Extant Methods Research Articles

Measuring and mapping displacement: The problem of quantification in the battle against gentrification

Debates concerning residential population displacement in the context of gentrification remain vociferous, but are hampered by a lack of empirical evidence of the extent of the displacement occurring. The lack of quantitative evidence on gentrification-induced displacement and the difficulties in collecting it has long hampered the fight against it. Based on a systematic review of quantitative studies of the displacement associated with gentrification, this article considers how researchers have attempted to measure displacement using a range of statistical and mapping techniques reflecting the multi-dimensional character of gentrification. We note that these techniques often struggle to provide meaningful estimates of the number of individuals and households displaced by gentrification, something compounded by the lack of data available on a sufficiently granular temporal and spatial scale. Noting the limitations of extant methods, we conclude by considering the potential of more novel data sources and emergent methods involving the processing of larger amounts of (micro)data, as well as participatory GIS methods that involve affected communities themselves. This implies that whilst the quantitative study of displacement remains difficult, patterns and processes of displacement can be inferred through existing data sources, as well as data generated from those who themselves have experienced displacement.

Fully-sensitive seed finding in sequence graphs using a hybrid index.

MotivationSequence graphs are versatile data structures that are, for instance, able to represent the genetic variation found in a population and to facilitate genome assembly. Read mapping to sequence graphs constitutes an important step for many applications and is usually done by first finding exact seed matches, which are then extended by alignment. Existing methods for finding seed hits prune the graph in complex regions, leading to a loss of information especially in highly polymorphic regions of the genome. While such complex graph structures can indeed lead to a combinatorial explosion of possible alleles, the query set of reads from a diploid individual realizes only two alleles per locus—a property that is not exploited by extant methods.ResultsWe present the Pan-genome Seed Index (PSI), a fully-sensitive hybrid method for seed finding, which takes full advantage of this property by combining an index over selected paths in the graph with an index over the query reads. This enables PSI to find all seeds while eliminating the need to prune the graph. We demonstrate its performance with different parameter settings on both simulated data and on a whole human genome graph constructed from variants in the 1000 Genome Project dataset. On this graph, PSI outperforms GCSA2 in terms of index size, query time and sensitivity.Availability and implementationThe C++ implementation is publicly available at: https://github.com/cartoonist/psi.

Research on Virtual Synchronous Generator Control for Vehicle-to-Grid System

This paper investigates the concept of Virtual Synchronous Generator (VSG) and analyses the working principle of common VSG control technology. With the promotion of Vehicle-to-Grid(V2G) technology, large-scale power electronics devices will be connected to the grid. Consequently, the stability of the grid will be influenced by the lack of mechanical inertia and system damping within these power electronic devices. The extant normal control methods are difficult to cope with this issue. Accordingly, a VSG control technology is proposed by this study, which can be applied to V2G system. Moreover, the mathematical analysis of this control method is also suggested in this research. This control method provides mechanical inertia and system damping, which is similar to synchronous generators. It also can achieve the accurate control of power. The original P/Q-U-I three-loop control is simplified to P/Q-I dual-loop control in the condition of not losing the inertia of the converter. Ultimately, the simulation examines the accuracy and effectiveness of the control method.

Coordinating Marketing and Production with Asymmetric Costs: Theory and Estimation

This paper proposes a theoretical framework for decision making when the firm needs to make marketing and production/order decisions simultaneously before demand uncertainty is resolved. We discuss the theoretical properties of the framework for single and multi-product firms; in addition, we show that the framework can be extended to allow for competitive reaction in a duopoly setting. We propose an empirical method to operationalize the model and compare the results to those from extant methods. The empirical results for both single and multi-product firms show that the proposed method outperforms decision making using standard econometric methods. In particular, depending on customer lifetime value (CLV) and other error costs and price elasticities, the loss in potential profits by using the standard regression-based methodology or quantile regression can be considerable.

