Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Solutions to Water Pollution

Preface 1. Green Chemistry and Other Novel Solutions to Water Pollution: Overview Satinder (Sut) Ahuja 2. Ionizing Radiation Techniques To Determine Hydroxyl Radical Efficiencies of Organic Pollutant Mixtures in Treated Wastewaters Julie R. Peller, Kenneth P. Ishida, and Stephen P. Mezyk 3. Ferrate(VI): A Green Chemistry Oxidant for Removal of Antibiotics in Water Virender K. Sharma, George Anquandah, Hyunook Kim, Jia-Qian Jiang, and Radek Zboril 4. Natural Treatment of Surface Water and Groundwater with Woodchip Reactors M. Palomo, A. Bhandari, H. Enriquez, and W. Rodriguez 5. In Situ Immobilization of Mercury in Water, Soil, and Sediment Using Carboxymethyl Cellulose Stabilized Iron Sulfide Nanoparticles Yanyan Gong and Dongye Zhao 6. In Situ Dechlorination in Soil and Groundwater Using Stabilized Zero-Valent Iron Nanoparticles: Some Field Experience on Effectiveness and Limitations Man Zhang and Dongye Zhao 7. Polycation-Tethered Micelles as Immobilized Detergents for NAPL Remediation Paul Dubin, Jessica Schiffman, and Bingqian Zheng 8. Novel Production of Activated Carbon for Hampering Oligomerization of Phenolic Compounds Liang Yan and George A. Sorial 9. Cyclic Electrowinning/Precipitation (CEP) for the Removal of Heavy Metals from Aqueous Solution Mixtures J. M. Calo, P. Grimshaw, and G. Hradil 10. Macronutrient Removal and Recovery from Tertiary Treatment of Wastewater Sukalyan Sengupta 11. Functionalized Nanoparticles as Removable Membrane Coatings Megan A. Smith, Carly A. Barnard, and David A. Ladner 12. Hematite Nanoparticle Modified Granular Activated Carbon for Removal of Arsenic and Organic Co-Contaminants Arti Jain, Jeremy Sanner, Robert Sandoval, and Kiril Hristovski 13. Titanium Dioxide-Based Hybrid Ion-Exchange Media for Simultaneous Removal of Arsenic and Nitrate Jennifer Elton, Kiril Hristovski, and Paul Westerhoff 14. Applications of Zero-Valent Iron (ZVI) and Nanoscale ZVI to Municipal and Decentralized Drinking Water Systems-A Review Pei C. Chiu 15. Decentralized Wastewater Treatment for Distributed Water Reclamation and Reuse: The Good, The Bad, and The UglyDLExperience from a Case Study A. L. Prieto, D. Vuono, R. Holloway, J. Benecke, J. Henkel, T. Y. Cath, T. Reid, L. Johnson, and J. E. Drewes Editors' Biographies Indexes Author Index Subject Index
The Impact of Pessimistic Expectations on the Effects of COVID‐19‐Induced Uncertainty in the Euro Area*

Abstract We estimate a monthly interacted‐VAR model for euro area macroeconomic aggregates allowing for the impact of uncertainty shocks to depend on the state of the average outlook for the economy measured by survey data. We find that, in response to an uncertainty shock, the peak decrease in industrial production and inflation is around three and a half times larger during pessimistic times. We build an assessment of the role of uncertainty for a path of innovations consistent with the increase in the observed VSTOXX measure of uncertainty since the outset of the COVID‐19 epidemics in February and March 2020. Industrial production is predicted to experience a year‐over‐year peak loss of around 9.2% in the fourth quarter of 2020, and subsequently to recover with a rebound to pre‐crisis levels roughly in June 2021. The large impact is the result of an extreme shock to uncertainty occurring at a time of very negative expectations for the economic outlook. We conduct simulations that quantify the potential benefit of recovered confidence in reducing the uncertainty‐induced losses associated with a possible third wave of the pandemic.
Trade and FDI Thresholds of CO 2 emissions for a Green Economy in Sub-Saharan Africa

