Desired Mapping Research Articles

HIV Care Among Elderly Population: Systematic Review and Meta-Analysis.

Elderly people living with HIV are increasing. At present in the United States, nearly half of newly diagnosed HIV-infected people are aged >50 years. Diagnosis and treatment of HIV-infected elderly patients tends to be delayed by several health care factors as several life-threatening diseases are common in elderly people. This study aimed to find the pooled HIV prevalence in elderly population and the present situation of continuum care for the elderly HIV patients through systematic review and meta-analysis. All previously published articles from 2000 to 2018 are retrieved using MEDLINE, PUBMED, Cochrane Library, EMBASE, and Google Scholar. DerSimonian and Laird Random Effects model are used to critically appraise articles. STATA 13.0 is used to perform the meta-analysis and quantum-geographic information system (Q-GIS) is used to prepare desired map. I2 statistics has been used to test heterogeneity and publication biases. Results have been presented using forest plots. A total of 28 studies are included in this meta-analysis. Present analysis revealed pooled prevalence of HIV in elderly population as 15.79% with a lower rate of viral suppression as 11.524% (95% confidence interval, CI: 11.199-11.855), where a moderate number 38.643% (95% CI: 38.289-38.997) of elderly patients received antiretroviral therapy (ART) globally. The ART retention rate was 12.769% (95% CI: 12.540-13.001) with 6.15% (95% CI: 6.089-6.212) mortality. Despite successful administration of ART in developing part of the world that have relatively higher retention rates among HIV-infected elderly patients only a small percentage are virally suppressed, largely due to elderly drugs interact with ART and several comorbidities reduce the life span of the elderly people.

Existence of a time periodic solution for the compressible Euler equation with a time periodic outer force

We are concerned with a time periodic supersonic flow through a bounded interval. This motion is described by the compressible Euler equation with a time periodic outer force. Our goal in this paper is to prove the existence of a time periodic solution. Although this is a fundamental problem for other equations, it has not been received much attention for the system of conservation laws until now.When we prove the existence of the time periodic solution, we face with two problems. One is to prove that initial data and the corresponding solutions after one period are contained in the same bounded set. To overcome this, we employ the generalized invariant region, which depends on the space variables. This enable us to investigate the behavior of solutions in detail. Second is to construct a continuous map. We apply a fixed point theorem to the map from initial data to solutions after one period. Then, the map needs to be continuous. To construct this, we introduce the modified Lax–Friedrichs scheme, which has a recurrence formula consisting of discretized approximate solutions. The formula yields the desired map. Moreover, the invariant region grantees that it maps a compact convex set to itself. In virtue of the fixed point theorem, we can prove a existence of a fixed point, which represents a time periodic solution. Finally, we apply the compensated compactness framework to prove the convergence of our approximate solutions.

All-Optical Frequency Processor for Networking Applications

We propose an electro-optic approach for transparent optical networking, in which frequency channels are actively transformed into any desired mapping in a wavelength-multiplexed environment. Based on electro-optic phase modulators and Fourier-transform pulse shapers, our all-optical frequency processor (AFP) is examined numerically for the specific operations of frequency channel hopping and broadcasting, and found capable of implementing these transformations with favorable component requirements. Extending our analysis via a mutual-information--based metric for system optimization, we show how to optimize transformation performance under limited resources in a classical context, contrasting the results with those found using metrics motivated by quantum information, such as fidelity and success probability. Given its compatibility with on-chip implementation, as well as elimination of optical-to-electrical conversion in frequency channel switching, the AFP looks to offer valuable potential in silicon photonic network design.

In-situ recommendation of alternative soil samples during field sampling based on environmental similarity

Field sampling is an essential step for digital soil mapping and various sampling strategies have been designed for achieving desirable mapping results. Some unpredictable complex circumstances in the field, however, often prevent some samples from being collected based on the pre-designed sampling strategies. Such circumstances include inaccessibility of some locations, change of land surface types, and heavily-disturbed soil at some locations, among others. This may result in the missing of some essential samples, which could impact the quality of digital soil mapping. Previous studies have attempted to design alternative samples for the selected ones beforehand to address this issue. It cannot solve the problem completely as those pre-designed alternative samples could also be inaccessible. In this paper, we propose a dynamic method to recommend alternative samples for those unavailable samples in the field. The identification of alternative samples is based on the environmental similarity between an unavailable soil sample and its alternative candidates, as well as the spatial accessibility of these candidates. For the convenience of fieldwork, the proposed method was implemented to be a mobile application on the Android platform. A simulated soil sampling study in Xuancheng county, Anhui province of China was used to evaluate its performance. From a sample set for the study are, 30 samples were assumed to be inaccessible. For each of them, an alternative soil sample from the set was recommended using the proposed method. A deviation analysis of silt and sand content at the depth of 20 ~ 40 cm between the soil samples and their alternatives shows that the deviation on silt content is less than 20% for half of the soil samples. A larger deviation on sand content might be attribute to the limited alternative candidates in this virtual experiment. In a second experiment, we randomly selected a number of existing soil samples and replaced them with their corresponding alternative soil samples. This created 1000 hybrid sample sets. Each hybrid sample set was then used for digital soil mapping with iPSM. An evaluation using 59 independent soil samples indicated that the RMSE and MAE with the hybrid sample sets were close to that with the original sample set. The proposed method proved to be able to recommend effective alternative samples for those unavailable samples in the field.