Multistage decision support framework for sites selection of solar power plants with probabilistic linguistic information

Energy consumption is constantly improving due to the increasing development of community economy and material living standards. Solar energy is the first green energy, and its unique advantage is of great importance in solving the energy crisis and environmental degradation. Furthermore, selecting appropriate locations is necessary to take full advantages of solar energy. This study intends to set up a multistage decision support framework for sites selection of solar power plants with probabilistic linguistic (PL) information. The proposed framework considers not only the disposal of linguistic information but also the interrelationships among criteria. A probabilistic linguistic term set (PLTS) is used to deal with a large volume of linguistic evaluation information. Then, probabilistic linguistic normal cloud (PLNC) is proposed to transform qualitative concepts into quantitative values so as to handle PLTS effectively and reduce information loss and distortion. A new distance measure that connects a Heronian mean (HM) operator with distance measure is also presented. An improved maximizing deviation method is introduced on the basis of this new model to deal with the interrelationships among criteria. In addition, a ranking method VlseKriterijumska optimizacija I Kompromisno (VIKOR) which considers the maximum utility of a group and the minimum regret value of individuals is proposed. Next, the proposed framework is successfully implemented in a case study. Results show that Gansu and Xinjiang are more suitable locations for building solar power plants. Sensitivity analysis is carried out to explore the change in ranking order due to variations in parameters. The proposed assessment model is then compared with the extant methods to deal with PL information and thereby certify its effectiveness, feasibility and advantages. Ultimately, the policy recommendations and contributions of this article are laid out.

Imputing race and ethnic information in administrative health data.

To improve on existing methods to infer race/ethnicity in health care data through an analysis of birth records from Connecticut. A total of 162467 Connecticut birth records from 2009 to 2013. We developed a logistic model to predict race/ethnicity using data from US Census and patient-level information. Model performance was tested and compared to previous studies. Five performance measures were used for comparison. Our full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race. Predictive models using Census information and patients' demographic characteristics can be used to accurately populate race/ethnicity information in health care databases, enhancing opportunities to investigate and address disparities in access to, utilization of, and outcomes of care.

Escherichia coli vectors having stringently repressible replication origins allow a streamlining of Crispr/Cas9 gene editing

Readily curable plasmids facilitate the construction of plasmid-free bacterial strains after the plasmid encoded genes are no longer needed. The most popular of these plasmids features a temperature-sensitive (Ts) pSC101 origin of replication which can readily revert during usage and cannot be used to construct Ts mutations in essential genes. Plasmid pAM34 which contains an IPTG-dependent origin of replication largely overcomes this issue but is limited by carrying the most commonly utilized antibiotic selection and replication origin. This study describes the construction of an expanded series of plasmid vectors having replication origins of p15a, RSF1030 or RSF1031 that like pAM34 have IPTG-dependent replication. Surprisingly, these plasmids can be cured in fewer generations than pAM34. Derivatives of pAM34 with alternative antibiotic selection markers were also constructed. The utility of these vectors is demonstrated in the construction of a CRISPR-Cas9 system consisting of an IPTG-dependent Cas9 plasmid and a curable guide RNA plasmid having a streptomycin counterselection marker. This system was successfully demonstrated by construction of point mutations, deletions and insertions in the E. coli genome with a very high efficiency and in a shorter timescale than extant methods. The plasmids themselves were readily cured either together or singly from the resultant strains with minimal effort.