Purpose This study aims to focus on assessing how improving openness influences carbon dioxide (CO2) emissions in sub-Saharan Africa (SSA). Design/methodology/approach This study focusses on 49 countries in SSA for the period 2000–2018 divided into: 44 countries in SSA for the period 2000–2012; and 49 countries for the period 2006–2018. Openness is measured in terms of trade and foreign direct investment (FDI) inflows. The empirical evidence is based on the generalised method of moments. Findings The following main findings are established. First, enhancing trade openness has a net positive impact on CO2 emissions, while increasing FDI has a net negative impact. Second, the relationship between CO2 emissions and trade is a Kuznets shape, while the nexus between CO2 emissions and FDI inflows is a U-shape. Third, a minimum trade openness (imports plus exports) threshold of 100 (% of gross domestic product (GDP)) and 200 (% of GDP) is beneficial in promoting a green economy for the first and second samples, respectively. Fourth, FDI is beneficial for the green economy below critical masses of 28.571 of net FDI inflows (% of GDP) and 33.333 of net FDI inflows (% of GDP) for first and second samples, respectively. It follows from findings that while FDI can be effectively managed to reduce CO2 emissions, this may not be the case with trade openness because the corresponding thresholds for trade openness are closer to the maximum limit. Originality/value This study complements the extant literature by providing critical masses of trade and FDI that are relevant in promoting the green economy in SSA.
Empirical Study of Low-Latency Network Model with Orchestrator in MEC

SUMMARY The Internet of Things (IoT) with its support for cyber-physical systems (CPS) will provide many latency-sensitive services that require very fast responses from network services. Mobile edge computing (MEC), one of the distributed computing models, is a promising component of the low-latency network architecture. In network architectures with MEC, mobile devices will o ﬄ oad heavy computing tasks to edge servers. There exist numbers of researches about low-latency network architecture with MEC. However, none of the existing researches simultaneously satisfy the followings: (1) guarantee the latency of computing tasks and (2) implement a real system. In this paper, we designed and implemented an MEC based network architecture that guarantees the latency of o ﬄ oading tasks. More speciﬁcally, we ﬁrst estimate the total latency including computing and communication ones at the centralized node called orchestrator. If the estimated value exceeds the latency requirement, the task will be rejected. We then evaluated its performance in terms of the blocking probability of the tasks. To analyze the results, we compared the performance between obtained from experiments and simulations. Based on the comparisons, we clariﬁed that the computing latency estimation accuracy is a signiﬁcant factor for this system. key words: mobile edge computing, low-latency network, prototype, and experiment
The Impact of Financial Openness on the Earnings of Self-employed Workers - An Empirical Evidence from a Panel Dataset

This paper contributes to the literature on the effect of financial openness by investigating the factors and determinants which drive the income share to self-employed labor during financial liberalization. The question of the precise impact of liberalization on the share of the self-employed has received less attention in the literature. The authors use a de jure or a rule-based indicator as a measure of capital account openness. The empirical work is applied for a panel dataset of 30 countries during the period of 1970 - 2015. The results from all specifications support the hypothesis that financial integration leads to an increase in the unemployment rate as well as in the income share of self-employed. Nevertheless, the positive relation between financial openness and self-employed income is not evident when we focus solely on developed countries.
The Preposition Process of “guan”

The word “guan” develops the meaning of “control and jurisdiction” on the basis of the noun meaning of “key”. The use of preposition “guan” originated from the verb meaning “control”, and its intermediary syntactic condition is “guan+NP1+V2+NP2”, through the reanalysis and extension mechanism, the word “guan” semantically de-semanticized, experienced the process from “control” to “introduction of objects” and then to “disposition”; In the process of grammaticalization, the characteristics of dynamic parallelism, domain-limited, verb pairing are presented.
Topological-based roof modeling from 3D point clouds

Automatic extraction of building roofs from remote sensing data is important for many applications including 3D city modeling, urban planning, disaster management, and simulations. In this paper, we propose an automatic workﬂow for roof reconstruction by polygonal models from classiﬁed high-density LIDAR data. Roof planes are initially delineated by a segmentation algorithm combining a robust Hough-based normal estimator and a region growing strategy. Then, each roof is modeled by a 2D α -shape mesh which is used to discover not only building outline but also all ridges deﬁned by intersecting roof planes, without any geometrical calculations. The mesh directly encodes the topological relations between neighboring planes which allows us to build the ﬁnal polygonal model straightforwardly. This topological approach makes our solution more simple and robust than existing methods which mostly extract the intersection lines by means of geometrical computations. Experimental results show that the proposed workﬂow offers a high success rate for extraction at plane level (94% completeness, 92.7% correctness, 90.8% quality) when LIDAR point density is sufﬁciently high.
Research on EEG-based Graphic User Interface Kansei Design Evaluation

