Diet of albacore Thunnus alalunga from the waters of Mauritius (western Indian Ocean) inferred from stomach contents and fatty acid analysis

The diet of albacore Thunnus alalunga from their spawning ground in the waters of Mauritius was investigated. The stomach contents of 249 albacore individuals, caught by industrial longliners and artisanal fishers, was analysed, and the dominant prey taxa were selected for lipid and fatty acid (FA) analysis. The FA profiles of prey were compared with those of liver tissue of spawning-capable and post-spawning female albacore, using multivariate analysis. Whereas stomach content analysis identified cephalopods and crustaceans as the dominant prey items in number, FA-profile analysis identified crustaceans and fishes as the most-frequently consumed prey of post-spawning female albacore. In contrast, the FA profiles of spawning-capable albacore and those of prey showed very low similarity. Analysis of the prey suggests that although cephalopods, crustaceans and fishes could all provide albacore with the required lipids and FAs, cephalopod prey seem to be less desirable owing to their lower energy content (i.e. low in total lipid, triacylglycerol, 16:0, 18:0 and 18:1ω9). Instead, the most beneficial food appeared to be locally available fishes, which are more energy-rich and have a higher docosahexaenoic acid/ eicosapentaenoic acid ratio. These results provide new information on the trophic ecology of albacore, promote our understanding of the importance of prey type for successful reproduction of albacore, and highlight the advantages of FA profiling to study diet.     

Extreme wind speeds in the European sector of the Arctic

Variations in extreme wind speed over the European part of the Arctic are studied from the data of meteorological observations, reanalysis, and modeling based on the INM CM4 climate model. It is demonstrated that the extremes determined from the observational data are a mixture of two datasets well simulated by the Weibull distribution. According to the special metaphoric terminology, they are called “black swans” and “dragons.” The analysis of extreme wind speeds based on the reanalysis and INM CM4 data demonstrated that they consist of “black swans” only. This important fact indicates that the models (at least those with medium horizontal resolution) are not able to simulate some essential circulation mechanisms causing the formation of significant anomalies of wind speed. Hence, the problem of direct identification of wind speed extremes based on the atmospheric modeling remains open.     

Secular Variability of the Caspian Sea Level

The variability ofthe level and area ofthe Caspian Sea during the Late Glacial Maximum and Holocene is analyzed. It is found that sea level variations during the period of quasisteady Holocene climate can be represented as a result of the accumulation of small anomalies in the water regime, i.e., as a manifestation of the self-developing system. The model of the Caspian Sea water balance was used to test this hypothesis. Empirical data on various-scale sea level variations are presented, and an attempt to explain their nature is made. The origin of large transgressions and regressions is analyzed.     

Use of artificial neural networks for classification of noisy seismic signals

Automatic identification of noisy seismic events is still a problem. The process involves the analysis of complex relationships between data from different sources. Moreover, there are disturbing factors such as poor signal-to-noise ratio, the presence of accidental bursts of man-made noise, and changes in the amplitude and phase of the waves as they travel through the medium. The amount of observed data increases rapidly, so it is imperative to develop suitable and effective methods for processing and analyzing the influx of big data. Artificial neural networks (ANNs) show promise as a disruptive technology that will accelerate and improve analysis of seismic signals. ANNs are easy to apply, and the results often outperform alternative methods. This paper gives an overview of the highs and lows of neural networks, discusses the possibility of routine processing of seismic signals using ANNs, and presents examples of some interesting applications. It is hoped that researchers who read the article will actively use this technique, because ANNs could become more robust and flexible for solving complex problems that currently cannot be solved by the standard approach.     

俄罗斯贝加尔湖北部 Yoko-Dovyren 层状纯橄岩-橄长岩-辉长岩地块: 结构、成分以及作为矿物原材料的用途

Yoko-Dovyren层状纯橄岩-橄长岩-辉长岩地块位于西伯利亚克拉通南部的一处褶皱构造框架中(俄罗斯贝加尔湖地区北部)。该地块的结构在其厚度最大的中部得到了着重研究。剖面底部主体成分为斜长橄榄岩,并依据内部的堆晶成分变化从下往上可分为五个主要的地层序列:纯橄岩→橄长岩→橄榄辉长岩→橄榄辉长苏长岩→石英辉长苏长岩以及含易变辉石的辉长岩。该地块的矿化包括铜-镍矿化、低硫型富铂族元素(PGE)矿化以及铬铁矿化等。另外,该地块也含多种非金属矿物原材料,如硼矿化、透辉石、各种镁质硅酸盐岩等。它们也包括纯橄岩、异剥橄榄岩和橄长岩,并以较高的品质产出,有望采掘加工成为建筑材料(水泥、混凝土、沥青混凝土和建筑陶瓷)。综合利用矿物原材料可增加矿床价值,并有助于建设环保型采矿工作体系。     

The problems of development of the Kholodninskoye lead and zinc deposit (Northern Baikal region)

We examine the pre-project data related to development of the Kholodninskoye lead and zinc ore deposit from the standpoint of ecological and economic soundness of the proposed production and engineering aspects of the design. Some of the proposals and recommendations are aimed at improving environmental safety of the projected facilities for extraction and benefication of mineral raw materials in conditions of the Central Ecological Zone of the Baikal Natural Territory, a World Natural Heritage Site.     

Earthquake arrival identification in a record with technogenic noise

Analysis of the problem of seismic event identification at an increased level of noise, by the data of a single sensor or local group of sensors, is made. This problem is the key to development of an ultra-short-term warning system of an earthquake that has occurred. Catastrophic damage and human deaths are usually caused by the sudden character of earthquake occurrence. If data processing could be done rapidly (i.e., within 4–5 s), such an earthquake warning would be of value for decreasing human deaths and economic damage. The aim of the present study is to investigate the selection of information features of signals in order to reduce the feature dimensionality and to incorporate the particular features of self-contained systems. Special emphasis is placed on the possibility of neural network techniques for the analysis of class separability in the attribute space. Our system will be helpful for reducing the impact of a disastrous earthquake. The main problems are highlighted and the methods for their solution are discussed; tests are carried out for a test problem, consisting in detection of small earthquakes in a noise-contaminated record. It is intended to develop algorithms and programs for identification of earthquake phase arrivals at an increased level of industrial and other noise. Further work will consist in designing instrumental solutions of the system.     

Detailed spatial modeling of temperature in Moscow

A computer complex is described developed for the detailed temperature forecasting in large megalopolises. It consists of the global and microclimatic models implementing the modeling of the thermal regime within Moscow. To increase the modeling quality, an improved technique is proposed of computation of temperature characteristics within the urban canyon. A spatial resolution of output modeling data reaches 500 m. A comparison with the station data demonstrates that the accuracy of air temperature simulation in Moscow and in its nearest vicinities is quite satisfactory.