Measuring Sustainable Intensification Using Satellite Remote Sensing Data

Farm-level sustainable intensification metrics are needed to evaluate farm performance and support policy-making processes aimed at enhancing sustainable production. Farm-level sustainable intensification metrics require environmental impacts associated with agricultural production to be accounted for. However, it is common that such indicators are not available. We show how satellite-based remote sensing information can be used in combination with farm efficiency analysis to obtain a sustainable intensification (SI) indicator, which can serve as a sustainability benchmarking tool for farmers and policy makers. We obtained an SI indicator for 114 maize farms in Yangxin County, located in the Shandong Province in China, by combining information on maize output and inputs with satellite information on the leaf area index (from which a nitrogen environmental damage indicator is derived) into a farm technical efficiency analysis using a stochastic frontier approach. We compare farm-level efficiency scores between models that incorporate environmental damage indicators based on satellite-based remote sensing information and models that do not account for environmental impact. The results demonstrate that (a) satellite-based information can be used to account for environmental impacts associated with agriculture production and (b) how the environmental impact metrics derived from satellite-based information combined with farm efficiency analysis can be used to obtain a farm-level sustainable intensification indicator. The approach can be used to obtain tools for farmers and policy makers aiming at improving SI

TF-FusNet: A Novel Framework for Parkinson’s Disease Detection via Time-Frequency Domain Fusion

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Modeling and Analyzing Logic Vulnerabilities of E-Commerce Systems at the Design Phase

E-commerce systems have become tremendously popular and important for modern business processes in the world of the digital economy. E-commerce business processes rely on the distributed and concurrent interaction process among Web applications of participants, such as clients, merchants, third-party payment platforms (TPPs), and bank systems. Such complex business interactions bridge the gap of trustiness among participants and introduce new security challenges in the form of logical vulnerabilities, which are prevalent in the business process at the application level. The most pressing challenge is to guarantee security throughout the checkout process at the conceptual design phase such that the logic errors can be detected before the actual implementation. Maintenance and repair of implemented e-commerce systems can be extremely costly. To this end, this article proposes a novel modeling and analyzing methodology for multiparticipants and multisessions e-commerce interaction processes based on colored Petri nets (CPNs). First, we define a novel model that can efficiently depict the key properties of e-commerce business interaction processes. Second, several modeling principles are formulated based on the design specification of e-commerce systems. Finally, the concept of Transaction-Logical Consistency is defined to analyze and verify the logical vulnerabilities of e-commerce systems. Through a discussed case study, we demonstrate the feasibility and applicability of the proposed methodology and its efficiency in detecting problems those can potentially lead to logical vulnerabilities

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.</p

May Measurement Month 2017: an analysis of blood pressure screening results worldwide

Background: Increased blood pressure is the biggest contributor to the global burden of disease and mortality. Data suggest that less than half of the population with hypertension is aware of it. May Measurement Month was initiated to raise awareness of the importance of blood pressure and as a pragmatic interim solution to the shortfall in screening programmes. Methods: This cross-sectional survey included volunteer adults (≥18 years) who ideally had not had their blood pressures measured in the past year. Each participant had their blood pressure measured three times and received a a questionnaire about demographic, lifestyle, and environmental factors. The primary objective was to raise awareness of blood pressure, measured by number of countries involved, number of people screened, and number of people who have untreated or inadequately treated hypertension (defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or both, or on the basis of receiving antihypertensive medication). Multiple imputation was used to estimate the mean of the second and third blood pressure readings if these were not recorded. Measures of association were analysed using linear mixed models. Findings: Data were collected from 1 201 570 individuals in 80 countries. After imputation, of the 1 128 635 individuals for whom a mean of the second and third readings was available, 393 924 (34·9%) individuals had hypertension. 153 905 (17·3%) of 888 616 individuals who were not receiving antihypertensive treatment were hypertensive, and 105 456 (46·3%) of the 227 721 individuals receiving treatment did not have controlled blood pressure. Significant differences in adjusted blood pressures and hypertension prevalence were apparent between regions. Adjusted blood pressure was higher in association with antihypertensive medication, diabetes, cerebrovascular disease, smoking, and alcohol consumption. Blood pressure was higher when measured on the right arm than on the left arm, and blood pressure was highest on Saturdays. Interpretation: Inexpensive global screening of blood pressure is achievable using volunteers and convenience sampling. Pending the set-up of systematic surveillance systems worldwide, MMM will be repeated annually to raise awareness of blood pressure. Funding: International Society of Hypertension, Centers for Disease Control and Prevention, Servier Pharmaceutical Co