Inbreeding uncovers fundamental differences in the genetic load affecting male and female fertility in a butterfly

Inbreeding depression is most pronounced for traits closely associated with fitness. The traditional explanation is that natural selection eliminates deleterious mutations with additive or dominant effects more effectively than recessive mutations, leading to directional dominance for traits subject to strong directional selection. Here we report the unexpected finding that, in the butterfly Bicyclus anynana, male sterility contributes disproportionately to inbreeding depression for fitness (complete sterility in about half the sons from brother-sister matings), while female fertility is insensitive to inbreeding. The contrast between the sexes for functionally equivalent traits is inconsistent with standard selection arguments, and suggests that trait-specific developmental properties and cryptic selection play crucial roles in shaping genetic architecture. There is evidence that spermatogenesis is less developmentally stable than oogenesis, though the unusually high male fertility load in B. anynana additionally suggests the operation of complex selection maintaining male sterility recessives. Analysis of the precise causes of inbreeding depression will be needed to generate a model that reliably explains variation in directional dominance and reconciles the gap between observed and expected genetic loads carried by populations. This challenging evolutionary puzzle should stimulate work on the occurrence and causes of sex differences in fertility load.

Estimation of Mycobacterium avium subsp. paratuberculosis load in raw bulk tank milk in Emilia-Romagna Region (Italy) by qPCR

Consumption of milk and dairy products is considered one of the main routes of human exposure to Mycobacterium avium subsp. paratuberculosis (MAP). Quantitative data on MAP load in raw cows’ milk are essential starting point for exposure assessment. Our study provides this information on a regional scale, estimating the load of MAP in bulk tank milk (BTM) produced in Emilia-Romagna region (Italy). The survey was carried out on 2934 BTM samples (88.6% of the farms herein present) using two different target sequences for qPCR (f57 and IS900). Data about the performances of both qPCRs are also reported, highlighting the superior sensitivity of IS900-qPCR. Seven hundred and eighty-nine samples tested MAP-positive (apparent prevalence 26.9%) by IS900 qPCR. However, only 90 of these samples were quantifiable by qPCR. The quantifiable samples contained a median load of 32.4 MAP cells mL−1 (and maximum load of 1424 MAP cells mL−1). This study has shown that a small proportion (3.1%) of BTM samples from Emilia-Romagna region contained MAP in excess of the limit of detection (1.5 × 101 MAP cells mL−1), indicating low potential exposure for consumers if the milk subsequently undergoes pasteurization or if it is destined to typical hard cheese production.

Genomic meningococcal load in the nasopharynx of children with meningococcal disease

This study demonstrates the feasibility of using quantitative real time PCR to measure genomic bacterial load in the nasopharynx of children with invasive meningococcal disease and shows that these loads are exceptionally high (median 6.6 x 105 (Range 1.2 x 105 to 1.1 x 108) genome copies of Neisseria meningitidis per swab).

Incipient turn-to-turn winding fault diagnosis of power transformers by the on-load exciting current Extended Park`s Vector Approach

This paper presents the application of the on-load exciting current Extended Park's Vector Approach for diagnosing incipient turn-to-turn winding faults in operating power transformers. Experimental and simulated test results demonstrate the effectiveness of the proposed technique, which is based on the spectral analysis of the AC component of the on-load exciting current Park's Vector modulus.

Transformers on-load exciting current Park`s vector approach as a tool for winding faults diagnostics

This paper presents the development of a new approach for diagnosing the occurrence of inter-turn short-circuits in the windings of three-phase transformers, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns. Experimental and simulated results demonstrate the effectiveness of the proposed technique for detecting winding inter-turn insulation faults in operating three-phase transformers.

Detection of trasformer intermittent winding faults by the on-load exciting current Park`s vector approach

This paper presents the application of the on-load exciting current Park's Vector Approach for diagnosing permanent and intermittent turn-to-turn winding faults in operating power transformers. First, an experimental investigation of the behaviour of the transformer under the occurrence of both permanent and intermittent winding faults is presented. Finally, experimental test results demonstrate the effectiveness of the proposed diagnostic technique, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns.

EU water framework directive: will the nitrate load reduction from diffuse sources produce the same results in all estuaries?

The impact of urban waste-water and non-point nitrate discharges in estuarine and near-shore coastal waters are analyzed. The study is focused on the effects of applying the European directives 91/271/EEC and 91/676/EEC to these systems. 4 Portuguese estuaries and two coastal lagoons with different characteristics are studied. A modelling system is applied and calibrated in each system. Three nitrate load scenarios are examined. It is shown that the morphologic and hydrodynamic characteristics of the domain largely control the ecological processes in these systems. The primary production limitation factors are split into “biologic” and “hydrodynamic” components. The physical limitation due to hydrodynamic and residence time is the most important factor. The combined limitation of “biologic” factors (temperature, light and nutrients availability) control productivity only in the systems where physical limitation is not important.

An investigation of container train service provision and load factors in Great Britain

The continued growth in the volume of international trade poses considerable economic and sustainability challenges, particularly as transport routes become more congested and concern grows about the role of transport movements in accelerating climate change. Rail freight plays a major role in the inland transport of containers passing through the main British container ports, and potentially could play a more significant role in the future. However, there is little detailed understanding of the nature of this particular rail market, especially in terms its current operating efficiency. This paper examines container train service provision to/from the four main ports, based on analysis of a representative survey of more than 500 container trains between February and August 2007. The extent to which the existing capacity is utilised is presented, and scenarios by which the number of containers carried could be increased without requiring additional train service provision are modelled, to identify the theoretical potential for greater rail volumes. Finally, the paper identifies the challenges involved in achieving higher load factors, emphasising the importance both of wider supply chain considerations and government policy decision-making.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.