Image contrast expand enhancement system based on fuzzy theory

In the current age of information explosion, the extraction of relevant data from a large information pool, which includes images, has become crucial. Because of the easy availability of imaging devices, millions of images are being added to the image pool every day. Current image contrast enhancement methods have some drawbacks. First, for images captured in extreme lighting conditions, such as backlit images and extremely bright or dark images, extant image contrast enhancement methods cannot retain the brightness distribution details of the original image, resulting in image distortion. Second, the methods can only achieve global contrast enhancement but not local contrast enhancement. Third, these methods cannot satisfy the Human Visual System mapping curve, resulting in nonsmooth or distorted images. In this study, a novel image expand enhancement system based on fuzzy theory is proposed. This system has two major features: (1) the expand model is designed such that the correlations between light intensity and the intensity of an image captured in this light are considered to achieve local contrast enhancement, and (2) an extreme case of images processing step that is capable of enhancing. The proposed model has a fusion framework; therefore, the image fusion model can represent the relationship between the original image and the expanded image. Next, the fusion model describes some expected statistical properties of the desired intensity of the expand image. Thus, the details of the original image are retained. Experimental results revealed that the proposed algorithm is capable of adaptively enhancing the contrast of the original image while simultaneously extruding the details of objects in the image. The resulting enhanced image can be effectively used for image information analysis and other image-processing tasks.

Computing exact score vectors for linear Gaussian state space models

A recursive formula for computing the exact value of score vectors is proposed for a general form of the linear Gaussian state space model, which is more desirable than approximate values in some statistical analyses. Unlike most extant methods, our formula calculates all components of the score vector simultaneously. This approach significantly simplifies its programing, in particular, with some matrix-oriented programing languages, such as MATLAB. We also consider a way of handling initial conditions that depend on unknown parameters. This issue has not yet been explicitly addressed in the existing literature in the context of exact score computing for a general case, such as the one that we consider in this paper. It is also shown that our formula is especially useful for calculating score tests with an outer product of gradient asymptotic covariance matrix estimator.

Artificial neural network based wavelet transform technique for image quality enhancement

Image quality enhancement is an important image processing task, wherein denoising an image is essential for accurate image diagnoses, because the presence of noise in an image produces incorrect information. This paper proposes double density wavelet transform based intelligent techniques for image denoising. This hybrid technique combines wavelet and neural networking methods, performed and validated using different standard images and extant denoising methods. These images were degraded using a variety of noise levels to simulate actual noise degradation. The results reveal the high-peak signal-to-noise ratio of the proposed system, which is thus considered effective compared to state-of-the-art techniques.

A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm

With the continuous increase of grid-connected photovoltaic (PV), high-precision PV power prediction is increasingly important. Extant deterministic forecasting methods do not facilitate fully effective dispatching decisions or power grid risk analysis. Furthermore, the single model also has insufficient generalization ability and unstable forecasting performance in PV power forecasting. This paper proposes an alternative multi-model PV power interval forecasting method which takes into account the seasonal distribution of power fluctuation characteristics. PV output power, absolute power deviation, and relative variation rate are first analyzed for the seasonal distribution characteristics of PV output as they fluctuate over time. Seasonal multi-models for deterministic forecasting of PV power are then built based on an extreme learning machine (ELM). Deterministic forecasting error is fitted by kernel density estimation to complete the PV power interval forecast. The effectiveness of the proposed method is validated by comparison against other methods.

Discriminative multi-source adaptation multi-feature co-regression for visual classification

Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in machine learning. While the advantages of multi-source adaptation have been widely recognized, there still exit three major limitations in extant methods. Firstly, how to effectively select the discriminative sources is yet an unresolved issue. Secondly, multiple different visual features on hand cannot be effectively exploited to represent a target object for boosting the adaptation performance. Last but not least, they mainly focus on either visual understanding or feature learning independently, which may lead to the so-called semantic gap between the low-level features and the high-level semantics. To overcome these defects, we propose a novel Multi-source Adaptation Multi-Feature (MAMF) co-regression framework by jointly exploring multi-feature co-regression, multiple latent spaces learning, and discriminative sources selection. Concretely, MAMF conducts the multi-feature representation co-regression with feature learning by simultaneously uncovering multiple optimal latent spaces and taking into account correlations among multiple feature representations. Moreover, to discriminatively leverage multi-source knowledge for each target feature representation, MAMF automatically selects the discriminative source models trained on source datasets by formulating it as a row-sparsity pursuit problem. Different from the state-of-the-arts, our method is able to adapt knowledge from multiple sources even if the features of each source and the target are partially different but overlapping. Experimental results on three challenging visual domain adaptation tasks consistently demonstrate the superiority of our method in comparison with the related state-of-the-arts.

