A multi-model evaluation of aerosols over South Asia: common problems and possible causes

Atmospheric pollution over South Asia attracts special attention due to its effects on regional climate, water cycle and human health. These effects are potentially growing owing to rising trends of anthropogenic aerosol emissions. In this study, the spatio-temporal aerosol distributions over South Asia from seven global aerosol models are evaluated against aerosol retrievals from NASA satellite sensors and ground-based measurements for the period of 2000–2007. Overall, substantial underestimations of aerosol loading over South Asia are found systematically in most model simulations. Averaged over the entire South Asia, the annual mean aerosol optical depth (AOD) is underestimated by a range 15 to 44% across models compared to MISR (Multi-angle Imaging SpectroRadiometer), which is the lowest bound among various satellite AOD retrievals (from MISR, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra). In particular during the post-monsoon and wintertime periods (i.e., October–January), when agricultural waste burning and anthropogenic emissions dominate, models fail to capture AOD and aerosol absorption optical depth (AAOD) over the Indo–Gangetic Plain (IGP) compared to ground-based Aerosol Robotic Network (AERONET) sunphotometer measurements. The underestimations of aerosol loading in models generally occur in the lower troposphere (below 2 km) based on the comparisons of aerosol extinction profiles calculated by the models with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Furthermore, surface concentrations of all aerosol components (sulfate, nitrate, organic aerosol (OA) and black carbon (BC)) from the models are found much lower than in situ measurements in winter. Several possible causes for these common problems of underestimating aerosols in models during the post-monsoon and wintertime periods are identified: the aerosol hygroscopic growth and formation of secondary inorganic aerosol are suppressed in the models because relative humidity (RH) is biased far too low in the boundary layer and thus foggy conditions are poorly represented in current models, the nitrate aerosol is either missing or inadequately accounted for, and emissions from agricultural waste burning and biofuel usage are too low in the emission inventories. These common problems and possible causes found in multiple models point out directions for future model improvements in this important region.

En jämförelse av molnbaserade realtidsanalystjänster : Skillnader i användbarhet mellan multi-tenant och single-tenant arkitekturer

Internet of Things är ett samlingsbegrepp för den utveckling som innebär att olika typer av enheter kan förses med sensorer och datachip som är uppkopplade mot internet. En ökad mängd data innebär en ökad förfrågan på lösningar som kan lagra, spåra, analysera och bearbeta data. Ett sätt att möta denna förfrågan är att använda sig av molnbaserade realtidsanalystjänster. Multi-tenant och single-tenant är två typer av arkitekturer för molnbaserade realtidsanalystjänster som kan användas för att lösa problemen med hanteringen av de ökade datamängderna. Dessa arkitekturer skiljer sig åt när det gäller komplexitet i utvecklingen. I detta arbete representerar Azure Stream Analytics en multi-tenant arkitektur och HDInsight/Storm representerar en single-tenant arkitektur. För att kunna göra en jämförelse av molnbaserade realtidsanalystjänster med olika arkitekturer, har vi valt att använda oss av användbarhetskriterierna: effektivitet, ändamålsenlighet och användarnöjdhet. Vi kom fram till att vi ville ha svar på följande frågor relaterade till ovannämnda tre användbarhetskriterier: • Vilka likheter och skillnader kan vi se i utvecklingstider? • Kan vi identifiera skillnader i funktionalitet? • Hur upplever utvecklare de olika analystjänsterna? Vi har använt en design and creation strategi för att utveckla två Proof of Concept prototyper och samlat in data genom att använda flera datainsamlingsmetoder. Proof of Concept prototyperna inkluderade två artefakter, en för Azure Stream Analytics och en för HDInsight/Storm. Vi utvärderade dessa genom att utföra fem olika scenarier som var för sig hade 2-5 delmål. Vi simulerade strömmande data genom att låta en applikation kontinuerligt slumpa fram data som vi analyserade med hjälp av de två realtidsanalystjänsterna. Vi har använt oss av observationer för att dokumentera hur vi arbetade med utvecklingen av analystjänsterna samt för att mäta utvecklingstider och identifiera skillnader i funktionalitet. Vi har även använt oss av frågeformulär för att ta reda på vad användare tyckte om analystjänsterna. Vi kom fram till att Azure Stream Analytics initialt var mer användbart än HDInsight/Storm men att skillnaderna minskade efter hand. Azure Stream Analytics var lättare att arbeta med vid simplare analyser medan HDInsight/Storm hade ett bredare val av funktionalitet.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

