A net export-led downturn?

Exports ; Germany ; Japan

A System Dynamics Simulation Approach to Container Terminal Management

The container terminal operates under a competitive and dynamic environment where every container terminal continuously seeks to secure a competitive advantage. One of the competitive advantages is the ability to turnaround vessels within the shortest time period. However, this ability very much depends on the overall efficiency of the container terminal operations itself. The planning, decision making and operation of the berth and container yard are crucial in order to ensure the whole container terminal operates in an efficient and timely manner. However, due to the complexity of the container terminal operation, decision making and planning in the berth and yard subsystems are very challenging. This research presents the application of system dynamics simulation into capturing the relationship and interdependency between the berth and yard operation. The system dynamics model reveals that both quay crane moves and prime mover traveling distances have an impact on the berth occupancy rate. Besides that, the system dynamics model also provides capacity planning by allowing the experimentation of the impact on the increase in container throughput, vessel arrival and vessel size on the container terminal operation. This research contributes at bridging the gap between the literatures by developing a model that is capable of capturing the relationship and interdependency between the berth and yard operation as well as incorporating both operational and strategic level issues at the container terminal. This research also benefits the container terminal management through the development of Microworlds. Microworlds is capable of aiding terminal managers on planning and decision making as well as serving as a learning tool where the managers can gain insight to the complexity of the terminal operations

Effects of Undesired Online Video Advertising Choice on User Behavior and Attitude

Although online video advertising is currently a pervasive medium, its effectiveness is still in great doubt. This study examines the effects of undesired choice on user behavior and attitude in the context of online video advertising. We propose that offering people a choice of video advertisements will motivate them into paying more attention to the chosen advertisement, which in turn leads to better memory of the information contained in the advertisement. Additionally, the choosing behavior will encourage viewers to form a favorable attitude towards the chosen video advertisement and their purchase intention towards the advertised product will also be enhanced. We posit that differentiability of choice-set is able to moderate the choice effect. This work is one of the first to investigate the impact of making an undesired choice regarding video advertisements. It extends our understanding of the impact of choice and presents significant implications for both researchers and practitioners

Simulated Annealing Determination Of Shear Wave Travel Time

The method of simulated annealing is introduced to obtain relative moveouts between different depths from an iso-offset section. This method has been shown to be more consistent than conventional picks based on peaks, troughs, or zero crossings especially in situations where the signal-to-noise ratio is low or the wavelet is emergent. This method also provides a means of quantifying the relative confidence in each pick over the entire depth of the well. The method has been applied to the data obtained by the ARCO shear wave logging tool and compared favorably with more conventional estimates of shear wave slowness and was shown to be robust, even in areas of weak arrivals.Massachusetts Institute of Technology. Full Waveform Acoustic Logging Consortiu

Two-loop parameter relations between dimensional regularization and dimensional reduction applied to SUSY-QCD

The two-loop relations between the running gluino-quark-squark coupling, the gluino and the quark mass defined in dimensional regularization (DREG) and dimensional reduction (DRED) in the framework of SUSY-QCD are presented. Furthermore, we verify with the help of these relations that the three-loop beta-functions derived in the minimal subtraction scheme combined with DREG or DRED transform into each other. This result confirms the equivalence of the two schemes through three-loops, if applied to SUSY-QCD.Comment: 14 pages, Latex; v2 matches published versio

Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle.

BackgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.MethodsCarcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation.ResultsMethods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms.ConclusionsSingle-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases