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The predictive accuracy of regional hydrologic models often varies across both time and space. Interpreting relationships between watershed characteristics, hydrologic regimes, and model performance can reveal potential areas for model improvement. In this study, we use machine learning to assess model performance of a regional hydrologic model to forecast the occurrence of streamflow drought. We demonstrate our methodology using a regional long short‐term memory (LSTM) deep learning model developed by the U.S. Geological Survey (USGS) and data from 384 streamgages across the Colorado River Basin region. Performance was assessed by clustering catchments using: (a) physical and climatological catchment attributes, and (b) streamflow drought signatures time series. We examined the association of USGS LSTM model error measures with clusters generated by both approaches to interpret meaningful spatial and temporal information about LSTM model performance. Clustering static catchment attributes identified elevation, degree of streamflow regulation, baseflow contribution, catchment aridity, and drainage area as the most influential attributes to model performance. Clustering gages by their drought signatures revealed that catchments with significant seasonal peak runoff between January and June generally exhibited better model performance. Additionally, a Random Forest classifier was trained to successfully predict LSTM model performance (F1 score of 0.72) based on physical and climatological catchment attributes. Low degree of flow regulation was identified as a key indicator of better LSTM model performance. These findings point to the opportunities for improving the USGS LSTM model performance in future hydrologic drought prediction efforts across regional and CONUS scales.

Surveillance and security cameras help security forces in public places such as airports, railway stations, universities and office buildings to perform high-level surveillance tasks such as detecting suspicious activity or anticipating undesirable events. Re-Identification (Re-ID) is defined as the process of communicating between images of the person in different cameras in a surveillance environment. Changing the field of view of any camera presents challenges such as changing body posture, changing brightness, noise and blockage. This article focuses on extracting the most distinctive features to overcome these challenges. The features of Hu moment, Zernike moment in 9th order and Legendre moment in 9th order for each image are extracted and merged into a single feature vector to form a single feature vector for each image. Principal Component Analysis (PCA) was used to reduce the vector dimensionality and finally the Mahalanobis distance criterion was used for identification. The proposed method in the VIPeR database has achieved a re-ID rate of 96.5. Although the presented method is simple, the outcome has been superior compared to many of the state-of-the-art methods.

While microbial fuel cells are being considered as a tool for energy saving in wastewater treatment facilities, such applications in oil refineries pose a challenge due to harder acclimation of microorganisms. In this research, the effect of heat pretreating mixed culture microorganisms (MCM), and cell cross section, on the performance of a novel cell design with two cross sections (single chamber microbial fuel cells, with circular: SCMFC_CC and rectangular: SCMFC_RC cross section) fed batched with refinery wastewater were investigated. First, using original and heat pretreated MCM, the performance of SCMFC_CC in terms of chemical oxygen demand (COD) removal and electricity production was investigated. Then, using only the heat pretreated MCM, the electricity production of SCMFC_RC was measured and compared with that of SCMFC_CC. Heat pretreatment of MCM improved maximum open circuit voltage (OCV) and maximum power density generated by 14% and 16%, respectively. However, heat pretreatment reduced COD removal by about 4%. The performance of SCMFC_CC in terms of maximum OCV and power density compared to SCMFC_RC was improved by 41% and 279%, respectively. Heat treatment of MCM increases the electricity generation of the cell, while reducing the performance of COD reduction due to decreasing the microorganism varieties in the MCM.

It has long been established in theory that uncertainty impacts on firm behaviour. However, the empirical basis for quantifying the uncertainty-reducing effects of trade agreements has not been firmly established. In this paper, we develop estimates of the effect of reducing uncertainty regarding regulation of foreign services markets by making commitments that are bound under a trade agreement. Specifically, we identify the effect on services trade of services trade restrictions, as measured by the OECD's Services Trade Restrictiveness Index (STRI), and the separate effect of ‘water’ in binding commitments, as assessed by the difference between countries’ commitments under the General Agreement on Trade in Services (GATS) or free trade agreements (FTAs) and applied levels of market access, as captured by STRI scores. Using a gravity model, we find that services trade responds positively but inelastically to reductions in services trade barriers, as measured by the STRI and, in our preferred regression, the response to actual restrictions is more than twice – specifically 2.4 times – as strong as the response to comparable reductions in uncertainty, as measured by water. Moving from GATS commitments to FTA commitments leads to a 4.7% increase in services trade because of the reduction in uncertainty.

Controlling the arrangement and organization of self-assembled peptide nanostructures is a crucial step in developing Lab-on-a-Chip devices based on biomolecular assembly. Here, we report a simple approach to achieve the vertically aligned assembly of diphenylalanine (FF) peptide by casting stock solution of FF peptide on gold and graphite modified silicones. We show at the first time the formation of highly ordered interlaced arrays of vertical flower crystals and peptide nanotubes (PNTs) on thiolated gold and graphite. Furthermore, their chemical stability was investigated in PBS buffer after 3 h to gain insight into the stability of modified electrodes upon cycling. Interestingly, a highly ordered hierarchical morphology was obtained on the substrate surface. Hierarchical morphology resembles a square lattice, dendritic forest, and three-dimensional packed arrays. The results confirmed that PNTs not only preserves its chemical stability but transform into hierarchical arrays in PBS which is very beneficial for their applications in bioelectrochemical and nanoelectronics devices. As an example, the significantly enhanced arrangement of antibody CD3 was also demonstrated at the PNTs modified gold electrode compared to unordered modified electrodes. The simple and mild approach described here opens a new path for the fabrication of organized self-assembled peptide bionanostructure arrays allowing the fabrication of a variety of microarrays used in practical applications.

