

Soil Health Assessment and Management Framework for Water-Limited Environments: Examples from the Great Plains of the USA

Healthy soils provide the foundation for sustainable agriculture. However, soil health degradation has been a significant challenge for agricultural sustainability and environmental quality in water-limited environments, such as arid and semi-arid regions. Soils in these regions is often characterized by low soil organic matter (SOM), poor fertility, and low overall productivity, thus limiting the ability to build SOM. Soil health assessment frameworks developed for more productive, humid, temperate environments typically emphasize building SOM as a key to soil health and have identified the best management practices that are often difficult to implement in regions with water limitations. This study reviewed existing soil health assessment frameworks to assess their potential relevance for water-limited environments and highlights the need to develop a framework that links soil health with key ecosystem functions in dry climates. It also discusses management strategies for improving soil health, including tillage and residue management, organic amendments, and cropping system diversification and intensification. The assessment of indicators sensitive to water management practices could provide valuable information in designing soil health assessment frameworks for arid and semi-arid regions. The responses of soil health indicators are generally greater when multiple complementary soil health management practices are integrated, leading to the resilience and sustainability of agriculture in water-limited environments.