Graphical user interface (GUI) is designed as the interaction medium between the user and the interface, and the perceptual experience of GUI design has been paid more and more attention by users. Based on the theory of perceptual engineering (KE), two groups of different visual style interfaces were taken as an example to record the EEG data when users watched two groups of visual interfaces, in order to explore the user’s perceptual imagery and perceptual experience for the visual interface. It aims to meet the user’s perceptual needs and provide an effective evaluation method and design basis for the graphical user interface design. Firstly, the EEG spectrogram and brain topographic maps were obtained by data analysis and processing. The results showed that the activity levels of the θ wave and α wave induced by the two groups of different visual style interfaces were significantly different. Secondly, this paper analyzed the user’s perceptual imagery with GUI perceptual design elements, and concluded that the perceptual design elements of GUI would affect the user’s cognitive interest and perceptual experience. GUI design should focus on the unity and coordination of perceptual design elements and perceptual imageries. Finally, it is concluded that the EEG-based perceptual design evaluation method can effectively evaluate the GUI visual interface.
A NEW NON-CONVEX APPROACH FOR COMPRESSIVE SENSING MRI

—Compressive sensing (CS) is an eﬀective method for reconstructing magnetic resonance imaging (MRI) image from under-determined linear system (ULS). However, how to improve the accuracy of MRI image reconstructed by CS is still a serious problem, especially in noisy conditions. To solve this problem, in this paper, we propose a novel approach, dubbed as regularized maximum entropy function (RMEF) minimization algorithm. Speciﬁcally, motivated by the entropy function in information theory, we propose a maximum entropy function (MEF) to approximate L q -norm (0 < q < 1) as sparsity promoting objectives, and then the regularization mechanism for improving the de-noising performance is adopted. Combining the above two ideas, a new objective function of RMEF method is proposed, and the global minimum is iteratively solved. We further analyze the convergence to verify the robustness of the RMEF algorithm. Experiments demonstrate the state-of-the-art performances of the proposed RMEF algorithm and show that the RMEF achieves higher PSNR and SSIM than other widely-adopted methods in MRI image recovery.
Research Article Epigenetic variation in guava (Psidium guajava) genotypes during the vegetative and reproductive phases of the production cycle

Psidium guajava (guava) is a fruit tree of the Myrtaceae family of nutritional and economic importance and wide edaphoclimatic adaptation. For commercial orchard purposes, guava trees are derived from clonal propagation and the production cycle begins with drastic pruning. During its development the guava tree displays varied characteristics that may involve epigenetic mechanisms, such as DNA methylation, revealed by the formation of 5-methylcytosine (5-mC). In order to understand the variability of this mechanism, the %5-mC was evaluated in 22 guava genotypes from a working collection, in an experimental orchard, over five development stages – post-pruning, flowering, fructification, harvest and post-harvest – and subsequently in different tissues. Wide variation in DNA methylation was detected among the genotypes. Hypermethylation was found in one, whereas methylation and demethylation mechanisms were observed in 10 genotypes. Greater variability in DNA methylation among genotypes was found during the vegetative phase, mainly after pruning. On the other hand, uniformity was observed among the genotypes with regards to global DNA methylation during flowering, indicating that this mechanism is conserved in this specific phase. No differences in global DNA methylation was detected in the different plant tissues. In this study, the global epigenetic mechanisms of P. guajava were detected more frequently in vegetative than in reproductive phases. Important
Optimally stiffened thin shell structures in 3D printing

Thin shell structures have been widely applied in crafts, art, industry, architecture, aerospace, etc. The dilemma in printing thin shell structures is the balance between material efficiency and structural soundness. The use of more material will produce a more solid and sound structure but it takes more time and material to fabricate. On the contrary, a thinner structure is more economical but the object tends to be more fragile or deformable. Therefore the problem of finding an optimal supporting structure is crucial to save printing material consumption while maintaining mechanical performance. To solve the problem, some methods have been proposed such as inserting struts, local thickening, skin-frame structure, grid-shell structure and Voronoi structure. However, these techniques either use traditional finite elements or ignore shell contribution. The advantages of attaching stiffeners seamlessly to skin surface are: • Stiffeners can alleviate interior space compared to insertion of struts or Voronoi structure; • Stiffeners are less likely to cause stress concentration compared to skinframe structure which connects frame to skin at nodes; • The stiffened plate theory takes stiffeners and surface as a coupled system by adding stiffness contribution of stiffener elements to corresponding plate elements which yields more accurate solution; • The strength or ability to resist bending can be further improved by improving shape of stiffeners. Inspired by these good properties of stiffeners, the research presents three computational methods to automatically generate optimized layout and size of stiffeners: First, the research discretizes the input geometry into a triangular mesh. The faces and edges of the mesh are taken as traditional plate and beam elements respectively. The contributions from stiffeners (beams) are assembled to corresponding plate elements and the coupled system is solved to obtain initial stress field. An optimization framework is proposed to use the initial stress field to guide the layout of stiffeners to follow the flow of stress, and to optimize the shape of stiffeners once the layout is fixed. The disadvantage of this approach is that stiffeners need to align with edges and in order to fulfil this requirement the input mesh has to update iteratively during flow process. To maintain the geometry, the second approach is to formulate the stiffeners using same shape functions from plate elements. This method assembles stiffness contribution from any stiffener within a plate element without re-meshing the triangular mesh. An optimization framework to generate uniand bi-directional stiffeners using this stiffened element is proposed to reinforce thin shell structures. The intuitive way of distributing stiffeners equally in space is not the best way to create the supporting structure as it does not follow the stress field.
Detecting and Explaining Unfairness in Consumer Contracts with Memory Networks