Variability in the prevalence of adult ADHD in treatment seeking substance use disorder patients: results from an international multi-center study exploring DSM-IV and DSM-5 criteria

Background: Available studies vary in their estimated prevalence of attention deficit/hyperactivity disor-der (ADHD) in substance use disorder (SUD) patients, ranging from 2 to 83%. A better understanding ofthe possible reasons for this variability and the effect of the change from DSM-IV to DSM-5 is needed.Methods: A two stage international multi-center, cross-sectional study in 10 countries, among patientsform inpatient and outpatient addiction treatment centers for alcohol and/or drug use disorder patients. Atotal of 3558 treatment seeking SUD patients were screened for adult ADHD. A subsample of 1276 subjects,both screen positive and screen negative patients, participated in a structured diagnostic interview. 5AdultsResults: Prevalence of DSM-IV and DSM-5 adult ADHD varied for DSM-IV from 5.4% (CI 95%: 2.4–8.3) forHungary to 31.3% (CI 95%:25.2–37.5) for Norway and for DSM-5 from 7.6% (CI 95%: 4.1–11.1) for Hungary to32.6% (CI 95%: 26.4–38.8) for Norway. Using the same assessment procedures in all countries and centersresulted in substantial reduction of the variability in the prevalence of adult ADHD reported in previousstudies among SUD patients (2–83% → 5.4–31.3%). The remaining variability was partly explained byprimary substance of abuse and by country (Nordic versus non-Nordic countries). Prevalence estimatesfor DSM-5 were slightly higher than for DSM-IV.Conclusions: Given the generally high prevalence of adult ADHD, all treatment seeking SUD patientsshould be screened and, after a confirmed diagnosis, treated for ADHD since the literature indicates poorprognoses of SUD in treatment seeking SUD patients with ADHD.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Multiple criteria optimization of contemporary logistics distribution network problems

In the contemporary customer-driven supply chain, maximization of customer service plays an equally important role as minimization of costs for a company to retain and increase its competitiveness. This article develops a multiple-criteria optimization approach, combining the analytic hierarchy process (AHP) and an integer linear programming (ILP) model, to aid the design of an optimal logistics distribution network. The proposed approach outperforms traditional cost-based optimization techniques because it considers both quantitative and qualitative factors and also aims at maximizing the benefits of deliverer and customers. In the approach, the AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to some critical customer-oriented criteria. The results of AHP prioritization are utilized as the input of the ILP model, the objective of which is to select the best warehouses at the lowest possible cost. In this article, two commercial packages are used: including Expert Choice and LINDO.

Vector Combinatorial Problems in a Space of Combinations with Linear Fractional Functions of Criteria

The paper considers vector discrete optimization problem with linear fractional functions of criteria on a feasible set that has combinatorial properties of combinations. Structural properties of a feasible solution domain and of Pareto–optimal (efficient), weakly efficient, strictly efficient solution sets are examined. A relation between vector optimization problems on a combinatorial set of combinations and on a continuous feasible set is determined. One possible approach is proposed in order to solve a multicriteria combinatorial problem with linear- fractional functions of criteria on a set of combinations.

Multi-Agent Information Processing and Adaptive Control in Global Telecommunication and Computer Networks

The problems and methods for adaptive control and multi-agent processing of information in global telecommunication and computer networks (TCN) are discussed. Criteria for controllability and communication ability (routing ability) of dataflows are described. Multi-agent model for exchange of divided information resources in global TCN has been suggested. Peculiarities for adaptive and intelligent control of dataflows in uncertain conditions and network collisions are analyzed.

A Method for Solving a Class of Multiple-Criteria Analysis Problems

AMS subject classification: 90C29.

Multi-threaded parallel simulation of non-local non-linear problems in ultrashort laser pulse propagation in the presence of plasma

We describe a parallel multi-threaded approach for high performance modelling of wide class of phenomena in ultrafast nonlinear optics. Specific implementation has been performed using the highly parallel capabilities of a programmable graphics processor. © 2011 SPIE.

Multi-stage scheduling problems with precedence constraints

Abstract not available