Cesarean Section or Natural Childbirth? Cesarean Birth May Damage Your Health.

The increasing popularity of Cesarean birth has become a social concern in many countries. This paper reviews the literature on the effects of Cesarean section on children’s psychological health. The results show that Cesarean birth may have adverse effects on children’s sensory perception, sensory integration ability, neuropsychiatric development, and the infant-mother relationship. However, there remain deficiencies in extant research methods, research content, subject groupings, and interpretation of research results. Future research should improve research methods, broaden the research content, and refine the grouping of children born by Cesarean section. The exploration of neural mechanisms is also needed, as well as research directed toward suggesting effective interventions to reduce unnecessary Cesarean sections.

Enantioselective Phenolic α-Oxidation Using H2O2 via an Unusual Double Dearomatization Mechanism.

Feedstock aromatic compounds are compelling low-cost starting points from which molecular complexity can be generated rapidly via oxidative dearomatization. Oxidative dearomatizations commonly rely heavily on hypervalent iodine or heavy metals to provide the requisite thermodynamic driving force for overcoming aromatic stabilization energy. This article describes oxidative dearomatizations of 2-(hydroxymethyl)phenols via their derived bis(dichloroacetates) using hydrogen peroxide as a mild oxidant that intercepts a transient quinone methide. A stereochemical study revealed that the reaction proceeds by a new mechanism relative to other phenol dearomatizations and is complementary to extant methods that rely on hypervalent iodine. Using a new chiral phase-transfer catalyst, the first asymmetric syntheses of 1-oxaspiro[2.5]octa-5,7-dien-4-ones were reported. The synthetic utility of the derived 1-oxaspiro[2.5]octadienones products is demonstrated in a downstream complexity-generating transformation.

Bayesian Synthetic Control Methods

The authors propose a new Bayesian synthetic control framework to overcome limitations of extant synthetic control methods (SCMs). The proposed Bayesian synthetic control methods (BSCMs) do not imp...

Priorities in applied research to ensure programmatic success in the global elimination of canine rabies

The elimination of human rabies mediated by dogs is attainable in concept, based upon current sensitive and specific diagnostic methods, existing safe and effective human and veterinary vaccines and a sound virological, pathological and epidemiological understanding of the disease. Globally, all developed countries achieved this goal. Regionally, major progress occurred throughout the Americas. However, less advancement is evident in Africa and Asia. Our objective was to concentrate upon those salient improvements to extant tools and methods over the next five years which could assist and simplify the task for both those developing countries that have already begun the process, as well as other localities in the earlier stages of consideration. We considered several categories of applied research which could be accomplished in the short term, based upon the available scientific evidence and recent recommendations from subject matter experts and key opinion leaders, focused upon perceived major limitations to prior program success. Areas of concentration included: laboratory-based surveillance, pathogen detection and characterization; human rabies prophylaxis; veterinary biologics; implementation of canine vaccination; and oral vaccination of free-ranging community dogs. Further real-time application in these core areas with proven techniques and technology would simplify attaining not only the global goal focused subtly upon human mortality, but the actual elimination of canine rabies as well.

QuEChERS Pretreatment Combined with Ultra-performance Liquid Chromatography–Tandem Mass Spectrometry for the Determination of Four Veterinary Drug Residues in Marine Products