A hybrid heuristic optimization of maintenance routing and scheduling for offshore wind farms

As the operation and maintenance (O&M) costs constitute a substantial portion of the overall life-cycle cost of offshore wind farms, routing, and scheduling of maintenance are very important for cost reduction. With the multi-type of vessels, multi-period, multi-base of O&M, multi-wind farm and uncertain weather conditions, the optimization of O&M cost is more challenging. In this article, a hybrid heuristic optimization of maintenance routing and scheduling for offshore wind farms is proposed. First, with the maintenance service protocol, mixed particle swarm optimization (MPSO) is applied to seek a desired mapping relation between vessels and wind farms. Utilizing the formalized rules, an optimal vessel allocation scheme is explored in the large solution space by individual crossover, swarm crossover and mutation. Then, with the scheme of vessel allocation, a discrete wolf pack search (DWPS) is introduced to optimize the maintenance route under all constraints. As the evaluation standard of MPSO, the purpose of DWPS is to search the solution space with depth and breadth balanced and find the optimal and open maintenance route with multiple round trips to bases that minimize O&M costs, including travel, technician and penalty costs. Finally, computational experiments and analysis are carried out. The results provide both the optimized cost and detailed arrangements, which can be directly used in the maintenance schedule.

Synthesis of a Three-Mirror System with Specified Shape of the Main Mirror Using Phase Front and Mapping Law

Ray-optic approximation is used to derive an analytical solution to the problem of synthesis of mirrors that form an axisymmetric three-mirror system with predetermined main mirror. Such a system provides transformation a spherical front of a source into an arbitrary front with the desired mapping law. The generatrix of an auxiliary mirror is represented as a polynomial in terms of even powers of angle. The solution can be used to exactly implement the output front and obtain the desired error of mapping law. The synthesis of a three-mirror telescope in which either parabolic or spherical mirror serves as the main mirror is considered.

Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from the inexperienced user unfriendliness, high model bias, and low effectiveness-to-efficiency ratio. The three challenges result from the explicitness characteristic (i.e. explicit formulation of the reconstruction model) in these techniques. For these challenges, we are the first to develop an implicit strain reconstruction framework by a deep neural network architecture. However, the classic neural network methods are unsuitable to the strain reconstruction task because they are difficult to impose any direct influence on the intermediate state of the learning process. This may lead the map learned by the neural network to be biased with the desired map. In order to correct the intermediate state of the learning process, our framework proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network. It provides the causal privileged information besides the training examples to help the network learning, while makes these privileged information unavailable at the test stage. This improvement can narrow the search region of the map learned by the network, and thus prompts the network to evolve towards the actual ultrasound elastography process. Moreover, in order to ensure the causality in LUPI, our framework proposes a physically-based data generation strategy to produce the triplets of privileged information, training examples and labels. This data generation process can approximately describes the actual ultrasound elastography process by the numerical simulation based on the tissue biomechanics and ultrasound physics. It thus can build the causal relationship between the privileged information and training examples/labels. It can also address the medical data insufficiency problem. The performance of our framework has been validated on 100 simulation data, 42 phantom data and 4 real clinical data by comparing with the ground truth performed by an ultrasound simulation system and four state-of-the-art methods. The experimental results show that our framework is well agreed (average bias is 0.065 for strain reconstruction) with the ground truth, as well as superior to these state-of-the-art methods. These results can demonstrate the effectiveness of our framework in the strain reconstruction.

