Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R2 value of 99.7 % and an AUC-ROC score of 98 %. These results were statistically significant, as there was a noticeable difference (within a 95 % confidence interval) compared to the estimations of the three base models. In terms of estimation error, the ensemble model outperformed the top base regressor by demonstrating improvements of 6.6 %, 11.1 %, and 12.75 % for the RMSE, MAE, and CV_MSE evaluation metrics, respectively. The precise and robust estimations achieved by the best regression model in this study further highlight the effectiveness of AI in the field of pipeline engineering.

Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network

Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle's yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.

Experimental Data on the Role of Melatonin in the Pathogenesis of Nonalcoholic Fatty Liver Disease

Despite the increasing prevalence of nonalcoholic fatty liver disease (NAFLD) worldwide, its complex pathogenesis remains incompletely understood. The currently stated hypotheses cannot fully clarify the interrelationships between individual pathogenetic mechanisms of the disease. No appropriate health strategies have been developed for treating NAFLD. NAFLD is characterized by an accumulation of triglycerides in hepatic cells (steatosis), with the advanced form known as nonalcoholic steatohepatitis. In the latter, superimposed inflammation can lead to fibrosis. There are scientific data on NAFLD's association with components of metabolic syndrome. Hormonal factors are thought to play a role in the development of metabolic syndrome. Endogenous melatonin, an indoleamine hormone synthesized by the pineal gland mainly at night, is a powerful chronobiotic that probably regulates metabolic processes and has antioxidant, anti-inflammatory, and genomic effects. Extrapineal melatonin has been found in various tissues and organs, including the liver, pancreas, and gastrointestinal tract, where it likely maintains cellular homeostasis. Melatonin exerts its effects on NAFLD at the cellular, subcellular, and molecular levels, affecting numerous signaling pathways. In this review article, we discuss the experimental scientific data accumulated on the involvement of melatonin in the intimate processes of the pathogenesis of NAFLD.

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

Accelerating the development of synthetic biology applications requires reproducible experimental findings. Different standards and repositories exist to exchange experimental data and metadata. However, the associated software tools often do not support a uniform data capture, encoding, and exchange of information. A connection between digital repositories is required to prevent siloing and loss of information. To this end, we developed the Experimental Data Connector (XDC). It captures experimental data and related metadata by encoding it in standard formats and storing the converted data in digital repositories. Experimental data is then uploaded to Flapjack and the metadata to SynBioHub in a consistent manner linking these repositories. This produces complete connected experimental data sets that are exchangeable. The information is captured using a single template Excel Workbook, which can be integrated into existing experimental workflow automation processes and semiautomated capture of results.

Simplified Modelling of the Edge Crush Resistance of Multi-Layered Corrugated Board: Experimental and Computational Study

The edge crush test is the most popular laboratory test in the corrugated packaging industry. It measures the edge crush resistance of a sample in the cross-fiber direction (CD), also known as the ECT index. This parameter is widely used for the specification of the board by its producers. It is also utilized in most analytical formulas describing the load capacity of the packaging. On the other hand, the ECT value can be estimated from both analytical and numerical models based on the basic parameters of each constituent paper. Knowing the compressive strength in CD (commonly known as SCT) and the elastic properties of the individual layers, the sample geometry (i.e., the period and height of the corrugated layer), as well as the boundary conditions, the ECT value can be calculated. This is very useful as new boards can be virtually analyzed before being manufactured. In this work, both detailed numerical models based on finite elements (FE) methods and very simple analytical (engineering) models were used for the ECT calculations. All presented models were validated with experimental data. The surprising consistency and high precision of the results obtained with the simplest approach was additionally analyzed in the study.

A Holistic Approach for the Identification of Success Factors in Secondary Cleft Osteoplasty

Cleft lip and palate belong to the most frequent craniofacial anomalies. Secondary osteoplasty is usually performed between 7 and 11 years with the closure of the osseus defect by autologous bone. Due to widespread occurrence of the defect in conjunction with its social significance due to possible esthetic impairments, the outcome of treatment is of substantial interest. The success of the treatment is determined by the precise rebuilding of the dental arch using autologous bone from the iliac crest. A detailed analysis of retrospective data disclosed a lack of essential and structured information to identify success factors for fast regeneration and specify the treatment. Moreover, according to the current status, no comparable process monitoring is possible during osteoplasty due to the lack of sensory systems. Therefore, a holistic approach was developed to determine the parameters for a successful treatment via the incorporation of patient data, the treatment sequences and sensor data gained by an attachable sensor module into a developed Dental Tech Space (DTS). This approach enables heterogeneous data sets to be linked inside of DTS, archiving and analysis, and is also for future considerations of respective patient-specific treatment plans.

