2,592 research outputs found

    An Integrated Deep Learning Model with Genetic Algorithm (GA) for Optimal Syngas Production Using Dry Reforming of Methane (DRM)

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    The dry reforming of methane is a chemical process transforming two primary sources of greenhouse gases, i.e., carbon dioxide (CO2) and methane (CH4), into syngas, a versatile precursor in the industry, which has gained significant attention over the past decades. Nonetheless, commercial development of this eco-friendly process faces barriers such as catalyst deactivation and high energy demand. Artificial intelligence (AI), specifically deep learning, accelerates the development of this process by providing advanced analytics. However, deep learning requires substantial training samples and collecting data on a bench scale encounters cost and physical constraints. This study fills this research gap by employing a pretraining approach, which is invaluable for small datasets. It introduces a software sensor for regression (SSR) powered by deep learning to estimate the quality parameters of the process. Moreover, combining the SSR with a genetic algorithm offers a prescriptive analysis, suggesting optimal thermodynamic parameters to improve the process efficiency

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Livestock health and disease economics: a scoping review of selected literature.

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    Animal diseases in production and subsistence environments have the potential to negatively affect consumers, producers, and economies as a whole. A growing global demand for animal sourced food requires safe and efficient production systems. Understanding the burden of animal disease and the distribution of burden throughout a value chain informs policy that promotes safe consumption and efficient markets, as well as providing more effective pathways for investment. This paper surveys existing knowledge on the burden of animal disease across economic categories of production, prevention and treatment, animal welfare, and trade and regulation. Our scoping review covers 192 papers across peer-reviewed journals and reports published by organizations. We find there exists a gap in knowledge in evaluating what the global burdens of animal diseases are and how these burdens are distributed in value chains. We also point to a need for creating an analytical framework based on established methods that guides future evaluation of animal disease burden, which will provide improved access to information on animal health impacts

    Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

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    Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

    Discovering circulating protein biomarkers through in-depth plasma proteomics

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    Plasma, i.e., the liquid component of blood, is one of the most clinically used samples for biomarker measurement. Despite that plasma proteins and metabolites are the most frequently analysed biomarkers in practice, identifying and implementing new circulating protein biomarkers for diagnosis, treatment prediction, prognosis, and disease monitoring has been limited. This PhD thesis compiles the discovery of systemic alterations in the blood plasma proteome and potential biomarkers related to disease status, prognosis, or treatment through plasma proteomics. We analysed plasma and serum samples with global proteomics by high-resolution isoelectric focusing (HiRIEF) and liquid chromatography coupled with mass-spectrometry (LC-MS/MS), and targeted proteomics by antibody-based proximity extension assays (PEA) in three diseases that would benefit from blood biomarkers: stage IV metastatic cutaneous melanoma (mCM), glioblastoma (GBM), and coronavirus disease 2019 (COVID-19). Specifically: a.) New treatment options for mCM substantially prolong overall survival (OS), but multiple patients do not respond to treatment or develop treatment resistance, thus having shorter progression free survival (PFS). Corroborated by the presence of multiple metastases, which makes biomarker sampling difficult, circulating proteins derived from the tumour and in response to treatment could serve as predictive and prognostic biomarkers in mCM. b.) GBM is the most malignant primary brain tumour with limited treatment options and notoriously short OS. Sampling biomarkers for GBM requires an invasive surgical intervention on the skull, which makes GBM a good candidate for circulating protein biomarkers for prognosis and monitoring. c.) COVID-19 is an inflammation-driven infectious disease that affects multiple organs and systems, thus making the plasma proteome a good source to explore systemic biological processes occurring in COVID-19. In papers I and II, using HiRIEF LC-MS/MS and PEA, we explored the treatment-driven plasma proteome alterations in mCM patients treated with anti-PD-1 immune checkpoint inhibitors (ICI) and MAPK-inhibitors (MAPKi), respectively, and identified potential treatment predictive and monitoring biomarkers. mCM patients treated with anti-PD-1 ICI had a strong increase in soluble PD-1 levels during treatment, and upregulation of proteins involved in T-cell response. BRAF[V600]-mutated mCM patients treated with MAPKi had deregulation in proteins involved in immune response and proteolysis. CPB1 had the highest increase in patients treated with BRAF- and MEK-inhibitors and was associated with longer PFS. Higher levels of several proteins involved in inflammation before treatment were associated with shorter PFS regardless of ICI or MAPKi treatment. In paper III, using HiRIEF LC-MS/MS and PEA, we longitudinally analysed the plasma proteome dynamics of GBM patients, collecting plasma samples before surgery and at three timepoints after surgery. Through consensus clustering, based on treatment-naĂŻve plasma protein levels, we identified two patient clusters that differed in median OS. The association between the cluster membership and OS remained consistent after adjustment for age, sex, and treatment. Through machine learning, we identified protein panels that separated the patient clusters and may serve as prognostic biomarkers. The largest alterations in the plasma proteome of GBM patients occurred within two months after surgery, whereas the plasma protein levels at later timepoints had no difference compared to pre- surgery levels. We observed a decrease in glioma-elevated proteins in the blood after surgery, identifying potential monitoring biomarkers. In paper IV, using HiRIEF LC-MS/MS, we analysed serum proteome alterations in hospitalised COVID-19 patients in comparison to healthy controls, and identified a strong upregulation in inflammatory, interferon-induced, and proteasomal proteins. Several protein groups showed association with clinical parameters of COVID-19 severity, including proteasomal proteins. Serum proteome alterations were traceable to proteome alterations induced in a lung adenocarcinoma cell line (Calu-3) by infection with SARS-CoV-2. Finally, we performed the first meta-analysis of global proteomics studies of the soluble blood proteome in COVID-19, providing estimates of standardised mean differences and summary receiver operating characteristics curves. We demonstrate the high accuracy and precision of HiRIEF LC-MS/MS when compared to the meta-analysis estimates and pinpoint proteins that may serve as biomarkers of COVID-19. In summary, this thesis postulates that new circulating protein biomarkers would be clinically useful. By combining mass-spectrometry- and antibody-based-proteomics, we demonstrate the potential of in-depth analyses of the plasma proteome in capturing systemic alterations related to treatment, survival, and disease status, pinpointing potentially novel biomarkers that require validation in larger cohorts