Cu(tpa)·(DMF) (Cu terephthalic acid Dimethyl formamide) MOF-5 and its hybrids have been successfully synthesized by hydrothermal method and have been used as a nano adsorbent for heavy metal removal from waste water. The present work focuses on the transient adsorption of Ni(II) by Cu(tpa)·(DMF)MOF-5 and its hybrid with different graphene concentrations. MOF-5 was synthesized by terephthalate ligand and copper cores. Adsorption experiments were accomplished in initial concentration of Ni(II) 300 ppm and 100 ml volume of solution, 25 mg of adsorbent and Room temperature. The XRD analysis of synthesized nano adsorbent (MOF-5 and all other hybrids) are compared to analyze the main factors and features. The results of scanning electron microscopy (SEM) of MOF-5 and MOF-5–Graphene hybrid 30% show that graphene layers behave as dividers and place between platelets of MOF-5Cu. Removal percentage of Ni(II) by various adsorbents, MOF-5Cu, 10% hybrid of MOF-5Cu–Graphene, 20% hybrid, 30% hybrid, 40% hybrid are approximately, 85, 86, 90, 96, 94%, respectively. Also, pseudo first and second order kinetic models studied to obtain the adsorption treatment of MOF-5Cu–Graphene hybrid 30% and it is found that the pseudo second order kinetic model is more reasonable for this adsorbent. Our results indicate that MOF-5Cu and its hybrid with graphene have great potential in removing Ni(II) ions from aqueous environment.

We assess the outcomes for the negotiating parties in the Trans-Pacific Partnership if the remaining eleven parties go ahead with the agreement as negotiated without the United States, as compared to the outcomes under the original twelve-member agreement signed in October 2016. We find that the eleven-party agreement, now renamed as the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), is a much smaller deal than the twelve-party one, but that some parties do better without the United States in the deal, in particular those in the Western Hemisphere - Canada, Mexico, Chile, and Peru. For the politically relevant medium term, the United States stands to be less well-off outside the TPP than inside. Since provisional deals can be in place for a long time, the results of this study suggest that the eleven parties are better off to implement the CPTPP, leaving aside the controversial governance elements, the implications of which for national interests are unclear and which, in any event, may be substantially affected by parallel bilateral negotiations between individual CPTPP parties and the United States.

Heavy metal pollutants in wastewater are a major environmental concern. In order to fabricate metal organic composite for adsorption of these pollutants, in a first step a pristine and several nitrogen doped graphene films were synthesized by chemical vapor deposition method. Preparation of graphene films was performed through a one-step co-growth of naphthalene and urea mixture as an inexpensive and easy technique to handle solid precursors. This was done over a copper catalyst at different growth temperatures. Different characterization methods including Raman spectroscopy, elemental analysis, and X-ray diffraction confirmed the quality of the pristine and doped graphene. This technique showed an increasing trend of the doping level (nitrogen concentration up to 5.1% overall) as the growth temperature decreased. Results showed that both nitrogen doping, and carrying the synthesis at higher temperatures increase the defects and wrinkles in the graphene. Furthermore, doping introduced a light shift in defect types from vacancy in pristine graphene to boundary type in nitrogen-doped samples, which are favorable for functionalization for environmental applications.

Artificial intelligence (AI) represents technologies with human‐like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site‐specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well‐being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers’ trust in those providing AI solutions for PA. We highlight that farmers’ trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers’ trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human‐machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers. Core Ideas Model transparency and explainability can help foster trust between farmers and those providing artificial intelligence (AI) solutions. Assigning clear responsibility and accountability to AI decisions can improve farmers’ acceptance and use of these technologies. Development of fair and equitable AI can improve human‐machine partnerships in agriculture. Regulation or voluntary compliance with data ownership, privacy, and security is needed if AI systems are to be used by farmers.

Precision agriculture (PA) has been defined as a “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” This definition suggests that because PA should simultaneously increase food production and reduce the environmental footprint, the barriers to adoption of PA should be explored. These barriers include (1) the financial constraints associated with adopting decision support system (DSS); (2) the hesitancy of farmers to change from their trusted advisor to a computer program that often behaves as a black box; (3) questions about data ownership and privacy; and (4) the lack of a trained workforce to provide the necessary training to implement DSSs on individual farms. This paper also discusses the lessons learned from successful and unsuccessful efforts to implement DSSs, the importance of communication with end users during DSS development, and potential career opportunities that DSSs are creating in PA. Core Ideas Decision support systems (DSSs) are one component of precision agriculture (PA). The accuracy of DSSs may be improved by using algorithms based on machine learning. Barriers to DSSs include financial constraints, hesitancy to change, data privacy, and workforce limitations. Professional opportunities exist to overcome DSS adoption barriers.