The Swiss joint information platform for natural hazards

Abstract. The Swiss Joint Information Platform for Natural Hazards (GIN) has been realized from 2008 to 2010 as part of the Swiss federal government’s OWARNA project, which aimed at optimizing warning and alerting procedures against natural hazard. The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic, graphic and other multimedia products to professional users – before, during and after natural hazard events. In Switzerland water-, weather-, snow- and earthquake-related hazards are the most relevant ones.In 2013, an online survey showed that the platform does not fully meet user expectations, particularly as to user experience and usability of its cartographic, web-based user interface. Revaluation and redesign of the overall platform were necessary in order to improve map legibility, caused by the complexity of data, large data amounts, and high spatial density of online, real-time measurement data locations. A new web design and user interaction concept have been developed in 2014 and eventually put online in June 2017. User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process. The GIN platform now features important novel technical and graphical elements: The starting page is based on a dashboard containing virtual dossiers (Fig. 1), with which users configure their desired information, data, and map bundles individually, or use predefined adaptable views on various existing data sets. In addition, there is a new overall spatial search function to query data parameters. A responsive approach further improves the usability of the platform. The focus of these new features is on multi-views involving maps, diagrams, tables, text products, as well as selected geographical areas on maps, and fast data queries (Fig. 2). Current user feedback suggests that the new GIN platform design is well received, and that it is moving closer to its very goal: online monitoring and management of natural hazard events by enhanced usability, more targeted and higher personalization.Several Swiss Cantons (i.e., the political entities in Switzerland below the federation) actively participated, and still participate, in the conceptual GIN platform development process through advisory board meetings and consultations. On the operational level, Cantons actively provide and contribute further natural hazard information and measurement data from their own natural hazard monitoring networks. These additional Cantonal regional-scale data sets help to fill spatial data gaps, where no Federal data is available. GIN thusly integrates natural hazard data from Federal and Cantonal levels (and partly even private level), which adds value to all stakeholders on various political levels involved in natural hazard management (Federal, Cantonal, Regional, Communal crisis committees). Stakeholders not only use GIN’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks, but also benefit from seamless, secure and reliable IT-services, provided by the Swiss Federal Government. With the new GIN platform, Switzerland has a powerful, integrative, and comprehensive tool for monitoring and responding to natural hazard events.

An Iterative Feedback-Based Change Detection Algorithm for Flood Mapping in SAR Images

This letter proposes a novel algorithm for the unsupervised detection of flood mapping in synthetic aperture radar (SAR) images. In the literature, unsupervised change detection of SAR images mainly consists of two steps, i.e., first generating a difference image from two given images and then binarizing the difference image to produce the desired change map. Conventional change detection algorithms usually execute these two steps sequentially and separately. In contrast, our algorithm introduces the feedback of the obtained intermediate change maps into both generation and binarization of the difference image. More specifically, we adjust the weights of neighboring pixels in generating the difference image according to the intermediate change maps. With the fed-back intermediate change maps, we also extend the conventional single binarizing threshold for all pixels of the difference image to threshold maps, i.e., two individual binarizing thresholds are defined for each pixel of the difference image and the threshold maps are adjusted accordingly. Due to such feedback of the intermediate change maps, we may obtain a better difference image and generate more precise change maps, which can surely be fed back again. This iterative execution of the above-mentioned generation and binarization of difference images is terminated when a predefined Markov energy function stops decreasing, i.e., it reaches a local minimum. Experiments with several SAR image data sets with floods show that our algorithm consistently outperforms several state-of-the-art algorithms.

Cavity identification in elastic structures by explicit domain mapping and boundary mode sensitivity analysis

This paper presents a novel cavity identification approach for linear elastic structures with the boundary mode data. The proposed approach consists of two major aspects. The first is the construction of an explicit domain mapping that maps the reference domain onto the physical domain by the level set functions. In proceeding so, the R-functions theory not only provide an analytical methodology to get the level set functions for the external and interior cavity boundaries, but also construct the desired domain mapping directly by level-set-functions weighted interpolation of the parameterized boundary mappings. The physical domain with the cavities is explicitly parameterized in the domain mapping and the desired mesh keeps fixed over the reference domain. The second is the enhanced sensitivity-based approach for minimum solution to the nonlinear least-squares goal function of the cavity identification problem. The sensitivity of the boundary modes to the geometric parameters is analyzed with the help of the acquired domain mapping and the trust-region constraint is introduced in order to reach a weak convergence. Numerical examples are conducted to show the feasibility and efficiency of the present cavity identification approach.

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data.

OBJECTIVEThe authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery.METHODSThe convex framework is based on the precomputation of a dictionary composed of the individual dose distributions of all possible shots, considering all their possible locations, sizes, and shapes inside the target volume. The convex problem is solved to determine the plan, i.e., which shots and with which weights, that will actually be used, considering a sparsity constraint on the shots to fulfill the constraints while minimizing the beam-on time. The system is called IntuitivePlan and allows data to be transferred from generated dose plans into the Gamma Knife treatment planning software for further dosimetry evaluation.RESULTSThe system has been very efficiently implemented, and an optimal plan is usually obtained in less than 1 to 2 minutes, depending on the complexity of the problem, on a desktop computer or in only a few minutes on a high-end laptop. Dosimetry data from 5 cases, 2 meningiomas and 3 vestibular schwannomas, were generated with IntuitivePlan. Results of evaluation of the dosimetry characteristics are very satisfactory and adequate in terms of conformity, selectivity, gradient, protection of organs at risk, and treatment time.CONCLUSIONSThe possibility of using optimal, interactive real-time inverse planning in conjunction with the Leksell Gamma Knife opens new perspectives in radiosurgery, especially considering the potential use of the full capabilities of the latest generations of the Leksell Gamma Knife. This approach gives new users the possibility of using the system for easier and quicker access to good-quality plans with a shorter technical training period and opens avenues for new planning strategies for expert users. The use of a convex optimization approach allows an optimal plan to be provided in a very short processing time. This way, innovative graphical user interfaces can be developed, allowing the user to interact directly with the planning system to graphically define the desired dose map and to modify on-the-fly the dose map by moving, in a very user-friendly manner, the isodose surfaces of an initial plan. Further independent quantitative prospective evaluation comparing inverse planned and forward planned cases is warranted to validate this novel and promising treatment planning approach.

Where, What, When, and Why Is Bottom Mapping Needed? An On-Line Application to Set Priorities Using Expert Opinion

Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping suggestions from diverse stakeholders. Participants are each provided with 100 virtual coins to place throughout a region of interest to convey their mapping priorities. Inputs are standardized into a spatial framework using a grid and pull-down menus. These enabled participants to convey the types of mapping products that they need, the rationale used to justify their needs, and the locations that they prioritize for mapping. This system was implemented in a proposed National Marine Sanctuary encompassing 2784 km2 of Lake Michigan, Wisconsin. We demonstrate key analyses of the outputs, including coin counts, cell ranking, and multivariate cluster analysis for isolating high priority topics and locations. These techniques partition the priorities among the disciplines of the respondents, their selected justifications, and types of desired map products. The results enable respondents to identify potential collaborations to achieve common goals and more effectively invest limited mapping funds. The approach can be scaled to accommodate larger geographic areas and numbers of participants and is not limited to seafloor mapping.

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Observed angles and geodesic light-cone coordinates

We discuss the interpretation of the angles in the geodesic light-cone (GLC) coordinates. In particular, we clarify the way in which these angles can be identified with the observed ones. We show that, although this identification is always possible in principle, one cannot implement it in the usual gauge-fixing way, i.e. through a set of conditions on the GLC metric. Rather, one needs to invoke a tetrad at the observer and a Cartesian-like coordinate system in order to obtain the desired map globally on the observed sky.

Assessing suitable sites for Falcata (Paraserianthes falcataria Nielsen) plantation in Bukidnon, Philippines using GIS

This study was conducted to determine sites that are suitable for the establishment of Falcata (Paraserianthes falcataria (Nielsen) plantations in Bukidnon, Philippines using Geographic Information System (GIS). GIS layers of variables such as soil, topography, climate and land use/land cover (lulc) were combined to generate the desired suitability map. A total of 2,076 Falcata plantation occurences distributed across the 22 municipalities needed for the validation of results were also collected. Results have indicated that among the 22 municipalities, the greatest number of occurrences (393) was found in Impasugong, while Kalilangan had the least with only 1 occurrence. Restricted areas (unsuitable) constitute 301,187 hectares or 33% of the total area of the province. These sites include protected areas, roads, built-up areas, lakes, rivers, and forests. Highly suitable, moderately suitable and marginally suitable constitute 22%, 44% and 1% respectively. It was also found that majority of the occurrence points coincide with highly and moderately suitable sites.

