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- Aakjær, Mia, et al.
(author)
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Surveillance of Antidepressant Safety (SADS) : Active Signal Detection of Serious Medical Events Following SSRI and SNRI Initiation Using Big Healthcare Data
- 2021
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In: Drug Safety. - : Springer. - 0114-5916 .- 1179-1942. ; 44, s. 1215-1230
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Journal article (peer-reviewed)abstract
- Introduction The current process for generating evidence in pharmacovigilance has several limitations, which often lead to delays in the evaluation of drug-associated risks.Objectives In this study, we proposed and tested a near real-time epidemiological surveillance system using sequential, cumulative analyses focusing on the detection and preliminary risk quantification of potential safety signals following initiation of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs).Methods We emulated an active surveillance system in an historical setting by conducting repeated annual cohort studies using nationwide Danish healthcare data (1996–2016). Outcomes were selected from the European Medicines Agency's Designated Medical Event list, summaries of product characteristics, and the literature. We followed patients for a maximum of 6 months from treatment initiation to the event of interest or censoring. We performed Cox regression analyses adjusted for standard sets of covariates. Potential safety signals were visualized using heat maps and cumulative hazard ratio (HR) plots over time.Results In the total study population, 969,667 new users were included and followed for 461,506 person-years. We detected potential safety signals with incidence rates as low as 0.9 per 10,000 person-years. Having eight different exposure drugs and 51 medical events, we identified 31 unique combinations of potential safety signals with a positive association to the event of interest in the exposed group. We proposed that these signals were designated for further evaluation once they appeared in a prospective setting. In total, 21 (67.7%) of these were not present in the current summaries of product characteristics.Conclusion The study demonstrated the feasibility of performing epidemiological surveillance using sequential, cumulative analyses. Larger populations are needed to evaluate rare events and infrequently used antidepressants.
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- Aaltonen, Harri, et al.
(author)
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A simulation environment for training a reinforcement learning agent trading a battery storage
- 2021
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In: Energies. - : MDPI. - 1996-1073. ; 14:17
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Journal article (peer-reviewed)abstract
- Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.
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- Aaltonen, Harri, et al.
(author)
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Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach
- 2022
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In: Energies. - : MDPI. - 1996-1073. ; 15:14
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Journal article (peer-reviewed)abstract
- Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multiâ€objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physicsâ€based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs.
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- Abb, Marcel J.S., et al.
(author)
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Thermal Stability of Single-Crystalline IrO2(110) Layers : Spectroscopic and Adsorption Studies
- 2020
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In: Journal of Physical Chemistry C. - : American Chemical Society (ACS). - 1932-7447 .- 1932-7455. ; 124:28, s. 15324-15336
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Journal article (peer-reviewed)abstract
- The interaction of ultrathin single-crystalline IrO2(110) films with the gas phase proceeds via the coordinatively unsaturated sites (cus), in particular Ircus, the undercoordinated oxygen species on-top O (Oot) that are coordinated to Ircus, and bridging O (Obr). With the combination of different experimental techniques, such as thermal desorption spectroscopy, scanning tunneling microscopy (STM), high-resolution core-level spectroscopy (HRCLS), infrared spectroscopy, and first-principles studies employing density functional theory calculations, we are able to elucidate surface properties of single-crystalline IrO2(110). We provide spectroscopic fingerprints of the active surface sites of IrO2(110). The freshly prepared IrO2(110) surface is virtually inactive toward gas-phase molecules. The IrO2(110) surface needs to be activated by annealing to 500-600 K under ultrahigh vacuum (UHV) conditions. In the activation step, Ircus sites are liberated from on-top oxygen (Oot) and monoatomic Ir metal islands are formed on the surface, leading to the formation of a bifunctional model catalyst. Vacant Ircus sites of IrO2(110) allow for strong interaction and accommodation of molecules from the gas phase. For instance, CO can adsorb atop on Ircus and water forms a strongly bound water layer on the activated IrO2(110) surface. Single-crystalline IrO2(110) is thermally not very stable although chemically stable. Chemical reduction of IrO2(110) by extensive CO exposure at 473 K is not observed, which is in contrast to the prototypical RuO2(110) system.
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- Abba, Alia Besma, et al.
(author)
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Copper and Zinc Removal from Wastewater Using Alum Sludge Recovered from Water Treatment Plant
- 2022
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In: Sustainability. - : MDPI. - 2071-1050. ; 14:16
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Journal article (peer-reviewed)abstract
- The study aimed to determine Aluminum sludge composition and structure for its valorisation as an alternative natural material for heavy metals removal from wastewater for further reuse as treated water in different applications. The study was conducted to investigate the introduction of Al-bearing sludge composition. The physical and chemical properties were examined using X-ray diffraction tests (XRD), scanning electron microscope tests (SEM), Fourier-transform infrared tests (FTIR), and Brunauer-Emmett-Teller tests (BET). Furthermore, the heavy metal concentrations of synthetic wastewater were measured using the spectrophotometry method. The experimental procedure is based on testing different pH limits and amounts of aluminum sludge to find the optimum conditions for copper (Cu) and zinc (Zn) removal. The results demonstrated a high removal efficiency where its value reached up to 97.4% and 96.6% for Zn and Cu, respectively, in an acidic medium (pH = 6) using a relatively high amount of sludge (1400 mg). Nevertheless, a low efficiency was obtained in the strongly acidic medium (pH = 4) and a smaller sludge amount of about 480 mg.
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- Abba, S. I., et al.
(author)
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Effluents quality prediction by using nonlinear dynamic block-oriented models : A system identification approach
- 2021
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In: Desalination and Water Treatment. - : Desalination Publications. - 1944-3994 .- 1944-3986. ; 218, s. 52-62
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Journal article (peer-reviewed)abstract
- The dynamic and complex municipal wastewater treatment plant (MWWTP) process should be handled efficiently to safeguard the excellent quality of effluents characteristics. Most of the available mathematical models do not efficiently capture the MWWTP process, in such cases, the data-driven models are reliable and indispensable for effective modeling of effluents characteristics. In the present research, two nonlinear system identification (NSI) models namely; Hammerstein-Wiener model (HW) and nonlinear autoregressive with exogenous (NARX) neural network model, and a classical autoregressive (AR) model were proposed to predict the characteristics of the effluent of total suspended solids (TSSeff) and pHeff from Nicosia MWWTP in Cyprus. In order to attain the optimal models, two different combinations of input variables were cast through auto-correla-tion function and partial auto-correlation analysis. The prediction accuracy was evaluated using three statistical indicators the determination coefficient (DC), root mean square error (RMSE) and correlation coefficient (CC). The results of the appraisal indicated that the HW model outperformed NARX and AR models in predicting the pHeff, while the NARX model performed better than the HW and AR models for TSSeff prediction. It was evident that the accuracy of the HW increased averagely up to 18% with regards to the NARX model for pHeff . Likewise, the TSSeff performance increased averagely up to 25% with regards to the HW model. Also, in the validation phase, the HW model yielded DC, RMSE, and CC of 0.7355, 0.1071, and 0.8578 for pHeff, while the NARX model yielded 0.9804, 0.0049 and 0.9902 for TSSeff, respectively. For comparison with the traditional AR, the results showed that both HW and NARX models outperformed in (TSSeff) and pHeff prediction at the study location. Hence, the outcomes determined that the NSI model (i.e., HW and NARX) are reliable and resilient modeling tools that could be adopted for pHeff and TSSeff prediction.
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- Abba, S.I., et al.
(author)
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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
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In: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
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Journal article (peer-reviewed)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
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