Search results for: recurrent event

Authors: Entisar A. Elgmati

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Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model.

Keywords: additive model, cumulative probabilities, infant diarrhoea, recurrent event

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Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Arne Koschel

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This article discusses event monitoring options for heterogeneous event sources as they are given in nowadays heterogeneous distributed information systems. It follows the central assumption, that a fully generic event monitoring solution cannot provide complete support for event monitoring; instead, event source specific semantics such as certain event types or support for certain event monitoring techniques have to be taken into account. Following from this, the core result of the work presented here is the extension of a configurable event monitoring (Web) service for a variety of event sources. A service approach allows us to trade genericity for the exploitation of source specific characteristics. It thus delivers results for the areas of SOA, Web services, CEP and EDA.

Keywords: event monitoring, ECA, CEP, SOA, web services

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Authors: Kwan Hee Han, Jun Woo Lee, Sung Moon Bae, Twae Kyung Park

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ECA (Event-Condition-Action) languages are largely adopted for event processing since they are an intuitive and powerful paradigm for programming reactive systems. However, there are some limitations about ECA rules for processing of complex events such as coupling of event producer and consumer. The objective of this paper is to propose an ECA rule pattern to improve the current limitations of ECA rule, and to develop a prototype system. In this paper, conventional ECA rule is separated into 3 parts and each part is extended to meet the requirements of CEP. Finally, event processing logic is established by combining the relevant elements of 3 parts. The usability of proposed extended ECA rule is validated by a test scenario in this study.

Keywords: complex event processing, ECA rule, Event processing system, event-driven architecture, internet of things

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Authors: Anam Alam, Rahim Jamaluddin Kanji

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With the rapid growth of event in everywhere, event extraction has now become an important matter to retrieve the information from the unstructured data. One of the challenging problems is to extract the event from it. An event is an observable occurrence of interaction among entities. The paper investigates the effectiveness of event extraction capabilities of three software tools that are Wandora, Nitro and SPSS. We performed standard text mining techniques of these tools on the data sets of (i) Afghan War Diaries (AWD collection), (ii) MUC4 and (iii) WebKB. Information retrieval measures such as precision and recall which are computed under extensive set of experiments for Event Extraction. The experimental study analyzes the difference between events extracted by the software and human. This approach helps to construct an algorithm that will be applied for different machine learning methods.

Keywords: event extraction, Wandora, nitro, SPSS, event analysis, extraction method, AFG, Afghan War Diaries, MUC4, 4 universities, dataset, algorithm, precision, recall, evaluation

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Authors: Giovanni Forte, Melania De Falco, Antonio Santo

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On 24th August 2016, central Italy was affected by a Mw 6.0 earthquake, representing the main shock of a long seismic sequence, which had a second shock Mw 6.6 on 26th October and lasts still nowadays. After the event, several field survey were carried out in the affected areas, which is made of historical masonry buildings. The post event reconnaissance missions were aimed at collecting information on the damage states of the buildings, the triggering of the landslides and the relationships with site effects. In this paper, the data collected after the event are analyzed considering the role of the geological and geomorphological setting and the ground motion scenario. The buildings displayed an uneven damage distribution, which was affected by both topographic and stratigraphic amplification. As pertains the landslides, which were the most recurrent among the ground failures, consisted mainly of rock falls and subordinately of translational slides. Finally, the collected knowledge showed a strong contribution of the local geological and geomorphological site condition on the resulting damage.

Keywords: Amatrice earthquake, damage states, landslides, site effects

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Srinivasarao Akuthota, Rajasekhar Pabolu, Bharathi Hepattam

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Purpose: To assess the efficacy of intravitreal Avastin in subjects with recurrent wet AMD and persistent diffuse DME on the basis of OCT. Design: Retrospective, non-comparative, observational study,single center study. Conclusion: The study showed that intravitreal Avastin has an equivalent effect on recurrent AMD and in persistent diffuse DME.

