Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.     

Synthesis,micellization and cytotoxic studies of ester-functionalized imidazolium gemini surfactants

The fatty acid-based imidazolium gemini surfactants containing hydroxy-substituted spacers were synthesized and characterized by different spectral techniques. The micellization behavior of product compounds was studied by determining their critical micelle concentration (cmc) values from steady state fluorescence and conductivity measurements. The thermodynamic parameters of micellization of synthesized gemini surfactants confirmed that the micelle formation is a thermodynamically favorable, endothermic and an entropy driven phenomenon. The surfactants were further analyzed for their cytotoxic nature by treating them upon human embryonic kidney cell (HEK-293) line and were found to be significantly low cytotoxic. The interesting features like low cmc values, non-cytotoxic and eco-friendly nature of the gemini surfactants may offer new and more efficient substitutes to the conventional drug delivery systems. Thus the gemini surfactants may serve as promising candidates in the biomedical and pharmaceutical fields.     

Potential of selected infant food formulas for production of Bacillus cereus emetic toxin, cereulide

Cereulide producing Bacillus cereus was isolated from randomly chosen commercial infant foods. The cereulide production in infant food formulas was investigated. When the reconstituted foods were inoculated with >10(5) cfu ml(-1) of cereulide producing B. cereus, 2 to 200 microg of cereulide per 100 ml of food accumulated during 24 h of non-refrigerated storage. The amount of cereulide measured in the foods by the accurate chemical assay (LC-MS) matched with that found by sperm micro assay, proving the cereulide was the sole heat stable toxin in the foods and present in its toxic form. The infant formulas containing both cereal and dairy ingredients were the most supportive for cereulide production. Cereulide accumulation was affected by the infant food composition as well as by the handling of the food. Diluting the reconstituted food with water resulted in increased toxin production expressed as mug per volume. More cereulide was accumulated when the food was incubated stationary compared with moderate shaking. The amount of cereulide accumulated within 24 h at room temperature per 100 ml of cereal and dairy or in rice-nondairy reconstituted infant formulas, inoculated with >or=10(5) cfu ml(-1) of B. cereus strain F4810/72, was higher or similar to the amounts reported for foods implicated in emetic type of food poisonings. Thus mishandling and temperature abuse of infant foods may cause food poisoning when emetic B. cereus is present.     

A comparison between neural-network forecasting techniques-casestudy: river flow forecasting

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of the paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize time series as a benchmark to compare between several neural-network forecasting methods. We compare four different methods to preprocess the inputs and outputs, including a novel method proposed here based on discrete Fourier series. We also compare three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform a longer horizon forecast, and that includes ways to partition the problem into several subproblems of forecasting K steps ahead.     

A control chart for monitoring the lognormal process variation using repetitive sampling

Control charts are developed to make the specific quality measures for a successful production process and follow normal distribution behaviors. But some real-life practices do not match such practices and exhibit some positively skewed behavior like lognormal distribution. The present study has considered this situation and proposed a monitoring control chart based on lognormal process variation using a repetitive sampling scheme. This concept proved better for detecting shifts as quickly as possible, and compared with the existing concept, results are elaborated through extensive tables. The average run lengths and standard deviations of the run lengths are being used as a performance evaluation measures and computed by using Monte Carlo simulations performed in R language. A real-life situation has been discussed in the example section to strengthen the proposed control chart concept in a real-life situation.     

Improved Heuristics for the Minimum Label Spanning Tree Problem

Given a connected, undirected graph G whose edges are labeled, the minimum label (or labeling) spanning tree (MLST) problem seeks a spanning tree on G with the minimum number of distinct labels. Maximum vertex covering algorithm (MVCA) is a well-known heuristic for the MLST problem. It is very fast and performs reasonably well. Recently, we developed a genetic algorithm (GA) for the MLST problem. The GA and MVCA are similarly fast but the GA outperforms the MVCA. In this paper, we present four modified versions of MVCA, as well as a modified GA. These modified procedures generate better results, but are more expensive computationally. The modified GA is the best performer with respect to both accuracy and running time     

Formulation development and evaluation of nifedipine as pylorospasm inhibitor

Introduction: In this study, different nifedipine-loaded formulations were prepared to treat pylorospasm, a sphincter muscle disorder characterized by delayed gastric emptying process. The efficacy of formulation was evaluated in patients by subjective assessment, gamma scintigraphic approaches, and confocal microscopy.

