A genetic algorithm for dynamic modelling and prediction of activity in document streams

The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.

Intracellular pH and redox states are critical for multiple processes and partly determine cell behavior. Here, we developed a genetically encoded dual-function probe, named p H and redox-sensitive fluorescent protein (pHaROS), for simultaneous real-time detection of changes in redox potential and pH in living cells. pHaROS consists of the Arabidopsis flavin mononucleotide-binding fluorescent protein iLOV and an mKATE variant, mBeRFP. Using pHaROS in Saccharomyces cerevisiae cells, we confirmed that H2O2 raises the overall redox potential of the cell and found that this increase is accompanied by a decrease in cytosolic pH. Furthermore, we observed spatiotemporal pH and redox homeostasis within the nucleus at various stages of the cell cycle in budding yeast (Saccharomyces cerevisiae) during cellular development and responses to oxidative stress. Importantly, we could tailor pHaROS to specific applications, including measurements in different organelles and cell types and the GSH/GSSG ratio, highlighting pHaROS's high flexibility and versatility. In summary, we have developed pHaROS as a dual-function probe that can be used for simultaneously measuring cellular pH and redox potential, representing a very promising tool for determining the cross-talk between intracellular redox- and pH-signaling processes in yeast and mammalian U87 cell.

The purpose of this study was to sonographically assess the cerebral hemodynamic differences and changes after oxygen therapy in healthy youths of different ethnicities in Tibet. Sixty-six healthy young Han visitors and 29 healthy young Tibetan residents were divided into 4 groups. Basic information was collected. Pulsed Doppler sonography was used to record the cerebral hemodynamic parameters for the internal carotid, vertebral, and middle cerebral arteries. The participants were then instructed to inhale oxygen, and basic information and cerebral hemodynamic parameters were recorded at 1, 2, 4, and 8 minutes, respectively. Differences in these parameters between groups were analyzed. In comparisons of the flow parameters between sex-matched groups, the mean resistive index values for the internal carotid, vertebral, and middle cerebral arteries in the Han groups were significantly lower than those in the Tibetan groups (P <. 05). The mean peak systolic velocity, end-diastolic velocity, and mean velocity values for the middle cerebral artery in the Han groups were significantly higher than those in the Tibetan groups (P < .05). After oxygen uptake, there were no significant differences in the mean arterial oxygen saturation, heart rate, mean velocity, and resistive index values between the male groups, and similar changes were found for the arterial oxygen saturation and peak systolic velocity values between female groups after 8 minutes of oxygen uptake (P > .05). Sonography is a useful modality for noninvasive and real-time detection of changes in cerebral hemodynamics and can provide reference values for the prevention and treatment of cerebrovascular diseases.

Living cells survive environmentally stressful conditions by initiating a stress response. We monitored changes in the electrophoretic mobility (EPM) of single, optically trapped yeast cells under hyperosmotic stress conditions using optical tweezers combined with a position detector. We studied the dynamics of the EPM stress response for cells at different phases of the cell cycle.

Many forms of social, information, and communication network activity create large volumes of graph stream data. In such cases, it is often desirable to track interesting properties of the underlying nodes, as they change over time. These dynamic properties can often be represented in the form of time-dependent labels associated with the nodes. Dynamic changes in such node labels may be indicative of important events or patterns of activity. This paper will study the problem of differential classification in graph streams, in which we predict significant classification events; i.e. the changes in classification labels of the nodes. Different from the static collective classification problem, this approach focusses on dynamic and real-time detection of changes in node classification, as opposed to the actual classification of nodes. The differential stream classification problem can also be considered a general form of the nodecentric event detection problem, in which node labels are used in order to supervise the detection process. We present experimental results illustrating the effectiveness of our method.

As a consequence of their sessile nature, plants have developed complex signaling mechanisms to cope with the highly dynamic environment that they live in. In response to external stimuli, plants can utilize various signaling pathways for intercellular communication to engage different metabolic and genetic machineries involved in the plant defense system. Among the many signaling molecules recently uncovered, hydrogen peroxide (H2O2) represents one of the most versatile signaling molecule in plants in response to environmental stresses such as mechanical wounding. Here, we develop an optical nanosensor platform using DNA-wrapped semiconducting single-walled carbon nanotubes (SWNT) to monitor the wound-induced H2O2 signaling pathway in plants. This approach enables non-destructive, real-time detection of changes in endogenous H2O2 level in plants at a remote distance with portable and inexpensive electronics. We demonstrate the versatility of this method to investigate the dynamics between H2O2 and electric signalling pathways in different vegetable crops and plant species. The reversibility of the sensor in vivo and its use to monitor insect feeding for agricultural applications are also demonstrated. In addition, we also developed an auto-propagating wave model to explain the propagation dynamics of H2O2 post-wounding. This newly developed tool offers new opportunities to understand plant defense responses as well as to monitor crop health in real time.

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability.