Keywords: age-related macular degeneration (AMD), diffuse diabetic retinopathy (DME), intravitreal Avastin (IVA), optical coherence tomography (OCT)

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Horesh Reinman Netta

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Objectives: To examine the relationship between stressful life events and recurrent major depressive disorders Methods: Three groups of 50 subjects were assessed. One group had a recurrent major depressive disorder with melancholic features; the second group met the criteria for borderline personality disorder, and the third consisted of healthy controls. The Structured Clinical Interview for AXIS I DSM-IV Disorders sand the Structured Clinical Interview for AXIS II DSM-IV Disorders were used for diagnosis. The Israel Psychiatric Epidemiology Research Interview (IPERI) Life Event Scale and the Coddington Life Events Schedule (CLES) were used to measure life events which were confirmed with a confirmatory semi-structured interview. The Beck Depression Inventory and the Satisfaction from Life scales were also administered. Results : The total number of loss-related events in childhood and in the year preceding the first episode was significantly higher in the affective disorder group than in the two control groups. Total number of LE, uncontrolled and independent events were also more common in the depressed patients in the year preceding the first episode. No category of SLE was differentiated among any of the three groups during any period of time following the first depressive episode. Conclusions: SLE play an important role in the onset of affective disorders. There appear to be specific kinds of SLE occurring in childhood and in the year preceding a first episode that have particular significance. SLE may have a lesser role in the maintenance of this illness.

Keywords: modd dosorders, recurrent depression, stress, life events

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Authors: Smita Lakhotia, C. Kew, S. H. M. Siraj, B. Chern

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Up to 50% of those with endometriosis may suffer from infertility due to either distorted pelvic anatomy/impaired oocyte release or inhibit ovum pickup and transport, altered peritoneal function, endocrine and anovulatory disorders, including LUF, impaired implantation, progesterone resistance or decreased levels of cellular immunity. The dilemma continues as to whether the surgery or IVF is the optimal management for such recurrent endometriomas. The core question is whether surgery adds anything of value for infertile women with recurrent endometriosis or not. Complete and detailed information on risks and benefits of treatment alternatives must be offered to patients, giving a realistic estimate of chances of success of repetitive surgery and of multiple IVF cycles in order to allow unbiased choices between different possible optionsAn individualized treatment plan should be developed taking into account patient age, duration of infertility, previous pregnancies and specific clinical conditions and wish.

Keywords: recurrent endometriosis, infertility, oocyte release, pregnancy

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Mohsin Raza, Arne Bilberg, Thomas Ditlev Brunø, Ann-Louise Andersen, Filip SKärin

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Shifting from a dedicated or flexible manufacturing system to a reconfigurable manufacturing system (RMS) requires a significant amount of time, money, and effort. Therefore, it is vital to verify beforehand that the potential reconfigurable solution will be able to achieve the organizational objectives. Discrete event simulation offers the opportunity of assessing several reconfigurable alternatives against the set objectives. This study signifies the importance of using discrete-event simulation as a tool to verify several reconfiguration options. Two different industrial cases have been presented in the study to elaborate on the role of discrete event simulation in the implementation methodology of RMSs. The study concluded that discrete event simulation is one of the important tools to consider in the RMS implementation methodology.

Keywords: reconfigurable manufacturing system, discrete event simulation, Tecnomatix plant simulation, RMS

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Authors: A. A. Behroozpoor, M. M. Mazarei

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In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed.

Keywords: REFERENCES [1] Xia, Y, A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7(6), 1996, pp.1544–1548. [2] Xia, Y., & Wang, J, A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks, 16(2), 2005, pp. 379–386. [3] Xia, Y., H, Leung, & J, Wang, A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I, 49(4), 2002, pp.447–458.B. [4] Q. Liu, Z. Guo, J. Wang, A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks, 26, 2012, pp. 99-109.