The Potentials of Egyptian and Indian Granites for Protection of Ionizing Radiation

This paper aims to study the radiation shielding characteristics and buildup factor of some types of granite in Egypt. The mass attenuation coefficient (MAC) for three types of granite (gandola, white halayeb, and red aswani) was experimentally determined, and the experimental results were validated by XCOM software. The relative deviation between the two methods does not exceed 3% in all discussed granite samples, which means that MAC calculated through the experimental and XCOM are in suitable agreement. The effective atomic number (Z_eff) varies from 13.64 to 10.69, 13.68 to 10.59, and 13.45 and 10.66 for gandola, white halayeb, and red aswani, respectively. As well as the equivalent atomic number (Z_eq) was calculated in a wide range of energy to deduce the exposure (EBF) and energy absorption (EABF) buildup factors for the studied granite materials. The linear attenuation coefficient (LAC), half-value layer (HVL), mean free path (MFP) were calculated at each investigated energy and showed that the most effective shielding ability at high energy was red aswani, while at low energy, the shielding ability was nearly constant for studied granites. The present study forms the first endeavor to obtain the radiation shielding properties of the studied materials to be used in practical applications.

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frames.

Crystal Memory near Discontinuous Triacylglycerol Phase Transitions: Models, Metastable Regimes, and Critical Points

It is proposed that "crystal memory", observed in a discontinuous solid-liquid phase transition of saturated triacylglycerol (TAG) molecules, is due to the coexistence of solid TAG crystalline phases and a liquid TAG phase, in a superheated metastable regime. Such a coexistence has been detected. Solid crystals can act as heterogeneous nuclei onto which molecules can condense as the temperature is lowered. We outlined a mathematical model, with a single phase transition, that shows how the time-temperature observations can be explained, makes predictions, and relates them to recent experimental data. A modified Vogel-Fulcher-Tammann (VFT) equation is used to predict time-temperature relations for the observation of "crystal memory" and to show boundaries beyond which "crystal memory" is not observed. A plot of the lifetime of a metastable state versus temperature, using the modified VFT equation, agrees with recent time-temperature data. The model can be falsified through its predictions: the model possesses a critical point and we outline a procedure describing how it could be observed by changing the hydrocarbon chain length. We make predictions about how thermodynamic functions will change as the critical point is reached and as the system enters a crossover regime. The model predicts that the phenomenon of "crystal memory" will not be observed unless the system is cooled from a superheated metastable regime associated with a discontinuous phase transition.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.

Microstructure and Compression Properties of VSS‑V3B2 Eutectic Alloys in the V-Si-B System

The present study reports on the microstructural evolution and room temperature plasticity of V(‑Si)‑B alloys with respect to the V solid solution (V_SS)‑V₃B₂ phase region. To investigate the occurring effects systematically, different binary V‑B and ternary V‑Si‑B alloys were produced by conventional arc melting. Scanning electron microscope (SEM) analyses and X-ray diffraction (XRD) measurements were used to characterize the resulting as-cast microstructures. For the first time, the eutectic composition was systematically traced from the binary V‑B domain to the ternary V‑Si‑B system. The observations discover that the binary eutectic trough (V_SS‑V₃B₂) seems to reach into the ternary system up to an alloy composition of V‑5Si‑9B. Room temperature compression tests were carried out in order to study the impact of single-phase and multi-phase microstructures on the strength and plasticity of binary and ternary alloys. The results indicate that the V_SS phase controls the plastic deformability in the V_SS‑V₃B₂ eutectic microstructure whereas the intermetallic V₃B₂ acts as a strong hardening phase.

