Information on sales and emission of selected pharmaceuticals were used to predict their concentrations in Japanese wastewater influent through a >300 of pharmaceuticals data sink. A combined wastewater-based epidemiology and environmental risk analysis follow was established. By comparing predicted environmental concentrations (PECs) of pharmaceuticals in wastewater influent against measured environmental concentrations (MECs) reported in previous studies, it was found that the model gave accurate results for 17 pharmaceuticals (0.5 < PEC/MEC < 2), and acceptable results for 32 out of 40 pharmaceuticals (0.1 < PEC/MEC < 10). Although the majority of pharmaceuticals considered in the model were antibiotics and analgesics, pranlukast, a receptor antagonist, was predicted to have the highest concentration in wastewater influent. With regard to the composition of wastewater effluent, the Estimation Program Interface (EPI) suite was used to predict pharmaceutical removal through activated sludge treatment. Although the performance of the EPI suite was variable in terms of accurate prediction of the removal of different pharmaceuticals, it could be an efficient tool in practice for predicting removal under extreme scenarios. By using the EPI suite with input data on PEC in the wastewater influent, the PEC values of pharmaceuticals in wastewater effluent were predicted. The concentrations of 26 pharmaceuticals were relatively high (>1 μg/L), and the PECs of 6 pharmaceuticals were extremely high (>10 μg/L) in wastewater effluent, which could be attributed to their high usage rates by consumers and poor removal rates in wastewater treatment plants (WWTPs). Furthermore, environmental risk assessment (ERA) was carried out by calculating the ratio of predicted no effect concentration (PNEC) to PEC of different pharmaceuticals, and it was found that 9 pharmaceuticals were likely to have high toxicity, and 54 pharmaceuticals were likely to have potential toxicity. It is recommended that this is further investigated in detail. The priority screening and environmental risk assessment results on pharmaceuticals can provide reliable basis for policy-making and environmental management.
Experimental observations from neuroscience have suggested that the cognitive process of human brain is realized as probabilistic reasoning and further modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented by network of neurons in the brain. Here a novel implementation of neural circuit, named the sampling-tree model, is proposed to fulfill this aim. By using a deep tree structure to implement sampling with simple and stackable basic neural network motifs for any given Bayesian networks, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without intensive iteration and scale-limitation. As a result, this model utilizes the structure benefit of neuronal system, i.e., neuronal abundance and multihierarchy, to perform fast inference in an extendable way.
Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice.
The Guanzhong basin is a part of the three top priority regions in China's blue sky action as of 2019. Understanding the chemical composition, sources, and atmospheric process of aerosol in this region is therefore imperative for improving air quality. In this study, we present, for the first time, the seasonal variations of organic aerosol (OA) in Xi'an, the largest city in the Guanzhong basin. Biomass burning OA (BBOA) and oxidized OA (OOA) contributed N50% of OA in both autumn and winter. The average concentrations of BBOA in autumn (14.8 +/- 5.1 mu g m(-3)) and winter (11.6 +/- 6.8 mu g m(-3)) were similar. The fractional contribution of BBOA to total OA, however, decreased from 31.9% in autumn to 15.3% in winter, because of enhanced contributions from other sources in winter. The OOA fraction in OA increased largely from 20.9% in autumn to 34.9% in winter, likely due to enhanced emissions of precursors and stagnant meteorological conditions which facilitate the accumulation and secondary formation. A large increase in OOA concentration was observed during polluted days, by a factor of similar to 4 in autumn and similar to 6 in winter compared to clean days. In both seasons, OOA formation was most likely dominated by photochemical oxidation when aerosol liquid water content was b30 mu g m(-3) or by aqueous-phase processes when Ox was b35 ppb. A higher concentration of BBOA was observed for air masses circulated within the Guanzhong basin (16.5-18.1 mu g m(-3)), compared to air masses from Northwest and West (10.9-14.5 mu g m(-3)). Furthermore, compared with OA fraction in non-refractory PM1 in other regions of China, BBOA (17-19%) and coal combustion OA (10-20%) were major emission sources in the Guanzhong Basin and the BTH region, respec-tively, whereas OOA (10-34%) was an important source in all studied regions. (C) 2020 Elsevier B.V. All rights reserved.
This article presents a power-efficient purely voltage-controlled oscillator (VCO)-based second-order continuous-time (CT) ΔΣ analog-to-digital converter (ADC), featuring a modified digital phase-locked loop (DPLL) structure. The proposed ADC combines a VCO with a switched-ring oscillator (SRO)-based time-to-digital converter (TDC), which enables second-order noise shaping without any operational transconductance amplifiers (OTAs). The nonlinearity of the front-end VCO is mitigated by putting it inside a closed loop. An array of phase/frequency detectors (PFDs) is used to relax the requirement on the VCO center frequency and thus reduces the VCO power and noise. The proposed architecture also realizes an intrinsic tri-level data-weighted averaging (DWA). A prototype chip is fabricated in a 40-nm CMOS process. The proposed ADC achieves a peak signal-to-noise-and-distortion ratio (SNDR) of 69.4 dB over 5.2-MHz bandwidth, while operating at the 260 MS/s and consuming 0.86 mW from a 1.1-V supply.
A semitransparent perovskite solar cell (PSC) with a dielectric/metal/dielectric (DMD) multilayer film as the top transparent electrode is investigated. Through adjusting the thickness and the deposition rate of Ag and WO3 layers, a transparent electrode with a low sheet resistance of 7 omega sq(-1) and high average visible transmittance (AVT) of 73% in the visible wavelength range of 400-800 nm is obtained. Using the resultant DMD film as the top transparent electrode and different bandgap perovskites of CH3NH3PbI3 (MAPbI(3)), CH(NH2)(2)PbI3 (FAPbI(3)), and FA(0.5)MA(0.38)Cs(0.12)PbI(2.04)Br(0.96) as the optical active layer, a solar cell with a device architecture of ITO/SnO2/perovskite/spiro-OMeTAD/MoO3/Ag/WO3 is fabricated. A series of efficient semitransparent PSCs with high transmittance are achieved.
Formaldehyde (HCHO) is the most abundant atmospheric carbonyl compound and plays an important role in the troposphere. However, HCHO detection via traditional incoherent broadband cavity enhanced absorption spectroscopy (IBBCEAS) is limited by short optical path lengths and weak light intensity. Thus, a new light-emitting diode (LED)-based IBBCEAS was developed herein to measure HCHO in ambient air. Two LEDs (325 and 340 nm) coupled by a Y-type fiber bundle were used as an IBBCEAS light source, which provided both high light intensity and a wide spectral fitting range. The reflectivity of the two cavity mirrors used herein was 0.99965 (1 - reflectivity = 350 ppm loss) at 350 nm, which corresponded with an effective optical path length of 2.15 km within a 0.84 m cavity. At an integration time of 30 s, the measurement precision (1 sigma) for HCHO was 380 parts per trillion volume (pptv), and the corresponding uncertainty was 8.3%. The instrument was successfully deployed for the first time in a field campaign and delivered results that correlated well with those of a commercial wet-chemical instrument based on Hantzsch fluorimetry (R2 = 0.769). The combined light source based on a Y-type fiber bundle overcomes the difficulty of measuring ambient HCHO via IBBCEAS in near-ultraviolet range, which may extend IBBCEAS technology to measure other atmospheric trace gases with high precision.