    The evolution and ecology of oxidative and antioxidant status : a comparative approach in African mole-rats

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    DATA AVAILABILITY STATEMENT : Data are contained within the article or Supplementary Materials.SUPPLEMENTARY MATERIALS: FILE S1: The family of Bathyergidae, their distribution, their life history, reproductive structure, reproductive suppression and oxidative ecology; FILE S2: Data.The naked mole-rat of the family Bathyergidae has been the showpiece for ageing research as they contradict the traditional understanding of the oxidative stress theory of ageing. Some other bathyergids also possess increased lifespans, but there has been a remarkable lack of comparison between species within the family Bathyergidae. This study set out to investigate how plasma oxidative markers (total oxidant status (TOS), total antioxidant capacity (TAC), and the oxidative stress index (OSI)) differ between five species and three subspecies of bathyergids, differing in their maximum lifespan potential (MLSP), resting metabolic rate, aridity index (AI), and sociality. We also investigated how oxidative markers may differ between captive and wild-caught mole-rats. Our results reveal that increased TOS, TAC, and OSI are associated with increased MLSP. This pattern is more prevalent in the social-living species than the solitary-living species. We also found that oxidative variables decreased with an increasing AI and that wild-caught individuals typically have higher antioxidants. We speculate that the correlation between higher oxidative markers and MLSP is due to the hypoxia-tolerance of the mole-rats investigated. Hormesis (the biphasic response to oxidative stress promoting protection) is a likely mechanism behind the increased oxidative markers observed and promotes longevity in some members of the Bathyergidae family.The SARChI chair of Mammalian Behavioural Ecology and Physiology from the DST-NRF South Africa, the National Research Foundation, and the Natural Environment Research Council and the University of Pretoria.https://www.mdpi.com/journal/antioxidantsMammal Research InstituteZoology and Entomolog

    National-scale detection of reservoir impacts through hydrological signatures

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    Reservoirs play a vital role in the supply and management of water resources and their operation can significantly alter downstream flow. Despite this, reservoirs are frequently excluded or poorly represented in large-scale hydrological models, which can be partly attributed to a lack of open-access data describing reservoir operations, inflow and storage. To help inform the development of reservoir operation schemes, we collate a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large-scales from downstream flow records only. By removing the need for pre-and-post-reservoir flow timeseries (a requirement of many pre-existing techniques), these signatures facilitate the assessment of flow alteration across a much wider range of catchments. To demonstrate their application, we calculate the signatures across Great Britain in 111 benchmark (i.e., near-natural) catchments and 186 reservoir catchments (where at least one upstream reservoir is present). We find that abstractions from water resource reservoirs induce deficits in the water balance, and that pre-defined flow releases (e.g., the compensation flow) reduce variability in the downstream flow duration curve and in intra-annual low flows. By comparing signatures in benchmark and reservoir catchments, we define thresholds above which the influence of reservoirs can be distinguished from natural variability and identify 40 catchments significantly impacted by the presence of reservoirs. The signatures also provide insights into local reservoir operations, which can inform the development of tailored reservoir operation schemes, and identify locations where current modeling practices (which lack reservoir representation) will be insufficient

    Aquaculture governance: five engagement arenas for sustainability transformation

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    A greater focus on governance is needed to facilitate effective and substantive progress toward sustainability transformations in the aquaculture sector. Concerted governance efforts can help move the sector beyond fragmented technical questions associated with intensification and expansion, social and environmental impacts, and toward system-based approaches that address interconnected sustainability issues. Through a review and expert-elicitation process, we identify five engagement arenas to advance a governance agenda for aquaculture sustainability transformation: (1) setting sustainability transformation goals, (2) cross-sectoral linkages, (3) land–water–sea connectivity, (4) knowledge and innovation, and (5) value chains. We then outline the roles different actors and modes of governance can play in fostering sustainability transformations, and discuss action items for researchers, practitioners, and policymakers to operationalize activities within their engagement arenas
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