Characterizing Usage Patterns and Service Demand of a Two-Way Car-Sharing System

Urban mobility is directly linked to the demand for communication resources and, clearly, its understanding is useful for better planning of urban and communication systems. However, getting data about urban mobility is still a challenge. In many cases, only a few companies have access to accurate and updated data. In most cases, these data are also privacy sensitive. It is thus important to generate models that can help to understand mobility patterns. We here characterize the demands of a two-way car-sharing system. We explore data of the public API of Modo, a car-sharing system that operates in Vancouver (Canada) and nearby regions. Our study uncovers patterns of users’ habits and demands in the service, which can be explored for urban and communication planning

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Characterisation of road bumps using smartphones

Introduction: Speed bumps are used as the main means of controlling vehicle speeds all over the world. It is not too infrequent, especially in the emerging economies, to have unmarked bumps that can be perilous for the passengers. Fortuitously, the roadways and mobile phone networks have grown simultaneously in emerging economies. This paper demonstrates the capability of smartphones placed inside the vehicles in characterisation of road bumps. The smart mobile phones have accelerometers and position sensors that can be useful for autonomous monitoring roads. This can empower the user community in monitoring of roads. However, the capability of the smartphone in discerning different types of speed bumps while travelling in heterogeneous vehicle types needs to be examined. Methods: A range of road vehicles is mathematically modelled as mass, spring, and damper systems. The mathematical model of the vehicle is excited with parameters analogous to some common speed bumps and its acceleration response is calculated. The accelerometer of a smartphone is validated by comparing it with high precision accelerometers. The acceleration response of the phone while passing over the corresponding road bumps, which was used in the model earlier, is recorded using an Android based application. The experiment is repeated for different classes of vehicles. Filters have been used to reduce noise in the signals. A time averaging technique has been employed to compress the collected data.Results and conclusions: The acceleration signals have been digitally processed to capture road bumps. The importance of using a mathematical model to understand the acceleration response of a vehicle has been established. Also, the use of pass filters to extract the signal of concern from the noisy data has been exhibited. The ability of the technique to discern different types of speed bumps while travelling in a variety of vehicle types has been demonstrated. This investigation demonstrates the potential to automatically monitor the condition of roadways obviating costly manual inspections. As smartphones are ubiquitous, the methodology has the potential to empower the user community in the maintenance of infrastructure

PATÓGENOS Y SÍNTOMAS ASOCIADOS A LA MARCHITEZ DEL TOMATE (Solanum lycopersicum L.) EN TEXCOCO MÉXICO

Se identificó a Phytophthora capsici, Rhizoctonia solani y Fusarium oxysporum como agentes causales de la marchitez del tomate en Texcoco, Edo. de México y, se evaluó la sintomatología e incidencia de la marchitez inducida por estos hongos con diferentes métodos de inoculación. Cultivos de cada hongo se inocularon en plantas de tomate con 4-5 hojas verdaderas. La inoculación de P. capsici por inmersión de raíces en solución de zoosporas fue más eficiente (96,7 % de incidencia) que la inoculación al cuello, a los 6 días después de la inoculación (ddi). Este hongo indujo marchitez, pudrición de raíz y cuello, y muerte de las plantas a los 4 ddi. R. solani, al inocularse por inmersión en solución de propágalos y a través de granos de trigo infectados con el hongo, no ocasionó la muerte de las plantas, sin embargo, la inoculación con granos de trigo provocó una incidencia de 100 %, que se manifestó en reducción de crecimiento (50 %) y en amarillamiento generalizado. F. oxysporum presentó una incidencia de 100 % a los 15 y 30 ddi, para la variedad Río Grande e híbrido Yaqui, respectivamente. Las plantas manifestaron clorosis, marchitez generalizada, necrosis de tejido vascular y finalmente la muerte

Underwater Hyperspectral Imaging (UHI): a review of systems and applications for proximal seafloor ecosystem studies

Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional sampling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research

Environmentally friendly analysis of emerging contaminants by pressurized hot water extraction-stir bar sorptive extraction-derivatization and gas chromatography-mass spectrometry

This work describes the development, optimiza- tion, and validation of a new method for the simultaneous determination of a wide range of pharmaceuticals (beta- blockers, lipid regulators ... ) and personal care products (fragrances, UV filters, phthalates ... ) in both aqueous and solid environmental matrices. Target compounds were extracted from sediments using pressurized hot water ex- traction followed by stir bar sorptive extraction. The first stage was performed at 1,500 psi during three static extrac- tion cycles of 5 min each after optimizing the extraction temperature (50 – 150 °C) and addition of organic modifiers (% methanol) to water, the extraction solvent. Next, aqueous extracts and water samples were processed using polydime- thylsiloxane bars. Several parameters were optimized for this technique, including extraction and desorption time, ionic strength, presence of organic modifiers, and pH. Fi- nally, analytes were extracted from the bars by ultrasonic irradiation using a reduced amount of solvent (0.2 mL) prior to derivatization and gas chromatography – mass spectrome- try analysis. The optimized protocol uses minimal amounts of organic solvents (<10 mL/sample) and time ( ≈ 8 h/sam- ple) compared to previous ex isting methodologies. Low standard deviation (usually below 10 %) and limits of de- tection (sub-ppb) vouch for the applicability of the method- ology for the analysis of target compounds at trace levels. Once developed, the method was applied to determin

Floral temperature and optimal foraging: is heat a feasible floral reward for pollinators?

As well as nutritional rewards, some plants also reward ectothermic pollinators with warmth. Bumble bees have some control over their temperature, but have been shown to forage at warmer flowers when given a choice, suggesting that there is some advantage to them of foraging at warm flowers (such as reducing the energy required to raise their body to flight temperature before leaving the flower). We describe a model that considers how a heat reward affects the foraging behaviour in a thermogenic central-place forager (such as a bumble bee). We show that although the pollinator should spend a longer time on individual flowers if they are warm, the increase in total visit time is likely to be small. The pollinator's net rate of energy gain will be increased by landing on warmer flowers. Therefore, if a plant provides a heat reward, it could reduce the amount of nectar it produces, whilst still providing its pollinator with the same net rate of gain. We suggest how heat rewards may link with plant life history strategies

Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder.

The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07-272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance

GDM-VieweR: A new tool in R to visualize the evolution of fuzzy consensus processes

With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases many experts, and a huge number of different alternatives. In the literature we can find a great deal of methodologies to assist multi-person decision making. However these classical approaches are not prepared to deal with such a huge complexity and there is a lack of tools that support the decision processes providing some graphical information. Therefore the main objective of this contribution is to present an open source tool developed in R to provide a quick insight of the evolution of the decision making by means of meaningful graphical representations. Thanks to the modular architecture of this solution this tool can be easily adapted to work with various Group decision making methodologies