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Authors: Rim Frikha, Nouha Bouayed, Afifa Sellami, Nozha Chakroun, Salima Douad, Leila Keskes, Tarek Rebai

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Recurrent pregnancy loss (RPL), classically defined as the occurrence of two or more failed pregnancies, is a serious reproductive problem, in which, chromosomal rearrangements in either carrier are a major cause; mainly the chromosome aneuploidy. This study was conducted to determine the frequency and contribution of X-chromosome aneuploidy in recurrent pregnancy loss. A retrospective study was carried out among 100 couples with more than 2 miscarriages, referred to our genetic counseling. In all the cases the detailed reproductive histories were taken. Chromosomal analysis was performed using RHG banding in peripheral blood. Of a total of 100 couples; 3 patients with a detected X-chromosome aneuploidy were identified with an overall frequency of 3%. Chromosome abnormalities are as below: a Turner syndrome with 45, X/46, XX mosaicism, a 47, XXX, and a Klinefelter syndrome with 46, XY/47, XXY. These data show a high incidence of X-chromosome aneuploidy; mainly with mosaicism; in RPL. Thus, couples with such chromosomal abnormality should be referred to a clinical geneticist with whom the option of pre-implantation genetic diagnosis in subsequent pregnancy should be discussed.

Keywords: aneuploidy, genetic testing, recurrent pregnancy loss, X-chromosome

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Authors: Rajaa Filali, Mohamed Bouhdadi

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This paper presents an incremental formal development of the Wireless Transaction Protocol (WTP) in Event-B. WTP is part of the Wireless Application Protocol (WAP) architectures and provides a reliable request-response service. To model and verify the protocol, we use the formal technique Event-B which provides an accessible and rigorous development method. This interaction between modelling and proving reduces the complexity and helps to eliminate misunderstandings, inconsistencies, and specification gaps. As result, verification of WTP allows us to find some deficiencies in the current specification.

Keywords: event-B, wireless transaction protocol, proof obligation, refinement, Rodin, ProB

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Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

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Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.

Keywords: recurrent neural networks, global solar radiation, multi-layer perceptron, gradient, root mean square error

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Authors: Rajaa Filali, Mohamed Bouhdadi

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This paper presents an incremental development of the Dynamic Host Configuration Protocol (DHCP) in Event-B. DHCP is widely used communication protocol, which provides a standard mechanism to obtain configuration parameters. The specification is performed in a stepwise manner and verified through a series of refinements. The Event-B formal method uses the Rodin platform to modeling and verifying some properties of the protocol such as safety, liveness and deadlock freedom. To model and verify the protocol, we use the formal technique Event-B which provides an accessible and rigorous development method. This interaction between modelling and proving reduces the complexity and helps to eliminate misunderstandings, inconsistencies, and specification gaps.

Keywords: DHCP protocol, Event-B, refinement, proof obligation, Rodin

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Authors: Meida Laely Ramdani, Earmporn Thongkrajai, Dedy Purwito

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Lifestyle is one of the most important factors affecting health. Measurement of lifestyle behaviors is necessary for the identification of causal associations between unhealthy lifestyle and health outcomes. There was many instruments have been measured for lifestyle, but not specific for stroke recurrence. This study aimed to develop a new questionnaire of Lifestyle Adjustment Scale (LSAS) among recurrent stroke patients in Indonesia and to measure the reliability and validity of LSAS. The instrument consist of 33 items was developed from the responses of 30 recurrent stroke patients with the maximum age 60 years. Data was collected during October to November 2015. The properties of the instrument were evaluated by validity assessment and reliability measures. The content validity was judged adequate by a panel of five experts, with the result of I-CVI was 0.97. The Cronbach’s alpha analysis was carried out to measure the reliability of LSAS. The result showed that Cronbach’s alpha coefficient was 0.819. LSAS were classified under the domains of dietary habit, smoking habit, physical activity, and stress management. The results of Cronbach’s alpha coefficient for each subscale were 0.60, 0.39, 0.67, 0.65 and 0.76 respectively. LSAS instrument was valid and reliable therefore can be used as research tool among recurrent stroke patients. The development of this questionnaire has been adapted to the socio-cultural context in Indonesia.

