Simulation of logistics in food retailing for freight transportation analysis

The study contributes to fill the gap between freight transportation analysis and logistic research. It describes the model SYNTRADE, a simulation model that reproduces logistic structures in the German food retailing sector. Logistic decisions and their interdependencies are simulated based on heuristics from the field of logistic optimization. The model provides the possibility to analyze changes in logistics and freight transport demand on a company, as well as on an overall sector level

An Approach to Analyze the Intermodal Rail Transport Market in Germany

This study aims to provide a complete overview of intermodal rail connections in Germany and identify the market players involved in their operation. The lack of a comprehensive overview is attributed to the difficulty of summarizing empirical data of intermodal rail transport, combined with the many rapid changes in the dynamic open market. The study uses a dataset compiled through online research and interviews with market players. The identified market players include intermodal operators, railway carriers, terminals, and ports

An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning

Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs

A statistical analysis of time trends in atmospheric ethane

Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns obtained from ground-based FTIR measurements. As many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above

Amplified Host Defense by Toll-Like Receptor-Mediated Downregulation of the Glucocorticoid-Induced Leucine Zipper (GILZ) in Macrophages

Activation of toll-like receptors (TLRs) plays a pivotal role in the host defense against bacteria and results in the activation of NF-κB-mediated transcription of proinflammatory mediators. Glucocorticoid-induced leucine zipper (GILZ) is an anti-inflammatory mediator, which inhibits NF-κB activity in macrophages. Thus, we aimed to investigate the regulation and role of GILZ expression in primary human and murine macrophages upon TLR activation. Treatment with TLR agonists, e.g., Pam3CSK4 (TLR1/2) or LPS (TLR4) rapidly decreased GILZ mRNA and protein levels. In consequence, GILZ downregulation led to enhanced induction of pro-inflammatory mediators, increased phagocytic activity, and a higher capacity to kill intracellular bacteria (Salmonella enterica serovar typhimurium), as shown in GILZ knockout macrophages. Treatment with the TLR3 ligand polyinosinic: polycytidylic acid [Poly(I:C)] did not affect GILZ mRNA levels, although GILZ protein expression was decreased. This effect was paralleled by sensitization toward TLR1/2- and TLR4-agonists. A bioinformatics approach implicated more than 250 miRNAs as potential GILZ regulators. Microarray analysis revealed that the expression of several potentially GILZ-targeting miRNAs was increased after Poly(I:C) treatment in primary human macrophages. We tested the ability of 11 of these miRNAs to target GILZ by luciferase reporter gene assays. Within this small set, four miRNAs (hsa-miR-34b*,−222,−320d,−484) were confirmed as GILZ regulators, suggesting that GILZ downregulation upon TLR3 activation is a consequence of the synergistic actions of multiple miRNAs. In summary, our data show that GILZ downregulation promotes macrophage activation. GILZ downregulation occurs both via MyD88-dependent and -independent mechanisms and can involve decreased mRNA or protein stability and an attenuated translation

PuraStat in gastrointestinal bleeding: results of a prospective multicentre observational pilot study

Background: A recently developed haemostatic peptide gel for endoscopic application has been introduced to improve the management of gastrointestinal bleeding. The aim of this pilot study was to evaluate the feasibility, safety, efficacy and indication profiles of PuraStat in a clinical setting. Methods: In this prospective observational multicentre pilot study, patients with acute non-variceal gastrointestinal bleeding (upper and lower) were included. Primary and secondary application of PuraStat was evaluated. Haemoglobin, prothrombin time, platelets and transfusion behaviour were documented before and after haemostasis. The efficacy of PuraStat was assessed during the procedure, at 3 days and 1 week after application. Results: 111 patients with acute gastrointestinal bleeding were recruited into the study. 70 percent (78/111) of the patients had upper gastrointestinal bleeding and 30% (33/111) had lower gastrointestinal bleeding. After primary application of PuraStat, initial haemostatic success was achieved in 94% of patients (74/79, 95% CI 88-99%), and in 75% of the patients when used as a secondary haemostatic product, following failure of established techniques (24/32, 95% CI 59-91%). The therapeutic success rates (absence of rebleeding) after 3 and 7 days were 91% and 87% after primary use, and 87% and 81% in all study patients. Overall rebleeding rate at 30 day follow-up was 16% (18/111). In the 5 patients who finally required surgery (4.5%), PuraStat allowed temporary haemostasis and stabilisation. Conclusions: PuraStat expanded the therapeutic toolbox available for an effective treatment of gastrointestinal bleeding sources. It could be safely applied and administered without complications as a primary or secondary therapy. PuraStat may additionally serve as a bridge to surgery in order to achieve temporary haemostasis in case of refractory severe bleeding, possibly playing a role in preventing immediate emergency surgery

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements