Collection of data on aboveground invertebrates in the Jena Experiment (time series since 2002)

This collection contains measurements of abundance and diversity of different groups of aboveground invertebrates sampled on the plots of the different sub-experiments at the field site of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. The following series of datasets are contained in this collection: 1. Measurements of ant abundance (number of individuals attracted to baits) and ant occurrence (binary data) in the Main Experiment in 2006 and 2013. Ants where sampled using two types of baited traps receiving ~10g of Tuna or ~10g of honey/Sucrose. After 30min the occurrence (presence = 1 / absence = 0) and abundance (number) of ants at the two types of baits was recorded and pooled per plot.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

County condemnation manual : manual of practice and procedure for the acquisition of real property through the exercise of the power of eminent domain, 1995

This manual contains a summary of acquisition policy and makes recommendations to implement law and policy.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

Continuous acquisition of MHC:peptide complexes by recipient cells contributes to the generation of anti-graft CD8+T cell immunity

Understanding the evolution of the direct and indirect pathways of allorecognition following tissue transplantation is essential in the design of tolerance-promoting protocols. On the basis that donor bone marrow-derived antigen presenting cells are eliminated within days of transplantation, it has been argued that the indirect response represents the major threat to long term transplant survival, and is consequently the key target for regulation. However, the detection of MHC transfer between cells, and particularly the capture of MHC:peptide complexes by dendritic cells, led us to propose a third, semi-direct, pathway of MHC allorecognition. Persistence of this pathway would lead to sustained activation of direct pathway T cells, arguably persisting for the life of the transplant. In this study, we focused on the contribution of acquired MHC class I, on recipient DCs, during the life span of a skin graft. We observed that MHC class I acquisition by recipient DCs occurs for at least one month following transplantation and may be the main source of alloantigen that drives CD8+ cytotoxic T cell responses. In addition, acquired MHC class I-peptide complexes stimulate T cell responses in vivo further emphasizing the need to regulate both pathways to induce indefinite survival of the graft.

Between path dependency and contingency: new challenges for the geography of port system evolution.

Seaports play a critical role as gateways and facilitators of economic interchange and logistics processes and thus have become crucial nodes in globalised production networks andmobility systems. Both the physical port infrastructure and its operational superstructure have undergone intensive evolution processes in an effort to adapt to changing economic environments, technological advances,maritime industry expectations and institutional reforms. The results, in terms of infrastructure, operator models and the role of an individual port within the port system, vary by region, institutional and economic context. While ports have undoubtedly developed in scale to respond to the changing volumes and structures in geographies of trade (Wilmsmeier, 2015), the development of hinterland access infrastructure, regulatory systems and institutional structures have in many instances lagged behind. The resulting bottlenecks reflect deficits in the interplay between the economic system and the factors defining port development (e.g. transport demand, the structure of trade, transport services, institutional capacities, etc. cf. Cullinane and Wilmsmeier, 2011). There is a wide range of case study approaches and analyses of individual ports, but analyses from a port system perspective are less common, and those that exist are seldom critical of the dominant discourse assuming the efficiency of market competition (cf. Debrie et al., 2013). This special section aims to capture the spectrum of approaches in current geography research on port system evolution. Thus, the papers reach from the traditional spatial approach (Rodrigue and Ashar, this volume) to network analysis (Mohamed-Chérif and Ducruet, this volume) to institutional discussions (Vonck and Notteboom, this volume; Wilmsmeier and Monios, this volume). The selection of papers allows an opening of discussion and reflection on current research, necessary critical analysis of the influences on port systemevolution and,most importantly, future directions. The remainder of this editorial aims to reflect on these challenges and identify the potential for future research.

Catalogue of data and platforms at Network GDAC level, including the example of Copernicus In Situ TAC

This document presents catalogue techniques used at network GDAC level to facilitate the discovery of platforms and data files. Some AtlantOS networks are organized as DAC-GDACs that continuously update a catalogue of metadata on observation datasets and platforms: • A DAC is a Data Assembly Centre operating at national or regional scale. It manages data and metadata for its area with a direct link to Scientifics and Operators. The DAC pushes observations to the network GDAC. • A GDAC is a Global Data Assembly Centre. It is designed for a global observation network such as Argo, OceanSITES, DBCP, EGO, Gosud, etc… The GDAC aggregates data and metadata of an observation network, in real-time and delayed mode, provided by DACs.