A Wearable High-Resolution Facial Electromyography for Long Term Recordings in Freely Behaving Humans

Human facial expressions are a complex capacity, carrying important psychological and neurological information. Facial expressions typically involve the co-activation of several muscles; they vary between individuals, between voluntary versus spontaneous expressions, and depend strongly on personal interpretation. Accordingly, while high-resolution recording of muscle activation in a non-laboratory setting offers exciting opportunities, it remains a major challenge. This paper describes a wearable and non-invasive method for objective mapping of facial muscle activation and demonstrates its application in a natural setting. We focus on muscle activation associated with “enjoyment”, “social” and “masked” smiles; three categories with distinct social meanings. We use an innovative, dry, soft electrode array designed specifically for facial surface electromyography recording, a customized independent component analysis algorithm, and a short training procedure to achieve the desired mapping. First, identification of the orbicularis oculi and the levator labii superioris was demonstrated from voluntary expressions. Second, the zygomaticus major was identified from voluntary and spontaneous Duchenne and non-Duchenne smiles. Finally, using a wireless device in an unmodified work environment revealed expressions of diverse emotions in face-to-face interaction. Our high-resolution and crosstalk-free mapping, along with excellent user-convenience, opens new opportunities in gaming, virtual-reality, bio-feedback and objective psychological and neurological assessment.

Generative Adversarial Networks for Change Detection in Multispectral Imagery

Change detection can be treated as a generative learning procedure, in which the connection between bitemporal images and the desired change map can be modeled as a generative one. In this letter, we propose an unsupervised change detection method based on generative adversarial networks (GANs), which has the ability of recovering the training data distribution from noise input. Here, the joint distribution of the two images to be detected is taken as input and an initial difference image (DI), generated by traditional change detection method such as change vector analysis, is used to provide prior knowledge for sampling the training data based on Bayesian theorem and GAN’s min–max game theory. Through the continuous adversarial learning, the shared mapping function between the training data and their corresponding image patches can be built in GAN’s generator, from which a better DI can be generated. Finally, an unsupervised clustering algorithm is used to analyze the better DI to obtain the desired binary change map. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed method.

A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification

A sparse and low-rank near-isometric linear embedding (SLRNILE) method has been proposed to make dimensionality reduction and extract proper features for hyperspectral imagery (HSI) classification. The SLRNILE stands on the theory of the John-Lindenstrauss lemma, and tries to estimate a sparse and low-rank projection matrix that satisfies the restricted isometric property (RIP) condition on all secants of the HSI data. The RIP condition guarantees that the desired linear mapping near-isometrically preserves nearest neighbor points of all HSI pixels. Seeking the desired mapping is then modeled into minimizing a Lagrange multipliers formulation. The alternating direction method of multipliers framework is utilized to solve the above convex program, and column generation techniques are adopted to alleviate the computation memory burden during the optimization procedure. Five experiments on three widely used HSI data sets are designed to completely test the performance of SLRNILE, and experimental results are compared against those of six state-of-the-art feature extraction methods, including principal component analysis, Laplacian eigenmaps, locality preserving projections, neighborhood preserving embedding, sparse nonnegative matrix underapproximation, and random projections. The results show that SLRNILE performs best among all the seven methods, and its computational time is longest of all but still bearable for regular users. Therefore, the SLRNILE can be a good choice for feature extraction in HSI classification.

Cobordisms of sutured manifolds and the functoriality of link Floer homology

It has been a central open problem in Heegaard Floer theory whether cobordisms of links induce homomorphisms on the associated link Floer homology groups. We provide an affirmative answer by introducing a natural notion of cobordism between sutured manifolds, and showing that such a cobordism induces a map on sutured Floer homology. This map is a common generalization of the hat version of the closed 3-manifold cobordism map in Heegaard Floer theory, and the contact gluing map defined by Honda, Kazez, and Matić. We show that sutured Floer homology, together with the above cobordism maps, forms a type of TQFT in the sense of Atiyah. Applied to the sutured manifold cobordism complementary to a decorated link cobordism, our theory gives rise to the desired map on link Floer homology. Hence, link Floer homology is a categorification of the multi-variable Alexander polynomial. We outline an alternative definition of the contact gluing map using only the contact element and handle maps. Finally, we show that a Weinstein sutured manifold cobordism preserves the contact element.

Run-time monitoring using bounded constraint instance discovery within big data streams

This paper presents RM-BCS, a novel run-time monitoring (RM) technique that uses periodic bounded constraint solving (BCS) to monitor streaming systems with undetermined artifacts such as agent roles and message classification. BCS is used to discover maps of interest, mapping a streaming collection of messages to a domain of discourse that consists of a set of known agents, roles, and message meanings; the desired map is one in which an underlying first-order logic constraint is satisfied. We demonstrate the technique for the problem of detecting an, Internet-based terrorist attack coordination session within frames of streaming data such as a Twitter stream. The proposed technique is a third class of RM techniques, the first two being RM of deterministic observation streams and RM of probabilistic observation streams.