Keywords: LSAS, recurrent stroke patients, lifestyle, Indonesia

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Authors: Sabrine Ammar, Mohamed Tahar Bhiri

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Automatic planning has a de facto standard language called Planning Domain Definition Language (PDDL) for describing planning problems. It aims to formalize the planning problems described by the concept of state space. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Indeed, these tools made it possible to detect errors a posteriori by means of test activity. In this paper, we recommend a formal approach coupling the two languages Event-B and PDDL, for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. Thus, this paper proposes a refinement strategy allowing to obtain reliable PDDL descriptions from an ultimate Event-B model correct by construction. The ultimate Event-B model, correct by construction which is supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool.

Keywords: code generation, event-b, PDDL, refinement strategy, translation rules

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Authors: Arghavan Tonkaboni, Shamsolmolouk Najafi, Mohmmad Taghi Kiani, Mehrzad Gholampour, Touraj Goli

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Introduction: Recurrent aphthous stomatitis (RAS) is a multifactorial recurrent oral lesion; which is an autoimmune disease. TH1 cytokines are the most important etiological factors. Autoimmune thyroid disease (ATD) is one of the most common autoimmune diseases and generally coexists with other autoimmune diseases. This study assessed the prevalence of thyroid disease in patients with recurrent aphthous stomatitis. Materials and Methods: This case control study assessed 100 known RAS patients who were diagnosed clinically by oral medicine specialists; venous blood samples were analyzed for thyroid stimulating hormone (TSH), free triiodothyronine (fT3), total thyroxine (fT4), thyroglobulin, anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-TG) levels. Results: Fifty patients with RAS aged between 18-42 years (28.5±5.8) and 50 healthy volunteers aged 19-45 years (27.3±5.4) participated. In RAS patients, fT3 and TSH levels were significantly higher (P=0.031, P=0.706); however, fT4 level was lower in the RAS group (P=0.447). Anti TG and anti-TPO levels were significantly higher in the RAS group (P=0.008, P=0.067). Conclusion: Our study showed that ATD prevalence was significantly higher in RAS patients. Based on this study, we recommend assessment of thyroid hormones and antibodies in RAS patients.

Keywords: recurrent aphthous stomatitis, thyroid antibodies, thyroid hormone, thyroid autoimmune disease

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Authors: Arijit Basuray, Saibal Chatterjee

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Pulsed Power in the form of Recurrent Surge Generator (RSG) can be used for testing various parameters of Motor or Transformer windings including inter-turn, interlayer insulation. Windings with solid insulation in motor and transformer have many interfaces and undesirable defects, and these defects can be exposed under this nondestructive testing methodology. Due to rapid development in power electronics variable frequency drives (VFD), Dry Type or cast resin Transformer used with PWM Sine wave inverters for solar power, solid insulation system used nowadays are shifting more and more to a high-frequency application. Authors have used the recurrent surge generator for testing winding integrity as well as Partial Discharge(PD) at fast rising voltage enabling PD measurement at closer situation under which the insulation system is supposed to work. Authors have discussed test results on a different system with recurrent surge voltages of different rise time.

Keywords: fast rising voltage, partial discharge, pulsed power, recurrent surge generator, solid insulation

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Authors: Yi-De Liu

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Cultural tourism is one of the largest and fastest growing global tourism markets and the cultures are increasingly being used to promote cities and to increase their competitiveness and attractiveness. One of the major forms of cultural tourism development undertaken throughout Europe has been the staging of a growing number of cultural events. The event of European Capitals of Culture (ECOC) is probably the best example of the new trends of cultural tourism in Europe, which is therefore used in this article to demonstrate some of the key issues surrounding the event-led strategy for cultural tourism development. Based on the experience of the 2008 ECOC Liverpool, UK, the study’s findings point to a number of ways in which the ECOC constitutes a boost for the development of cultural tourism in terms of realising experience economy, enhancing city image, facilitating urban regeneration, promoting cultural production and consumption, as well as establishing partnerships. This study is concluded by drawing some critical factors that event and tourism organisers should consider.

