Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

Impact of chronic neonicotinoid exposure on honeybee colony performance and queen supersedure

BACKGROUND: Honeybees provide economically and ecologically vital pollination services to crops and wild plants. During the last decade elevated colony losses have been documented in Europe and North America. Despite growing consensus on the involvement of multiple causal factors, the underlying interactions impacting on honeybee health and colony failure are not fully resolved. Parasites and pathogens are among the main candidates, but sublethal exposure to widespread agricultural pesticides may also affect bees. METHODOLOGY/PRINCIPAL FINDINGS: To investigate effects of sublethal dietary neonicotinoid exposure on honeybee colony performance, a fully crossed experimental design was implemented using 24 colonies, including sister-queens from two different strains, and experimental in-hive pollen feeding with or without environmentally relevant concentrations of thiamethoxam and clothianidin. Honeybee colonies chronically exposed to both neonicotinoids over two brood cycles exhibited decreased performance in the short-term resulting in declining numbers of adult bees (-28%) and brood (-13%), as well as a reduction in honey production (-29%) and pollen collections (-19%), but colonies recovered in the medium-term and overwintered successfully. However, significantly decelerated growth of neonicotinoid-exposed colonies during the following spring was associated with queen failure, revealing previously undocumented long-term impacts of neonicotinoids: queen supersedure was observed for 60% of the neonicotinoid-exposed colonies within a one year period, but not for control colonies. Linked to this, neonicotinoid exposure was significantly associated with a reduced propensity to swarm during the next spring. Both short-term and long-term effects of neonicotinoids on colony performance were significantly influenced by the honeybees' genetic background. CONCLUSIONS/SIGNIFICANCE: Sublethal neonicotinoid exposure did not provoke increased winter losses. Yet, significant detrimental short and long-term impacts on colony performance and queen fate suggest that neonicotinoids may contribute to colony weakening in a complex manner. Further, we highlight the importance of the genetic basis of neonicotinoid susceptibility in honeybees which can vary substantially.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Attempted re-establishment of a sooty tern Onychoprion fuscatus breeding colony on Denis Island, Seychelles

Seychelles supports around three million nesting pairs of sooty terns. However, there have been recent declines and the colonies continue to face ongoing threats from habitat change and excessive commercial harvesting of their eggs, as well as potential threats by commercial fishing and climate change. A possible method to counter these threats is to re-establish breeding colonies on islands from which they have disappeared. An attempt was made to attract birds to a previously occupied island through habitat management, decoy birds and playback of recorded sooty tern calls. Habitat preparation involved predator eradication and tree removal to provide open ground with bare sandy areas and low herb vegetation. Overflying birds were attracted by broadcast calls, with some circling over and landing among the decoys. Large three-dimensional plastic models were superior to other models presented. This study demonstrated that large numbers of birds can be attracted by these means and that the birds then undertook behaviour associated with breeding, including egg laying by a few birds. However, after five seasons a breeding colony has not yet been established; one possible cause is the emergence of unexpected egg predators, common moorhen Gallinula chloropus and common myna Acridotheres tristis.

Rapid preconditioning of data for accelerating convex hull algorithms

Given a dataset of two-dimensional points in the plane with integer coordinates, the method proposed reduces a set of n points down to a set of s points s ≤ n, such that the convex hull on the set of s points is the same as the convex hull of the original set of n points. The method is O(n). It helps any convex hull algorithm run faster. The empirical analysis of a practical case shows a percentage reduction in points of over 98%, that is reflected as a faster computation with a speedup factor of at least 4.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Ant larval demand reduces aphid colony growth rates in an ant-aphid interaction

Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown whether demand from within the ant colony can affect the ant-aphid interaction. In a factorial experiment, we tested whether the presence of larvae in Lasius niger ant colonies affected the growth rate of Aphis fabae colonies. Other explanatory variables tested were the origin of ant colonies (two separate colonies were used) and previous diet (sugar only or sugar and protein). We found that the presence of larvae in the ant colony significantly reduced the growth rate of aphid colonies. Previous diet and colony origin did not affect aphid colony growth rates. Our results suggest that ant colonies balance the flow of two separate resources from aphid colonies- renewable sugars or a protein-rich meal, depending on demand from ant larvae within the nest. Aphid payoffs from the ant-aphid interaction may change on a seasonal basis, as the demand from larvae within the ant colony waxes and wanes.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.

Selecting algorithms for Earth observation of climate within the European Space Agency Climate Change initiative: introduction to a special issue

This special issue is focused on the assessment of algorithms for the observation of Earth’s climate from environ- mental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth’s climate system. Producers of data sets must devote considerable effort and expertise to maximise the true climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the climate variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential climate variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temper- ature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as re- quiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observa- tional contexts, and help ensure that scientific conclusions drawn from climate data records are not influenced by observational artifacts, but are robust.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

Colony dehydration and water collection by specialized caste in the leaf-cutting ant Atta sexdens rubropilosa

Social organization enables leaf-cutting ants to keep appropriate micro-ecological nest conditions for the fungus garden (their main food), eggs, larvae and adults. To maintain stability while facing changing conditions, individual ants must perceive destabilising factors and produce a proper behavioral response. We investigated behavioral responses to experimental dehydration in leaf-cutting ants to verify if task specialization exists, and to quantify the ability of ant sub-colonies for water management. Our setup consisted of fourteen sub-colonies, ten of which were randomly assigned to different levels of experimental dehydration with silica gel, whereas the remaining four were controls. The ten experimental sub-colonies were split into two groups, so that five of them had access to water. Diverse ant morphs searched for water in dehydrated colonies, but mainly a caste of small ants collected water after sources had been discovered. Size specialization for water collection was replicable in shorter experiments with three additional colonies. Ants of dehydrated colonies accumulated leaf-fragments on the nest entrance, and covering the fungus garden. Behaviors that may enhance humidity within the nests were common to all dehydration treatments. Water availability increased the life span of dehydrated colonies.

Granulocyte Colony-stimulating Factor Reduces Mortality by Suppressing Ventricular Arrhythmias in Acute Phase of Myocardial Infarction in Rats

Our aim was to evaluate the effects of granulocyte colony-stimulating factor (G-CSF) on early cardiac arrhythmias after myocardial infarction (MI) and the impact on survival. Male Wistar rats received repeated doses of 50 mu g/kg G-CSF (MI-GCSF group) or vehicle (MI group) at 7, 3, and 1 days before surgery. MI was induced by permanent occlusion of left corollary artery. The electrocardiogram was obtained before occlusion and then for 30 minutes after surgery. Events and duration of ventricular arrhythmias were analyzed. The levels of connexin43 (Cx43) were measured by Western blot immediately before MI production. Survival was significantly increased in MI-GCSF pretreated group (74% versus 52.0% MI. P < 0.05). G-CSF pretreatment also significantly reduced the ventricular premature beats when compared with the untreated-MI group (201 +/- 47 versus 679 +/- 117, P < 0.05). The number and the duration of ventricular tachycardia were smaller in the MI-G-CSF group, as well as the number of ventricular fibrillation episodes (10% versus 69% in NIL P < 0.05). Cx43 levels were significantly increased by G-CSF treatment (1.27 +/- 0.13 versus 0.86 +/- 0.11; P < 0.05). The MI size 24 hours after occlusion was reduced by G-CSF pretreatment (36 +/- 3% versus 44 +/- 2% of left ventricle in MI group; P < 0.05). The increase of Cx43 expression in the heart may explain the reduced incidence in ventricular arrhythmias in the early phases after coronary artery occlusion in rats, thus increasing survival after MI.