Time series of air chemistry measurements at Georg-von-Neumayer station, Dronning Maud Land, Antarctica in the years 1982 to 1991

The first Air Chemistry Observatory at the German Antarctic station Georg von Neumayer (GvN) was operated for 10 years from 1982 to 1991. The focus of the established observational programme was on characterizing the physical properties and chemical composition of the aerosol, as well as on monitoring the changing trace gas composition of the background atmosphere, especially concerning greenhouse gases. The observatory was designed by the Institut für Umweltphysik, University of Heidelberg (UHEIIUP). The experiments were installed inside the bivouac lodge, mounted on a sledge and put upon a snow hill to prevent snow accumulation during blizzards. All experiments were under daily control and daily performance protocols were documented. A ventilated stainless steel inlet stack (total height about 3-4 m above the snow surface) with a 50% aerodynamic cut-off diameter around 7-10 µm at wind velocities between 4-10 m/s supplied all experiments with ambient air. Contamination free sampling was realized by several means: (i) The Air Chemistry Observatory was situated in a clean air area about 1500 m south of GvN. Due to the fact that northern wind directions are very rare, contamination from the base can be excluded for most of the time. (ii) The power supply (20 kW) is provided by a cable from the main station, thus no fuel-driven generator is operated in the very vicinity. (iii) Contamination-free sampling is controlled by the permanently recorded wind velocity, wind direction and by condensation particle concentration. Contamination was indicated if one of the following criteria were given: Wind direction within a 330°-30° sector, wind velocity <2.2 m/s or >17.5 m/s, or condensation particle concentrations >2500/cm**3 during summer, >800/cm**3 during spring/autumn and >400/cm**3 during winter. If one or a definable combination of these criteria were given, high volume aerosol sampling and part of the trace gas sampling were interrupted. Starting at 1982 through 1991-01-14 surface ozone was measured with an electrochemical concentration cell (ECC). Surface ozone mixing ratio are given in ppbv = parts per 10**9 by volume. The averaging time corresponds to the given time intervals in the data sheet. The accuracy of the values are better than ±1 ppbv and the detection limit is around 1.0 ppbv. Aerosols were sampled on two Whatman 541 cellulose filters in series and analyzed by ion chromatography at the UHEI-IUP. Generally, the sampling period was seven days but could be up to two weeks on occasion. The air flow was around 100 m**3/h and typically 10000-20000 m**3 of ambient air was forced through the filters for one sample. Concentration values are given in nanogram (ng) per 1 m**3 air at standard pressure and temperature (1013 mbar, 273.16 K). Uncertainties of the values were approximately ±10% to ±15% for the main components MSA, chloride, nitrate, sulfate and sodium, and between ±20% and ±30% for the minor species bromide, ammonium, potassium, magnesium and calcium.

Table 3.1 Content and composition of air in an ice profile from Camp III i Greenland

The air trapped in freshly formed ice gives information concerning the ice formation processes as weH as concerning severa,l environmental parameters at the time of ice formation. Air arnount, air composition, and the size and form of bubbles may change with time. Possible processes responsible for such changes are discussed. In very cold ice air content and air composition remain almost unchanged. Samples of ancient atmospheric air are therefore very weH preserved in cold ice. In temperate ice changes of the air amount and air composition depend on the intergranular water fiow through the glacier. This waterfiow can be estimated by measuring air amount and air composition in ice sampIes.

Table 3.5 Content and composition of air in fresh ice

Natural ice is formed by freezing of water or by sintering of dry or wet snow. Each of these processes causes atmospheric air to be enclosed in ice as bubbles. The air amount and composition as well as the bubble sizes and density depend not only on the kind of process but also on several environmental conditions. The ice in the deepest layers of the Greenland and thc Antarctic ice sheet was formed more than 100 000 years ago. In the bubbles of this ice, samples of atmospheric air from that time are preserved. The enclosure of air is discussed for each of the three processes. Of special interest are the parameters which control the amount and composition of the enclosed air. If the ice is formed by sintering of very cold dry snow, the air composition in the bubbles corresponds with good accuracy to the composition of atmospheric air.

On the multi-dimensionality and sampling of air transport networks

Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled.

Carcass and meat quality characteristics of Arsi-Bale goats supplemented with different levels of air-dried Moringa stenopetala leaf

This study was conducted to assess the effect of air-dried Moringa stenopetala leaf (MSL) supplementation on carcass components and meat quality in Arsi-Bale goats. A total of 24 yearling goats with initial body weight of 13.6+/-0.25 kg were randomly divided into four treatments with six goats each. All goats received a basal diet of natural grass hay ad libitum and 340 g head^(−1) d^(−1) concentrate. The treatment diets contain a control diet without supplementation (T1) and diets supplemented with MSL at a rate of 120 g head^(−1) d^(−1) (T2), 170 g head^(−1) d^(−1) (T3) and 220 g head^(−1) d^(−1) (T4). The results indicated that the average slaughter weight of goats reared on T3 and T4 was 18.2 and 18.3 kg, respectively, being (P<0.05) higher than those of T1 (15.8 kg) and T2 (16.5 kg). Goats fed on T3 and T4 diets had higher (P<0.05) daily weight gain compared with those of T1 and T2. The hot carcass weight in goats reared on T3 and T4 diets was 6.40 and 7.30 kg, respectively, being (P<0.05) higher than those of T1 (4.81 kg) and T2 (5.06 kg). Goats reared on T4 had higher (P<0.05) dressing percentage than those reared in other treatment diets. The rib-eye area in goats reared on T2, T3 and T4 diets was higher (P<0.05) than those of T1. The protein content of the meat in goats reared on T3 and T4 was 24.0 and 26.4%, respectively being significantly higher than those of T1 (19.1%) and T2 (20.1%). In conclusion, the supplementation of MSL to natural grass hay improved the weight gain and carcass parts of Arsi-Bale goats indicating Moringa leaves as alternative protein supplements to poor quality forages.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.