The acquisition of adjunct control: grammar and processing

This dissertation uses children’s acquisition of adjunct control as a case study to investigate grammatical and performance accounts of language acquisition. In previous research, children have consistently exhibited non-adultlike behavior for sentences with adjunct control. To explain children’s behavior, several different grammatical accounts have been proposed, but evidence for these accounts has been inconclusive. In this dissertation, I take two approaches to account for children’s errors. First, I spell out the predictions of previous grammatical accounts, and test these predictions after accounting for some methodological concerns that might have influenced children’s behavior in previous studies. While I reproduce the non-adultlike behavior observed in previous studies, the predictions of previous grammatical accounts are not borne out, suggesting that extragrammatical factors are needed to explain children’s behavior. Next, I consider the role of two different types of extragrammatical factors in predicting children’s non-adultlike behavior. With a new task designed to address the task demands in previous studies, children exhibit significantly higher accuracy than with previous tasks. This suggests that children’s behavior has been influenced by task- specific processing factors. In addition to the task, I also test the predictions of a similarity-based interference account, which links children’s errors to the same memory mechanisms involved in sentence processing difficulties observed in adults. These predictions are borne out, supporting a more continuous developmental trajectory as children’s processing mechanisms become more resistant to interference. Finally, I consider how children’s errors might influence their acquisition of adjunct control, given the distribution in the linguistic input. I discuss the results of a corpus analysis, including the possibility that adjunct control could be learned from the input. The kinds of information that could be useful to a learner become much more limited, however, after considering the processing limitations that would interfere with the representations available to the learner.

Effects of two teaching interventions on nursing students’ acquisition of competence in ECG interpretation

Purpose: Nurse ability to recognise patient arrhythmias could contribute to preventing in-hospital cardiac arrest. Research suggests that nurses and nursing students lack competence in electrocardiogram (ECG) interpretation. The aim of this study was to compare the effects of two training strategies on nursing students’ acquisition of competence in ECG interpretation. Materials and methods: A controlled randomised trial with 98 nursing students. Divided in groups of 12–16, participants were randomly allocated to one of the following 3-h teaching intervention groups: 1) traditional instructor-led (TILG), and 2) flipped classroom (FCG). Participants’ competence in ECG interpretation was measured in terms of knowledge (%), skills (%) and self-efficacy (%) using a specifically designed and previously validated toolkit at pre-test and post-test. Two-way MANOVA explored the interaction effect between ‘teaching group’ and ‘time of assessment’ and its impact on participants’ competence. Within-group differences at pre-test and post-test were explored by carrying out paired t-tests. Between-group differences at pre- and post-test were examined by performing independent t-test analysis. Results: There was a statistically significant interaction effect between ‘teaching group’ and ‘time of assessment’ on participants’ competence in ECG interpretation (F(3,190) = 86.541, p = 0.001; Wilks’ Λ = 0.423). At pre-test, differences in knowledge (TILG = 35.12 ± 12.07; FCG = 35.66 ± 10.66), skills (TILG = 14.05 ± 10.37; FCG = 14.82 ± 14.14), self-efficacy (TILG = 46.22 ± 23.78; FCG = 40.01 ± 21.77) and all other variables were non-significant (p > 0.05). At post-test, knowledge (TILG = 55.12 ± 14.16; FCG = 94.2 ± 7.31), skills (TILG = 36.90 ± 16.45; FCG = 86.43 ± 14.32) and self-efficacy (TILG = 70.78 ± 14.55; FCG = 79.98 ± 10.35) had significantly improved, regardless of the training received (p < 0.05). Nonetheless, participants in the FCG scored significantly higher than participants in the TILG in knowledge, skills and self-efficacy (p < 0.05). Conclusion: Flipping the classroom for teaching ECG interpretation to nursing students may be more effective than using a traditional instructor-led approach in terms of immediate acquisition of competence in terms of knowledge, skills and self-efficacy. Further research on the effects of both teaching strategies on the retention of the competence will be undertaken.