The need for reliable data in computational thermodynamics

Computational methods have become indispensable tools for efficient development and processing of new materials and have led to the new discipline of integrated computational materials engineering (ICME). The CALPHAD (calculation of phase diagrams) method has been identified as one of the pillars of ICME. The CALPHAD method, originally developed to model thermodynamic properties and phase diagrams, uses extrapolation methods for the functions of binary and ternary systems that enable the calculation of the properties of higher order systems. The CALPHAD functions are built to a large extent on available experimental data for these binary and ternary systems. To ensure reliability of the results from CALPHAD calculations, it is necessary to critically evaluate the experimental data that are being used for developing the CALPHAD functions. This review presents a brief overview of the CALPHAD method and its models, summarizes the data that are needed and the criteria that need to be applied for the evaluation of these data.

A Bayesian model updating framework for robust seismic fragility analysis of non-isolated historic masonry towers

Seismic assessment of existing masonry structures requires a numerical model able to both reproduce their nonlinear behaviour and account for the different sources of uncertainties; the latter have to be dealt with since the unavoidable lack of knowledge on the input parameters (material properties, geometry, boundary conditions, etc.) has a relevant effect on the reliability of the seismic response provided by the numerical approaches. The steadily increasing necessity of combining different sources of information/knowledge makes the Bayesian approach an appealing technique, not yet fully investigated for historic masonry constructions. In fact, while the Bayesian paradigm is currently employed to solve inverse problems in several sectors of the structural engineering domain, only a few studies pay attention to its effectiveness for parameter identification on historic masonry structures. This study combines a Bayesian framework with probabilistic structural analyses: starting from the Bayesian finite element model updating by using experimental data it provides the definition of robust seismic fragility curves for non-isolated masonry towers. A comparison between this method and the standard deterministic approach illustrates its benefits. This article is part of the theme issue 'Environmental loading of heritage structures'.

Mass Transfer Coefficient in Multi-Stage Reformer/Membrane Modules for Hydrogen Production

Hydrogen is a promising energy carrier, and is exploitable to extract energy from fossil fuels, biomasses, and intermittent renewable energy sources and its generation from fossil fuels, with CO₂ separation at the source being one of the most promising pathways for fossil fuels' utilization. This work focuses on a particular configuration called the Reformer and Membrane Module (RMM), which alternates between stages of Steam Reforming (SR) reactions with H₂ separation stages to overcome the thermodynamic limit of the conventional SR. The configuration has numerous advantages with respect to the more widely studied and tested membrane reactors, and has been tested during a pilot-scale research project. Although numerous modelling works appeared in the literature, the design features of the material exchanger (in the so-called RMM architecture) of different geometrical configurations have not been developed, and the mass transfer correlations, capable of providing design tools useful for such membrane modules, are not available. The purpose of this work is therefore to apply a physical-mathematical model of the mass transfer, in three different geometries, considering both concentration polarization and membrane permeation, in order to: (i) simulate the cited experimental results; (ii) estimate the scaling-up correlations for the "material exchange modules"; and (iii) identify the mass transfer limiting regime in relation to the gas mass flow rate.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids

To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

Design and performance analysis of a 3-RRR spherical parallel manipulator for hip exoskeleton applications

This paper presents the design and performance analysis and experimental study of a 3-RRR spherical parallel manipulator in the context of hip exoskeleton applications. First, the mechanism's inverse kinematics analysis and Jacobian matrix development are revisited. Manipulability, dexterity, and rotational sensitivity indices are then evaluated for two different methods of attachment to the human body. The superior attachment method in terms of these performance measures is indicated, and an experimental study based on the selected method is conducted; the experiment involves testing the capability of a 3-RRR manipulator's end-effector in tracking the motions experienced by a human hip joint during normal gait cycles. Finally, the results of the experimental study indicate that the manipulator represents a feasible hip exoskeleton solution providing total kinematic compliance with the human hip joint's 3-degree-of-freedom motion capabilities.