Abstract. Real-time change detection and analysis of natural disasters is of great importance to emergency response and disaster rescue. Recently, a number of video satellites that can record the whole process of natural disasters have been launched. These satellites capture high resolution video image sequences and provide researchers with a large number of image frames, which allows for the implementation of a rapid disaster procedure change detection approach based on deep learning. In this paper, pixel change in image sequences is estimated by optical flow based on FlowNet 2.0 for quick change detection in natural disasters. Experiments are carried out by using image frames from Digital Globe WorldView in Indonesia Earthquake took place on Sept. 28, 2018. In order to test the efficiency of FlowNet 2.0 on natural disaster dataset, 7 state-of-the-art optical flow estimation methods are compared. The experimental results show that FlowNet 2.0 is not only robust to large displacements but small displacements in natural disaster dataset. Two evaluation indicators: Root Mean Square Error (RMSE) and Mean Value are used to record the accuracy. For estimation error of RMSE, FlowNet 2.0 achieves 0.30 and 0.11 pixels in horizontal and vertical direction, respectively. The error in horizontal error is similar to other algorithms but the value in vertical direction is significantly lower than them. And the Mean Value are 1.50 and 0.09 pixels in horizontal and vertical direction, which are most close to the ground truth comparing to other algorithms. Combining the superiority of computing time, the paper proves that only the approach based on FlowNet 2.0 is able to achieve real-time change detection with higher accuracy in the case of natural disasters.

For problems involving uncertainties in design variables and parameters, a bi-objective evolutionary algorithm (EA) based approach to design optimization using evidence theory is proposed and implemented in this paper. In addition to a functional objective, a plausibility measure of failure of constraint satisfaction is minimized. Despite some interests in classical optimization literature, this is the first attempt to use evidence theory with an EA. Due to EA's flexibility in modifying its operators, nonrequirement of any gradient, its ability to handle multiple conflicting objectives, and ease of parallelization, evidence-based design optimization using an EA is promising. Results on a test problem and two engineering design problems show that the modified evolutionary multi-objective optimization algorithm is capable of finding a widely distributed trade-off frontier showing different optimal solutions corresponding to different levels of plausibility failure limits. Furthermore, a single-objective evidence-based EA is found to produce better optimal solutions than a previously reported classical optimization algorithm. Furthermore, the use of a graphical processing unit (GPU) based parallel computing platform demonstrates EA's performance enhancement around 160–700 times in implementing plausibility computations. Handling uncertainties of different types are getting increasingly popular in applied optimization studies and this EA based study is promising to be applied in real-world design optimization problems.

Rapid urbanization took place in the Pearl River Delta of south China since 1980. Although drastic land use change took place in very short interval within this area, hardly any research has been done on this phenomenon for lacking of available data. Remote sensing is presently the most favorable observation method for land use and land cover change (LUCC) researches. While located in the south of China, the Pearl River Delta suffers from heavy cloud cover for more than half of the year. This makes real-time LUCC monitoring and change detection almost impossible with commonly used optical remote sensing data. In this paper, the orbital highest resolution SAR (Synthetic Aperture Radar) data - Fine Mode Radarsat data was used for trail of short-term land use change detection. Three scenes of repeat-pass Radarsat data was collected over the study area. Although repeat-pass Radarsat enable continuous land use monitoring under all weather condition, images acquired during different time are inevitably affected by seasonal land cover change and variable environmental status such as air humidity and raining. Besides, some significant observation bias might be induced because of the platform and sensor instability. All these variations and instability made short-term land use change detection quite a perplex problem. In this paper, short-term land use change caused by human activity was considered as abnormal phenomena in both spatial and temporal domain in time series images. And a Density-based Anomaly Detection (DBAD) algorithm was designed to detect abnormally changed land parcels in time series Radarsat images. Firstly, totally 3 scenes of fine mode Radarsat images were collected in the study area from January 1st to May 3rd, 2006. Simply stacked temporal images reveal apparent backscattering variation between the three scenes of images, which mainly owes to the fast vegetable growth during the observation period. Then image segmentation was done on the multi-temporal Radarsat images and object features including mean value of backscattering coefficient (Mean), minimal value of backscattering (Min), homogeneity of gray level co-occurrence matrix (GLCMhomo) and dissimilarity of gray level co-occurrence matrix (GLCMdis) were extracted basing on segmented image objects. After that change- vector was constructed for each land objects. In the third step DBAD algorithm was applied to the change vector dataset to detect anomaly change in the 3 scenes of images. Finally field surveying data plus manual interpretation were used for validation. Comparing with object-based image regression method, DBAD results in better accuracy. Besides, data validation also shows that DBAD have better accuracy in both under-constructed area and newly built up area (error lower than 12%). While for built up area and some mixed used area, it gains relatively lower accuracy than other land types (from 10% to 28.57%). To conclude, short-term land use change in time series images could be defined as spatial and temporal anomaly in remote sensing images. By extending traditional anomaly detection to spatial-temporal anomaly detection, land use change caused by human activity could be effectively detected during short time intervals. The algorithm DBAD focus only on the density of change vectors in feature space, which is independent of the amplitude and direction of change vectors. This enable DBAD effectively discriminate temporal image variation caused by observation system, environment or seasonal land cover change, especially in vegetation and cultivated area which changed remarkably during the observation period, from land use change caused by human activities. This helps to decrease the false alarming in short-term change detection. © 2008 SPIE.