Methods: Nifedipine-loaded different formulations such as sucrose bead, pellets, and microparticles (slugging method, ionotropic gelation, and chemical denaturation) were designed. The studies were performed on 50 subjects, of which 30 subjects were treated with optimized nifedipine loaded microcapsules while 20 subjects were given capsule becosule-Z as a control. The efficacy of formulation was assessed by comparing symptoms like dyspepsia, abdominal pain, abdominal fullness, poor appetite, nausea, vomiting, and irregular motion. The effectiveness of formulation was also assessed by gamma scintigraphic studies by determining the rate of emptying of a radioactivity labeled standard meal from patients’ stomach into the duodenum. Confocal microscopy was used to assess targeting potential of developed formulation.

Results: Drug-loaded alginate-chitosan microcapsules were found to be satisfactory, in terms of controlled drug release, surface morphology, and bioadhesive properties and thus selected for in vivo studies. Clinical studies revealed the efficacy of formulation in abolishing various GI symptoms at high altitude. Associated symptoms such as dyspepsia, abdominal pain, poor appetite, nausea, vomiting, and irregular motion were recovered by 75, 62, 76.5, 86.7, 85.7, and 37.5%, respectively in nifedipine-treated patients. In comparison, 73.7, 40, 33.3, 40, 20, and 0% recoveries were observed in patients given control treatment only. Gamma Scintigraphic studies in lab also revealed 2.425?±?0.245 (p?Conclusions: Results strongly suggest that nifedipine loaded mucoadhesive formulation has a targeting potential which accelerates gastric emptying process in gastroparesis patients, and thus the formulation might prove useful as a potent prokinetic agent.     

Application and comparison of computational intelligence techniques for optimal location and parameter setting of UPFC

Unified power flow controller (UPFC) is one of the most effective flexible AC transmission systems (FACTS) devices for enhancing power system security. However, to what extent the performance of UPFC can be brought out, it highly depends upon the location and parameter setting of this device in the system. This paper presents a new approach based on computational intelligence (CI) techniques to find out the optimal placement and parameter setting of UPFC for enhancing power system security under single line contingencies (N?1 contingency). Firstly, a contingency analysis and ranking process to determine the most severe line outage contingencies, considering lines overload and bus voltage limit violations as a performance index, is performed. Secondly, a relatively new evolutionary optimization technique, namely: differential evolution (DE) technique is applied to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach and for comparison purposes, simulations are performed on an IEEE 14-bus and an IEEE 30-bus power systems. The results, we have obtained, indicate that DE is an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA) and particle swarm optimization (PSO). Installing UPFC in the optimal location determined by DE can significantly enhance the security of power system by eliminating or minimizing the number of overloaded lines and the bus voltage limit violations.     

Improving utilization of reconfigurable resources using two-dimensional compaction

Partial reconfiguration allows parts of the reconfigurable chip area to be configured without affecting the rest of the chip. This allows placement of tasks at run time on the reconfigurable chip. Area management is a very important issue which highly affect the utilization of the chip and hence the performance. This paper focuses on a major aspect of moving running tasks to free space for new incoming tasks (compaction). We study the effect of compacting running tasks to free more contiguous space on the system performance. First, we introduce a straightforward compaction strategy called Blind compaction. We use its performance as a reference to measure the performance of other compaction algorithms. Then we propose a two-dimensional compaction algorithm called one-corner compaction. This algorithm runs with respect to one chip corner. We further extend this algorithm to the four corners of the chip and introduce the four-corners compaction algorithm. Finally, we compare the performance of these algorithms with some existing compaction strategies: Brebner, G. and Diessel, O. (Proceedings of the 11th international workshop on field programmable gate arrays (FPL), pp. 182–191, 2001); Diesel, O. and ElGindy, H. (Proceedings of the 5th Australasian conference on parallel and real-time systems (PART), pp. 191–200, 1998); Diesel, O., et al. (IEE proceedings on computers and digital techniques, vol. 147, pp. 181–188, 2000). The simulation results show improvement in average task allocation time when using the four-corners compaction algorithm by 15% and in chip utilization by 16% over the Blind compaction. These results outperform the existing strategies.

A divide-and-conquer local search heuristic for data visualization

Data visualization techniques have become important tools for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. Typically, these visualization applications are modeled and solved using nonlinear optimization techniques. In this paper, we propose a discretization of the data visualization problem that allows us to formulate it as a quadratic assignment problem. However, this formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of a local search technique for the data visualization problem. The space in which the data points are to be embedded can be discretized using an n×n$n\times n$ lattice. Conducting a local search on this n×n$n\times n$ lattice is computationally ineffective. Instead, we propose a divide-and-conquer local search approach that refines the lattice at each step. We show that this approach is much faster than conducting local search on the entire n×n$n\times n$ lattice and, in general, it generates higher quality solutions. We envision two uses of our divide-and-conquer local search heuristic: (1) as a stand-alone approach for data visualization, and (2) to provide a good approximate starting solution for a nonlinear algorithm.