Coupling schemes for the FSI forward prediction challenge: Comparative study and validation

This paper presents a numerical study in which several partitioned solution procedures for incompressible fluid-structure interaction are compared and validated against the results of an experimental fluid-structure interaction benchmark. The numerical methods discussed cover the three main families of coupling schemes: strongly coupled, semi-implicit, and loosely coupled. Very good agreement is observed between the numerical and experimental results. The comparisons confirm that strong coupling can be efficiently avoided, via semi-implicit and loosely coupled schemes, without compromising stability and accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

An Extended Damage Plasticity Model for Shotcrete: Formulation and Comparison with Other Shotcrete Models

The aims of the present paper are (i) to briefly review single-field and multi-field shotcrete models proposed in the literature; (ii) to propose the extension of a damage-plasticity model for concrete to shotcrete; and (iii) to evaluate the capabilities of the proposed extended damage-plasticity model for shotcrete by comparing the predicted response with experimental data for shotcrete and with the response predicted by shotcrete models, available in the literature. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated shotcrete models and they will serve as a basis for the design of a new lab test program, complementing the existing ones.

Deriving Structural Information from Experimentally Measured Data on Biomolecules

During the past half century, the number and accuracy of experimental techniques that can deliver values of observables for biomolecular systems have been steadily increasing. The conversion of a measured value Q^exp of an observable quantity Q into structural information is, however, a task beset with theoretical and practical problems: 1) insufficient or inaccurate values of Q^exp , 2) inaccuracies in the function Q(r→) used to relate the quantity Q to structure r→ , 3) how to account for the averaging inherent in the measurement of Q^exp , 4) how to handle the possible multiple-valuedness of the inverse r→(Q) of the function Q(r→) , to mention a few. These apply to a variety of observable quantities Q and measurement techniques such as X-ray and neutron diffraction, small-angle and wide-angle X-ray scattering, free-electron laser imaging, cryo-electron microscopy, nuclear magnetic resonance, electron paramagnetic resonance, infrared and Raman spectroscopy, circular dichroism, Förster resonance energy transfer, atomic force microscopy and ion-mobility mass spectrometry. The process of deriving structural information from measured data is reviewed with an eye to non-experts and newcomers in the field using examples from the literature of the effect of the various choices and approximations involved in the process. A list of choices to be avoided is provided.

The Program and Treatment Effect of Summer Jobs on Girls' Post-Schooling Incomes

BACKGROUND

Public programs offering summer jobs to smooth the transition from school to work is commonplace. However, the empirical support for summer jobs is limited. This article exploits the availability of registered individual information and random allocation to summer jobs to provide empirical evidence on this issue.

OBJECTIVES

To identify the effect of summer job programs on the post-schooling incomes of the intended participants. Also to identify the effect of sophomore girls' high school work experience on their post-schooling incomes.

RESEARCH DESIGN

In this article, 1,447 sophomore girls from 1997 to 2003 are followed 5-12 years after graduation. They all applied to Falun municipality's (Sweden) summer job program, and about 25% of them were randomly allotted a job. The random allocation to a summer job is used to identify the causal effect of sophomore girls' high school income on their post-schooling incomes.

SUBJECTS

All the 1,447 sophomore girls who applied to Falun municipality's summer job program during 1997-2003.

MEASURES

Annual post-schooling income is used as an outcome measure. The work experience of girls in high school is also measured in terms of total income while in high school.

RESULTS

The program led to a substantially larger accumulation of income during high school as well as 19% higher post-schooling incomes. The high school income led to a post-schooling income elasticity of 0.37 which is, however, potentially heterogeneous with regard to academic ability.

CONCLUSIONS

Both the program effect and the causal effect of high school income on post-schooling incomes were substantial and statistically significant.

From the ground up: Integrative research in primate locomotion

Primate locomotor adaptation and evolution is a principal and thriving area of research by biological anthropologists. Research in this field generally targets hypotheses regarding locomotor kinetics and kinematics, form-function associations in both the soft and hard tissue components of the musculoskeletal system, and reconstructing locomotor behavior in fossil primates. A wide array of methodological approaches is used to address adaptive hypotheses in all of these realms. Recent advances in three-dimensional shape capture, musculoskeletal physiological measurements, and analytical processing technologies (e.g., laser and CT-scans, 3D motion analysis systems, finite element analysis) have facilitated the collection and analysis of larger and more complex locomotor datasets than previously possible. With these advances in technology, new methods of form-function analyses can be developed to produce a more thorough understanding of how form reflects an organism's mechanical requirements, how shape is influenced by external environmental factors, and how these investigations of living taxa can inform questions of primate paleobiology. The papers in this special section of the American Journal of Physical Anthropology present research that builds on that foundation, by combining new data on living primates and new methodologies and approaches to answer a range of questions on extant and extinct primates.

Estimating the peer effect on youth overweight and inactivity using an intervention study

BACKGROUND

Understanding peer effect is potentially important for finding ways to combat the obesity epidemic. Nevertheless, it is not clear whether a peer effect exists because of the composition of the peer group, because the group members have similar, unobserved individual characteristics, or because of an endogenous effect.

METHODS

This issue is addressed here by using a unique longitudinal data set of 573 schoolchildren attending state schools in the Municipality of Aalborg, Denmark, during 2008-2010. To identify the spillover effect of a targeted intervention on peers, we use a difference-in-differences approach.

RESULTS

A targeted health intervention reduced body mass index (BMI) among overweight, inactive individuals and, more interestingly, that BMI among the nontreated peers was reduced by 1.04%.

CONCLUSIONS

The analysis suggests that individually targeted health intervention has a health benefit beyond the treated individual, which needs to be included when evaluating targeted policies aiming to combat childhood overweight and inactivity.

An open-source data storage and visualization back end for experimental data

In this article, a flexible free and open-source software system for data logging and presentation will be described. The system is highly modular and adaptable and can be used in any laboratory in which continuous and/or ad hoc measurements require centralized storage. A presentation component for the data back end has furthermore been written that enables live visualization of data on any device capable of displaying Web pages. The system consists of three parts: data-logging clients, a data server, and a data presentation Web site. The logging of data from independent clients leads to high resilience to equipment failure, whereas the central storage of data dramatically eases backup and data exchange. The visualization front end allows direct monitoring of acquired data to see live progress of long-duration experiments. This enables the user to alter experimental conditions based on these data and to interfere with the experiment if needed. The data stored consist both of specific measurements and of continuously logged system parameters. The latter is crucial to a variety of automation and surveillance features, and three cases of such features are described: monitoring system health, getting status of long-duration experiments, and implementation of instant alarms in the event of failure.

The role of cholecystokinin in the induction of aggressive behavior: a focus on the available experimental data (review)

Cholecystokinin (CCK) is a neuropeptide that is (among others) reportedly involved in the pathophysiology of psychiatric disorders. The excitatory role of CCK in negative affective emotions as well as in aversive reactions, antisocial behaviors and memories, has been indicated by numerous electrophysiological, neurochemical and behavioral methodologies on both animal models for anxiety and human studies. The current review article summarizes the existing experimental evidence with regards to the role of CCK in the induction of aggressive behavior, and: (a) synopsizes the anatomical circuits through which it could potentially mediate all types of aggressive behavior, as well as (b) highlights the potential use of these experimental evidence in the current research quest for the clinical treatment of mood and anxiety disorders.

NSAIDs in the Acute Treatment of Migraine: A Review of Clinical and Experimental Data

Migraine is a common disabling neurological disorder with a serious socio-economical burden. By blocking cyclooxygenase nonsteroidal anti-inflammatory drugs (NSAIDs) decrease the synthesis of prostaglandins, which are involved in the pathophysiology of migraine headaches. Despite the introduction more than a decade ago of a new class of migraine-specific drugs with superior efficacy, the triptans, NSAIDs remain the most commonly used therapies for the migraine attack. This is in part due to their wide availability as over-the-counter drugs and their pharmaco-economic advantages, but also to a favorable efficacy/side effect profile at least in attacks of mild and moderate intensity. We summarize here both the experimental data showing that NSAIDs are able to influence several pathophysiological facets of the migraine headache and the clinical studies providing evidence for the therapeutic efficacy of various subclasses of NSAIDs in migraine therapy. Taken together these data indicate that there are several targets for NSAIDs in migraine pathophysiology and that on the spectrum of clinical potency acetaminophen is at the lower end while ibuprofen is among the most effective drugs. Acetaminophen and aspirin excluded, comparative trials between the other NSAIDs are missing. Since evidence-based criteria are scarce, the selection of an NSAID should take into account proof and degree of efficacy, rapid GI absorption, gastric ulcer risk and previous experience of each individual patient. If selected and prescribed wisely, NSAIDs are precious, safe and cost-efficient drugs for the treatment of migraine attacks.