Signal validation and process monitoring problems in many cases require the prediction of one or more process variables in a system. The feasibility of using neural networks to characterize one variable as a function of other related variables is studied. The Backpropagation Network (BPN) is used to develop ``models`` of signals from both a commercial power plant and the EBR-II. Several innovations are made in the algorithm, the most significant of which is the progressive adjustment of the sigmoidal threshold function and weight updating terms, thus leading to the designation ``Adaptive`` Backpropagation Neural Network. The estimation of system variables is performed traditionally using either physical models or empirical models. The prediction of system variables is important in control systems for validating instrumentation outputs and for process monitoring. The model-based prediction assumes a fixed structure for characterizing steady-state or dynamic relationship among process variables. The applications to large and complex systems require more time in order to get an accurate model. Since our goal is to relate signals in a subsystem of a plant, such a relationship can be developed by using neural network ``models`` which provide results faster than model-based techniques. Both steady-state and transient behavior can be incorporated into more » the network during training. « less

Sensor validation in power plants using adaptive backpropagation neural network

A reusable agent-based generic model is presented for a specific class of Ambient Intelligence pplications: those cases addressing human wellbeing and functioning from a human-like understanding. The model incorporates ontologies, knowledge and dynamic models from human-directed sciences such as psychology, social science, neuroscience and biomedical sciences. The model has been formally specified, and it is shown how for specific applications it can be instantiated by applicationspecific elements, thus providing an executable specification that can be used for prototyping. Moreover, it is shown how dynamic properties can be formally specified and verified against generated traces.

/pdf/a-generic-architecture-for-human-aware-ambient-computing-1d5hsr2cuu.pdf

A Generic Architecture for Human-Aware Ambient Computing

An interdisciplinary fetal-neonatal neurology (FNN) program over the first 1,000 days teaches perspectives of the neural exposome that are applicable across the life span. This curriculum strengthens neonatal neurocritical care, pediatric, and adult neurology training objectives. Teaching at maternal-pediatric hospital centers optimally merges reproductive, pregnancy, and pediatric approaches to healthcare. Phenotype–genotype expressions of health or disease pathways represent a dynamic neural exposome over developmental time. The science of uncertainty applied to FNN training re-enforces the importance of shared clinical decisions that minimize bias and reduce cognitive errors. Trainees select mentoring committee participants that will maximize their learning experiences. Standardized questions and oral presentations monitor educational progress. Master or doctoral defense preparation and competitive research funding can be goals for specific individuals. FNN principles applied to practice offer an understanding of gene–environment interactions that recognizes the effects of reproductive health on the maternal-placental-fetal triad, neonate, child, and adult. Pre-conception and prenatal adversities potentially diminish life-course brain health. Endogenous and exogenous toxic stressor interplay (TSI) alters the neural exposome through maladaptive developmental neuroplasticity. Developmental disorders and epilepsy are primarily expressed during the first 1,000 days. Communicable and noncommunicable illnesses continue to interact with the neural exposome to express diverse neurologic disorders across the lifespan, particularly during the critical/sensitive time periods of adolescence and reproductive senescence. Anomalous or destructive fetal neuropathologic lesions change clinical expressions across this developmental-aging continuum. An integrated understanding of reproductive, pregnancy, placental, neonatal, childhood, and adult exposome effects offers a life-course perspective of the neural exposome. Exosome research promises improved disease monitoring and drug delivery starting during pregnancy. Developmental origins of health and disease principles applied to FNN practice anticipate neurologic diagnoses with interventions that can benefit successive generations. Addressing health care disparities in the Global South and high-income country medical deserts require constructive dialogue among stakeholders to achieve medical equity. Population health policies require a brain capital strategy that reduces the global burden of neurologic diseases by applying FNN principles and practice. This integrative neurologic care approach will prolong survival with an improved quality of life for persons across the lifespan confronted with neurological disorders.

Interdisciplinary fetal-neonatal neurology training applies neural exposome perspectives to neurology principles and practice

Mycotoxins are natural metabolites produced by fungi that contaminate food and feed worldwide. They can pose a threat to human and animal health, mainly causing chronic effects, e.g., immunotoxic and carcinogenic. Due to climate change, an increase in European population exposure to mycotoxins is expected to occur, raising public health concerns. This urges us to assess the current human exposure to mycotoxins in Europe to allow monitoring exposure and prevent future health impacts. The mycotoxins deoxynivalenol (DON) and fumonisin B1 (FB1) were considered as priority substances to be studied within the European Human Biomonitoring Initiative (HBM4EU) to generate knowledge on internal exposure and their potential health impacts. Several policy questions were addressed concerning hazard characterization, exposure and risk assessment. The present article presents the current advances attained under the HBM4EU, research needs and gaps. Overall, the knowledge on the European population risk from exposure to DON was improved by using new harmonised data and a newly derived reference value. In addition, mechanistic information on FB1 was, for the first time, organized into an adverse outcome pathway for a congenital anomaly. It is expected that this knowledge will support policy making and contribute to driving new Human Biomonitoring (HBM) studies on mycotoxin exposure in Europe.