—We present an approach based on Memory-Augmented Neural Networks for the task of detecting clauses in online Terms of Service that are potentially unfair for the consumer, with the additional goal to explain the legal rationale behind unfairness. The proposed approach goes in the direction of developing neural models for the legal domain, whose output is interpretable and explainable by humans.
Network Representation Learning-Enhanced Multisource Information Fusion Model for POI Recommendation in Smart City

With the advance of artificial intelligence and communication technology in the smart city, various location-based data of users can be collected via location-based social networks (LBSNs). How to make full use of these data for accurate point-of-interest (POI) recommendation is challenging because POI selection is influenced by various factors. In this article, we propose a network representation learning-enhanced multisource information (MSI) fusion model for POI recommendation in the context of LBSNs. The proposed model jointly considers various factors, including user preference, geographical influence, and social influence for a recommendation. Specifically, the social influence is modeled by performing network representation learning methods on the constructed co-visiting user networks so that the hidden complex social relationships among users can be measured automatically. Moreover, considering the significance of user preference and geographical influence, a fusion model is designed to jointly consider user preference, social influence, and geographical influence for POI recommendation. Our method is evaluated based on two publicly available data sets and extensive experimental results demonstrate that the proposed MSI fusion model outperforms several state-of-the-art algorithms for POI recommendation in terms of precision, recall, and F1.
Solution structure of the reduced active site of a starch-active polysaccharide monooxygenase from Neurospora crassa

X-ray absorption spectroscopy (XAS) was utilized to gain insights into the structure and electronic properties of the reduced copper active site in NCU08746, a polysaccharide monooxygenase (PMO) from Neurospora crassa that activates O2 to cleave glycosidic linkages in starch. The reaction of NCU08746 likely starts with binding of O2 to the copper(I) center. However, the solution structure of the reduced active site in NCU08746 has not been properly elucidated. In this study, we prepared Cu(I)-NCU08746 in solution, which was snap-frozen to preserve the solution structure of the copper(I) active site prior to XAS analysis. Results show that the copper(I) center in Cu(I)-NCU08746 exhibits a 4-coordinate geometry, which is different from the 3-coordinate geometry observed for some other PMOs. This difference likely arises from the coordination of the active site tyrosine residue and could contribute to the difference in activity between NCU08746 and other PMOs.
Nonlinear Active Disturbance Rejection Control of VGT-EGR System in Diesel Engines

In this paper, a nonlinear active disturbance rejection control (NLADRC) strategy based on nonlinear extended state observer (NLESO) is proposed to solve the unmodeled dynamics, coupling and disturbance due to change of working point in the variable geometry turbine (VGT) and exhaust gas recirculation (EGR) system, so as to achieve accurate control of intake manifold pressure and mass air flow in a diesel engine. To achieve decoupling, the double-input double-output (DIDO) VGT-EGR system is decomposed into two single-input single-output (SISO) subsystems, and each subsystem has a separate nonlinear active disturbance rejection controller. At the same time, the convergence proof of the designed NLESO is also given theoretically. Finally, the NLADRC controller is compared with linear active disturbance rejection controller and proportional–integral–derivative (PID) controller. Through simulation, it is indicated that the proposed NLADRC controller has better transient response performance, resistance to external disturbance and robustness to the change of engine operating point.
Meta Learning-Based MIMO Detectors: Design, Simulation, and Experimental Test

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximum a posteriori decoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.