The extant methods for detecting multi-residues in aquatic products can avoid the disadvantages of complex pretreatment, long time consumption, single detection type, and low sensitivity. Therefore, developing a method that enables a multi-residue detection of veterinary drugs is very important. QuEChERS is a simple, rapid, and efficient method for detecting multi-drug residues. In this work, QuEChERS pretreatment combined with ultra-performance liquid chromatography–tandem mass spectrometry was proposed to identify sulfamide, tetracycline, quinolone, and malachite green residues in oyster, scallop, squid, and cuttlefish. The sample was extracted by using acetonitrile solution containing 0.1% acetic acid, while the extract was purified by using the QuEChERS method. The target analytes were separated on an ACQUITY UPLC BEH chromatographic column by using acetonitrile solution as a mobile phase for the gradient elution. Quantification was performed by using the internal standard method. The recovery of the 21 tested compounds was within the range of 65.26 to 113.5% and the relative standard deviation was less than 6.78%, thereby proving the good repeatability of the method. The intra- and inter-day precisions were less than 6.26% and 6.02%, respectively, while the limits of detection and quantification were less than 0.8 μg/kg and 2.5 μg/kg, respectively. The proposed method was then applied to detect residues in 150 samples of oysters, scallops, squid, and cuttlefish obtained from eight regions, including Shenzhen, Dongguan, Zhaoqing, Shantou, Maoming, Jiangmen, Shanwei, and Huizhou in Guangdong Province. No residues were detected in any of these samples. In sum, the proposed detection method has good reproducibility, high sensitivity, and reliability.

New model of the storage location assignment problem considering demand correlation pattern

Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP). In existing studies, most storage assignment methods only consider the properties of individual item rather than the item groups that are usually collectively required. This paper introduces the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the SLAP. The model is subsequently reduced using the S-shape routing strategy, and a method for determining DCPs from historical data is proposed. To solve the model, a heuristic and a simulated annealing method are developed. The proposed methods are examined and compared extant methods using both real data collected from an online retailer and numerical instances that are randomly generated. The computational results are discussed.

How good are we at detecting deception? A review of current techniques and theories

The task of discerning truth from untruth has long interested psychologists; however, methods for doing so accurately remain elusive. In this article, we provide an overview and evaluation of methods of detecting deception used in the laboratory and the field. We identify and discuss three broad approaches to detecting deception: measurement of non-verbal behaviour, verbal interview methods, and statement evaluation by humans and computers. Part of the problem in devising good methods for detecting deception is the absence of a sound understanding of deception in human lives. We thus consider three theories of deception – leakage, reality monitoring, and truth-default – and conclude that although promising, they do not yet provide an adequate foundation. We review 10 extant methods of detecting deception in the second part of the article, focusing at greatest length on the most widely used method in South Africa, the polygraph test of deception. Our conclusion is that non-verbal methods that work by inducing anxiety in interviewees are fundamentally flawed, and that we ought to move away from such methods. Alternate methods of detecting deception, including statement analysis, are considered, but ultimately our view is that there are currently no methods sufficiently accurate for practitioners to rely on. We suspect that a precondition for developing such measures is a coherent and validated theory.

Reliability Impacts of the Dynamic Thermal Rating System on Smart Grids Considering Wireless Communications

The dynamic thermal rating (DTR) system is one of most widely used state-of-the-art of smart grids owing to its proven ability to improve the existing line ratings at a fraction of the cost of most extant methods without infringing line safety requirements. However, the successful implementation of this system depends on the reliability of communication networks, especially wireless communication systems, in transmitting line rating information back to the utility company so that an appropriate line flow control can be executed. Therefore, the efficient, continuous, and secure operations of smart grids are equally affected by the reliability of the DTR and wireless communication systems. Despite this evidence, the reliability impacts of these systems on power grids are yet to be examined in a single framework. In this paper, a new model for investigating the reliability impacts of the DTR system on power grids considering the wireless communication network is proposed. The inability of the DTR system to perform, either due to its unavailability or communication failure, is taken as a function of line ratings that affect the transmission capacity and reliability of power grids. When the DTR system service is unavailable, the line rating is either reverted to the static thermal rating or estimated via the multi-linear regression (MLR) model based on information from neighboring regions. The comparison of these two models on the IEEE 24-bus reliability test system reveals that the MLR model is more beneficial in mitigating DTR and communication systems failures. This paper also explores various communication system failure scenarios and the reliability impacts of redundancy designs. The findings reveal that the communication system with moderate reliability levels yield the most benefits when the redundancy number increases from 1 to 4. Overall, the communication system significantly affects the availability of the DTR system.