/pdf/current-advances-research-needs-and-gaps-in-mycotoxins-353ayw9e.pdf

Current Advances, Research Needs and Gaps in Mycotoxins Biomonitoring under the HBM4EU—Lessons Learned and Future Trends

Human biomonitoring (HBM) is a rapidly developing field that is emphasized as an important approach for the assessment of health risks. However, its value for health risk assessment (HRA) remains to be clarified. We performed a review of publications concerned with applications of HBM in the assessment of health risks. The selection of publications for this review was limited by the search engines used (only PubMed and Scopus) and a timeframe of the last five years. The review focused on the clarity of 10 HRA elements, which influence the quality of HRA. We show that the usage of HBM data in HRA is limited and unclear. Primarily, the key HRA elements are not consistently applied or followed when using HBM in such assessments, and secondly, there are inconsistencies regarding the understanding of fundamental risk analysis principles and good practices in risk analysis. Our recommendations are as follows: (i) potential usage of HBM data in HRA should not be non-critically overestimated but rather limited and aligned to a specific value for exposure assessment or for the interpretation of health damage; (ii) improvements to HRA approaches, using HBM information or not, are needed and should strictly follow theoretical foundations of risk analysis.

/pdf/human-biomonitoring-data-in-health-risk-assessments-137dzkh5.pdf

Human Biomonitoring Data in Health Risk Assessments Published in Peer-Reviewed Journals between 2016 and 2021: Confronting Reality after a Preliminary Review

This paper describes a procedure of identifying sensor faults and reconstructing the erroneous measurements. Data mining algorithms are successfully applied for deriving models that estimate the value of one variable based on correlated others. The estimated values can then be used instead of the recorded ones of a measuring instrument with false reading. The aim is to reassure the correctness of data entered to an optimization software application under development for the Thermal Power Plants of Western Macedonia, Greece.

Utilizing data mining algorithms for identification and reconstruction of sensor faults: a Thermal Power Plant case study

The exposome paradigm through an integrated approach to investigating the impact of perinatal exposure to metals on child neurodevelopment in two cohorts carried out in Slovenia (PHIME cohort) and Greece (HERACLES cohort) respectively, is presented herein. Heavy metals are well-known neurotoxicants with well-established links to impaired neurodevelopment. The links between in utero and early-life exposure to metals, metabolic pathway dysregulation, and neurodevelopmental disorders were drawn through urinary and plasma untargeted metabolomics analysis, followed by the combined application of in silico and biostatistical methods. Heavy metal prenatal and postnatal exposure was evaluated, including parameters indirectly related to exposure and health adversities, such as sociodemographic and anthropometric parameters and dietary factors. The primary outcome of the study was that the identified perturbations related to the TCA cycle are mainly associated with impaired mitochondrial respiration, which is detrimental to cellular homeostasis and functionality; this is further potentiated by the capacity of heavy metals to induce oxidative stress. Insufficient production of energy from the mitochondria during the perinatal period is associated with developmental disorders in children. The HERACLES cohort included more detailed data regarding diet and sociodemographic status of the studied population, allowing the identification of a broader spectrum of effect modifiers, such as the beneficial role of a diet rich in antioxidants such as lycopene and ω-3 fatty acids, the negative effect the consumption of food items such as pork and chicken meat has or the multiple impacts of fish consumption. Beyond diet, several other factors have been proven influential for child neurodevelopment, such as the proximity to pollution sources (e.g., waste treatment site) and the broader living environment, including socioeconomic and demographic characteristics. Overall, our results demonstrate the utility of exposome-wide association studies (EWAS) toward understanding the relationships among the multiple factors that determine human exposure and the underlying biology, reflected as omics markers of effect on neurodevelopment during childhood.

An exposome connectivity paradigm for the mechanistic assessment of the effects of prenatal and early life exposure to metals on neurodevelopment

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Human Biomonitoring Data in Health Risk Assessments Published in Peer-Reviewed Journals between 2016 and 2021: Confronting Reality after a Preliminary Review

Utilizing data mining algorithms for identification and reconstruction of sensor faults: a Thermal Power Plant case study

An exposome connectivity paradigm for the mechanistic assessment of the effects of prenatal and early life exposure to metals on neurodevelopment