In this paper, we propose a population-based, four-step, real-parameter optimization algorithm-generator. The approach divides the task of reaching near the optimum solution into four independent plans of (i) selecting good solutions from a solution base, (ii) generating new solutions using the selected solutions, (iii) choosing inferior or spurious solutions for replacement, and (iv) updating the solution base with good new or old solutions. Interestingly, many classical and evolutionary optimization algorithms are found to be representable by this algorithm-generator. The paper also recommends an efficient optimization algorithm with the possibility of using a number of different recombination plans and parameter values. With a systematic parametric study, the paper finally recommends a real-parameter optimization algorithm which outperforms a number of existing classical and evolutionary algorithms. To extend this study, the proposed algorithm-generator can be utilized to develop new and more efficient population-based optimization algorithms. The treatment of population-based classical and evolutionary optimization algorithms identically through the proposed algorithm-generator is the main hall-mark of this paper and should enable researchers from both classical and evolutionary fields to understand each other’s methods better and interact in a more coherent manner.

To apply univariate and multivariate analyses of EEG spectral features in order to detect subtle post-clamp changes during carotid endarterectomy, and to devise a data-driven, multivariate classification method for rapid, real-time detection of small post-clamp EEG changes. We used intraoperative EEG records of 27 patients who underwent carotid endarterectomy (CEA) and did not undergo a shunt per surgeon's and technologist's visual inspection of the electro-encephalogram (EEG). Furthermore, these records could not be flagged with r-sBSI (revised spatial brain symmetry index) or r-tBSI (revised temporal brain symmetry index) qEEG (quantitative EEG) metrics. Spectral energies of 10s EEG snippets were recorded at 5min intervals from 10min pre-clamp to 20min post-clamp. Using receiver operating characteristic curves, the power of standard EEG spectral bands in detection of post-clamp changes was determined. Next, we employed Fisher Linear Discriminant Analysis for classifier-guided multivariate feature selection and classification. In univariate analysis, delta waves in non-frontal areas, irrespective of the laterality of the affected hemisphere, were found to be generally more powerful indicators of clamp-induced EEG changes. In multivariate analysis, cross-validation results for Fisher LDA produced subject-independent equal error rates as low as 32% when separating post-clamp signals from pre-clamp controls. Within our subject group, the intricate clamp-induced EEG signatures were predominantly anterior, bilateral, and had a strong delta rhythm presence. The overall post-clamp saliencies, both in univariate and multivariate analyses, were time-dependent. By applying automatic data-driven feature extraction and classification to short EEG records, it is possible to construct subject-independent computational models that can detect subtle post clamp changes possibly caused by small perturbations in cerebral blood flow. These subtle changes are missed by visual inspection of the EEG and by other quantitative EEG techniques such as r-sBSI and r-tBSI. Within our subject group, the intricate post-clamp EEG signatures were predominantly anterior, bilateral, and had a strong delta rhythm presence.

Detecting conformational change in protein or peptide is imperative in understanding their dynamic function and diagnosing diseases. Existing techniques either rely on ensemble average that lacks the necessary sensitivity or require florescence labeling. Here we propose to discriminate between different protein conformations with multiple layers of graphene nanopore sensors by measuring the effect of protein-produced electrostatic potential (EP) on electric transport. Using conformations of the octapeptide Angiotensin II obtained through molecular dynamics simulations, we show that the EP critically depends on the geometries of constituent atoms and each conformation carries a unique EP signature. We then, using quantum transport simulations, reveal that these characteristic EP profiles cause distinctive modulation to electric charge densities of the graphene nanopores, leading to distinguishable changes in conductivity. Our results open the potential of label-free, single-molecule, and real-time detection of protein conformational changes.

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world problems rarely exist in isolation, it is expected to alleviate the above issue by properly exploiting the knowledge shared across different problems. In this context, this study proposes a novel evolutionary multitasking optimization algorithm assisted by a classifier rather than a regression model for expensive multitasking problems, where the accuracy of the classifier is boosted by knowledge transfer. To be specific, a support vector classifier (SVC) is first developed and integrated into a classic evolutionary algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES). With a low computational cost, it helps CMA-ES to prescreen parent solutions from the current population. Following that, a knowledge transfer strategy is designed to enrich the training samples for each task-oriented classifier by sharing high-quality solutions among different tasks, where a PCA-based subspace alignment technique is employed. Extensive experiments indicate that the SVC-assisted CMA-ES gains significant superiority over general CMA-ES in terms of both robustness and scalability, and the knowledge transfer strategy further helps it earn a competitive edge over some state-of-the-art algorithms on expensive multitasking optimization problems.

Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.