Layered Path Planning with Human Motion Detection for Autonomous Robots

Reactively planning a path in a dynamic and unstructured environment is a key challenge for mobile robots and autonomous systems. Planning should consider factors including the long-term and short-term prediction, current environmental situation, and human context. In this chapter, we present a novel robotic path-planning method with human activity information in a large-scale three-dimensional (3D) environment. In the learning stage, this method uses human motion detection results and preliminary environmental information to build a long-term context model with a hidden Markov model (HMM) to describe and predict human activities in the environment. In the application stage, when a robot detects humans in the environment, it first uses the long-term context model to generate impedance areas in the environment. Then, the robot searches each area of the environment to find paths between key locations, such as escalators, to generate a Reactive Key Cost Map (RKCM), whose vertexes are those key locations and edges are generated paths. The graphs of all areas are connected using the key nodes in the subgraphs to build a global graph of the whole environment. Finally, the robot can reactively plan a path based on the current environmental situation and predicted human activities. This method enables robots to navigate robustly in a large-scale 3D environment with regular human activities, and it significantly reduces computing workload with proposed RKCM

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

Ferrous Sulphate From Titanium Dioxide Industry For The Treatment Of Petroleum Refinery Wastewater

Kaedah rawatan penggumpalan dan pengelompokan digunakan secara meluas dalam proses industri. Kecekapan proses penggumpalan dan pengelompokan ditentukan oleh dos penggumpal, pH, suhu, kekuatan ionik, kepekatan bahan organik, caj permukaan dan beberapa faktor lain. FeSO4 (penggumpal) tidak didokumentasikan dengan baik dalam rawatan air sisa kilang penapisan petroleum (PRW). Oleh itu, dalam kajian ini, keupayaan FeSO4 dalam rawatan PRW telah dikaji. Kecekapan penyingkiran/pengurangan warna, jumlah pepejal terampai (TSS), kekeruhan dan keperluan okisigen kimia (COD) telah dikira. Penggumpalan dan pengelompokan boleh dipantau melalui pengukuran kegerakan elektroforetik dan penentuan potensi zeta. Dalam kajian ini, interaksi antara FeSO4 dengan zarah koloid bercas negatif dalam PRW dan mekanisme yang terlibat dalam proses penggumpalan dan pengelompokan dikaji. Tingkah laku zarah PRW yang berbeza terhadap FeSO4 bergantung kepada pH dan dos FeSO4 telah ditunjukkan. Coagulation-flocculation treatment method is widely used in industrial processes. The efficiency of coagulation-flocculation process is determined by the coagulant dosage, pH, temperature, ionic strength, nature and concentration of organic matter, the surface charge and several other factors. FeSO4 (coagulant) is not well documented in the treatment of petroleum refinery wastewater (PRW). Therefore, in this research, capability of FeSO4 in the treatment of PRW was studied. The removal/reduction efficiencies for color, total suspended solids (TSS), turbidity and chemical oxygen demand (COD) were calculated. The coagulation-flocculation process can be monitored through the measurement of the electrophoretic mobility and the determination of zeta potential. In this study, the interaction between FeSO4 and the negatively charged colloidal particles in PRW and the mechanisms involved in the coagulation-flocculation process were studied. Different behaviors of PRW particles with FeSO4 depending on the pH and the dosage of the FeSO4 were demonstrated

Populated Places and Conspicuous Consumption: High Population Density Cues Predict Consumers’ Luxury-Linked Brand Attitudes

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MiR-24 is required for hematopoietic differentiation of mouse embryonic stem cells

Overexpression of miRNA, miR-24, in mouse hematopoietic progenitors increases monocytic/ granulocytic differentiation and inhibits B cell development. To determine if endogenous miR-24 is required for hematopoiesis, we antagonized miR-24 in mouse embryonic stem cells (ESCs) and performed in vitro differentiations. Suppression of miR-24 resulted in an inability to produce blood and hematopoietic progenitors (HPCs) from ESCs. The phenotype is not a general defect in mesoderm production since we observe production of nascent mesoderm as well as mesoderm derived cardiac muscle and endothelial cells. Results from blast colony forming cell (BL-CFC) assays demonstrate that miR-24 is not required for generation of the hemangioblast, the mesoderm progenitor that gives rise to blood and endothelial cells. However, expression of the transcription factors Runx1 and Scl is greatly reduced, suggesting an impaired ability of the hemangioblast to differentiate. Lastly, we observed that known miR-24 target, Trib3, is upregulated in the miR-24 antagonized embryoid bodies (EBs). Overexpression of Trib3 alone in ESCs was able to decrease HPC production, though not as great as seen with miR-24 knockdown. These results demonstrate an essential role for miR-24 in the hematopoietic differentiation of ESCs. Although many miRNAs have been implicated in regulation of hematopoiesis, this is the first miRNA observed to be required for the specification of mammalian blood progenitors from early mesoderm

Probing hydrogen-bonding in binary liquid mixtures with terahertz time-domain spectroscopy: a comparison of Debye and absorption analysis.

Terahertz time-domain spectroscopy is used to explore hydrogen bonding structure and dynamics in binary liquid mixtures, spanning a range of protic-protic, protic-aprotic and aprotic-aprotic systems. A direct absorption coefficient analysis is compared against more complex Debye analysis and we observed good agreement of the two methods in determining the hydrogen bonding properties when at least one of the mixture components is protic. When both components are aprotic, we show that the trend in absorption coefficients match well with the theoretical trend in strength of hydrogen bond interactions predicted based on steric and electronic properties of the components.The authors would like to acknowledge funding provided by EPSRC Grant EP/G011397/1.This is the final published version. It first appeared at http://pubs.rsc.org/en/Content/ArticleLanding/2015/CP/c4cp04477k#!divAbstract

Successful Surgical Treatment of a Patient with a Solitary Asymptomatic Cardiac Metastasis from Breast Cancer, Identified by Elevated Tumor Markers and Circulating Tumor Cells

Indroduction: Cardiac metastases are a not infrequent autopsy finding in patients dying of metastatic cancer, but are less commonly diagnosed during life (1). Although the autopsy incidence of cardiac metastases ranges may be as high as 25%, solitary cardiac metastases in the absence of metastatic involvement of other organs are rare (2).Case Presentation: We report here the case of a 66-year old woman with a history of bilateral breast cancer, where a solitary metastasis in the right atrium was successfully resected.Conclusion: In the absence of any symptoms or clinical findings on physical examination, the presence of metastatic disease was first suggested by the detection of elevated tumor markers and circulating tumor cells during routine follow up after treatment for early stage breast cancer.

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a priori. This paper surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. We also summarize two critical applications of time series classification and extrinsic regression, human activity recognition and satellite earth observation

Powering population health research: Considerations for plausible and actionable effect sizes

Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform power and sample size calculations. E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to power and sample size calculations for research proposals to study population health interventions. We show that plausible effects sizes for population health inteventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes.Comment: 24 pages, 1 figur