Keywords: cultural tourism, event tourism, cultural event, European capital of culture, Liverpool

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Authors: Fatin Fadhilah Hasib, Dewi Nuraini, Nisful Laila, Muhammad Madyan

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A big political event such as Cabinet reshuffle mostly can affect the stock price positively or negatively, depend on the perception of each investor and potential investor. This study aims to analyze the movement of the market and trading activities which respect to an event using event study method. This method is used to measure the movement of the stock exchange in which abnormal return can be obtained by investor related to the event. This study examines the differences of reaction on abnormal return and trading volume activity from the companies listed in the Jakarta Islamic Index (JII), before and after the announcement of the Cabinet Work Volume II on 27 July 2016. The study was conducted in observation of 21 days in total which consists of 10 days before the event and 10 days after the event. The method used in this study is event study with market adjusted model method that observes market reaction to the information of an announcement or publicity events. The Results from the study showed that there is no significant negative nor positive reaction at the abnormal return and abnormal trading before and after the announcement of the cabinet reshuffle. It is indicated by the results of statistical tests whose value not exceeds the level of significance. Stock exchange of the JII just reflects from the previous stock prices without reflecting the information regarding to the Cabinet reshuffle event. It can be concluded that the capital market is efficient with a weak form.

Keywords: abnormal return, abnormal trading volume activity, event study, political event

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Authors: Majid Mathlouthi, Fethi Lebdi

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Throughout the world, the extent and magnitude of droughts have economic, social and environmental consequences. Today climate change has become more and more felt; most likely they increase the frequency and duration of droughts. An analysis by event of dry event, from series of observations of the daily rainfall is carried out. A daily precipitation threshold value has been set. A catchment localized in Northern Tunisia where the average rainfall is about 600 mm has been studied. Rainfall events are defined as an uninterrupted series of rainfall days understanding at least a day having received a precipitation superior or equal to a fixed threshold. The dry events are constituted of a series of dry days framed by two successive rainfall events. A rainfall event is a vector of coordinates the duration, the rainfall depth per event and the duration of the dry event. The depth and duration are found to be correlated. So we use conditional probabilities to analyse the depth per event. The negative binomial distribution fits well the dry event. The duration of the rainfall event follows a geometric distribution. The length of the climatically cycle adjusts to the Incomplete Gamma. Results of this analysis was used to study of the effects of climate change on water resources and crops and to calibrate precipitation models with little rainfall records. In response to long droughts in the basin, the drought management system is based on three phases during each of the three phases; different measurements are applied and executed. The first is before drought, preparedness and early warning; the second is drought management, mitigation in the event of drought; and the last subsequent drought, when the drought is over.

Keywords: dry spell, precipitation threshold, climate vulnerability, adaptation measures

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Jun Sung Go, Jong Kang Park, Jong Tae Kim

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As semi-conductor manufacturing technology evolves; the single event transient problem becomes more significant issue. Single event transient has a critical impact on both combinational and sequential logic circuits, so it is important to evaluate the soft error tolerance of the circuits at the design stage. In this paper, we present a soft error detecting simulation using scan chain. The simulation model generates a single event transient randomly in the circuit, and detects the soft error during the execution of the test patterns. We verified this model by inserting a scan chain in an 8051 microprocessor using 65 nm CMOS technology. While the test patterns generated by ATPG program are passing through the scan chain, we insert a single event transient and detect the number of soft errors per sub-module. The experiments show that the soft error rates per cell area of the SFR module is 277% larger than other modules.

Keywords: scan chain, single event transient, soft error, 8051 processor

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Authors: Shaban Guma, Naim Bajcinca

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We propose an event-triggered algorithm for the solution of a distributed optimization problem by means of the projected subgradient method. Thereby, we invoke an OFDMA resource allocation scheme by applying an event-triggered sensitivity analysis at the access point. The optimal resource assignment of the subcarriers to the involved wireless nodes is carried out by considering the sensitivity analysis of the overall objective function as defined by the control of vehicle clusters with respect to the information exchange between the nodes.

Keywords: consensus, cross-layer, distributed, event-triggered, multi-vehicle, protocol, resource, OFDMA, wireless

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