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Building on insights from upper echelon theory, and inspired by the growing interest in bright-side personality, we examine the relationship between CEO humility and the firm’s entrepreneurial orie...

Orbital cellulitis is a potentially life-threatening and an uncommon emergency condition in children. There are little published data from incidence and prevalence in the developing country. This study was done to identify incidence, etiology and management of orbital cellulitis in children from two service hospitals in east and west coast of India. Methods : The two multispecialty service hospitals cater tertiary level care to dependant population with 80700 children between ages 1 month to 12 years. This study was a 2 years retrospective observational study using hospital based electronic information system and electronic case records from Jan 2017 to Dec 2018 between ages of 1 month to 12 years. Results : A total of 15 children with orbital cellulitis were included in the study giving incidence of 9.29 per 100000 in children

Aims The number of elderly persons with end-stage renal disease is increasing with many requiring hospitalizations. This study examines the causes and predictors of hospitalization in older haemodialysis patients. Methods We reviewed hospitalizations of older (≥65 years) incident chronic haemodialysis patients initiating therapy between 1 January 2007 and 31 December 2009 under the care of a single Midwestern United States dialysis provider. Results Of 125 patients, the mean age was 76 ± 7 years and 72% were male. At first dialysis, 68% used a central venous catheter (CVC) and 51% were in the hospital. Mean follow-up was 1.8 ± 1.0 years. At least one hospitalization occurred in 89 (71%) patients and half of all patients were hospitalized once within the first 223 days. Total hospital admission rate was 1.48 per patient year with hospital days totalling 8.54 days per patient year. The three most common reasons for first admission were cardiac (33%), infection (18%) and gastrointestinal (12%). Predictors of future hospitalization included the first dialysis occurring in hospital (hazard ratios (HR) 2.1, 95% CI 1.4–3.3, P = 0.0005) and the use of a CVC at first haemodialysis (HR 2.6, CI 1.6–4.4, P

Decision Tree is a tree-structured plan of a set of attributes to test in order to predict the output. MapReduce and Spark is a programming model used for processing data on a distributed file system. In this paper, MapReduce and Spark implementation of Decision Tree is named as Distributed Decision Tree (DDT) and Spark Tree (ST) respectively. Decision Tree (DT), Ensemble of Trees (BT), DDT and ST are compared over accuracy, size of tree and number of leaves of tree(s) generated. DDT and ST is empirically evaluated over 10 selected datasets. Using DDT, size of tree is reduced by 71% and 82% as compared to DT and BT respectively. In case of ST size of tree is reduced by 48% and 67% as compared to DT and BT. Number of leaves is reduced by 70% and 81% with respect to DT and BT, respectively using DDT. Whereas, it is reduced by 45% and 65% with respect to DT and BT in case of ST. We evaluated DDT and ST using Yahoo! Webscope dataset. Our evaluation shows improvement in accuracy as well as reduction in size of tree and number of leaves. Hence, DDT and ST outperformed DT and BT with respect to size of tree and number of leaves with adequate classification accuracy.

The primary focus of this paper is to detect disease and estimate its stage for a cotton plant using images. Most disease symptoms are reflected on the cotton leaf. Unlike earlier approaches, the novelty of the proposal lies in processing images captured under uncontrolled conditions in the field using normal or a mobile phone camera by an untrained person. Such field images have a cluttered background making leaf segmentation very challenging. The proposed work use two cascaded classifiers. Using local statistical features, first classifier segments leaf from the background. Then using hue and luminance from HSV colour space another classifier is trained to detect disease and find its stage. The developed algorithm is a generalised as it can be applied for any disease. However as a showcase, we detect Grey Mildew, widely prevalent fungal disease in North Gujarat, India.

An otherwise healthy, full-term neonate presented at day 15 of life to the pediatric emergency with generalized papulo-pustular rash for 2 d. This was finally diagnosed as bullous impetigo caused by Staphylococcus aureus (S. aureus). The skin lesions decreased significantly after starting antibiotic therapy and drainage of blister fluid. There was no recurrence of the lesions on follow-up. This case of generalized pustular eruption due to S. aureus in a neonate is reported, as it poses a diagnostic dilemma and can have serious consequences if left untreated.

In Indian, canal irrigation sector is one of the most important sources of water for the farmers which accounts to 21.7 percent of the total irrigated area of the country. According to the planning commission about 300000 crores rupees have been invested till 2011 into the infrastructure development. Even after such huge investments into the sector, the service provision is in complete disarray. Water distribution has been a major concern, with unequal distribution patterns in the command area. There is a huge communication gap between the irrigation department, Water User Associations and the farmers, about issues such as time of release of water to the quantity that will be made available to them. There are hardly any mechanisms in place to get feedback from the farmers on whether water is being delivered to them or not. This has led to a complete lack of accountability in the sector and has made farmers lose trust from the sector, and has forced farmers to look for irrigation from other private sources in turn has increased the cost of cultivation. The paper looks at how ICT can be used to build back the lost trust into the system by developing a farmer centric water governance system and use of crowd sourcing techniques to improve the communication and accountability of the irrigation department with the farmers.

Application development for Cyber-Physical Systems (CPSs) is challenging because it involves dealing with the heterogeneity that exists both in Physical and Cyber worlds. Second, stakeholders involved in the application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment. Given these, a critical challenge is to enable an application development in CPSs with effectively and efficiently from various stakeholders. Several approaches to tackling this challenge have been proposed in the fields of Wireless Sensor Networks (WSN) and Pervasive Computing, regarded as precursors to the modern day of CPSs. However, existing approaches only cover limited subsets of the above-mentioned challenges when applied to the CPSs. In view of this, the contribution of this paper is twofold. First, we have built upon our existing framework and evolved it into a framework for developing CPSs, with substantial additions and enhancements in high-level modeling languages and their integration into the framework. Second, we present a comparative evaluation results with existing approaches. This provides the CPS community for further benchmarking. The evaluation is carried out on real devices exhibiting heterogeneity. Our experimental analysis and results demonstrate that our approach drastically reduces development effort for CPSs compared to existing approaches.

Cloud computing is the Internet based computing to deliver service. The major components to establish cloud are distributed systems, service oriented computing, web2.0, virtualization and utility computing. The integration of these components are required to make available data anytime and anywhere. Migration of virtual machines is the key issue to manage heterogenous cloud for load balancing. The migration is performed to accommodate the VM of highly loaded server to lightly loaded server. Pre-copy and post-copy are two migration approaches. Pre-copy is the iterative based algorithm which has better performance over post-copy due to its effective execution process for transferring pages. The outcome of pre-copy algorithm is dependent on the dirty and skip models. Dirty model is useful for identifying those pages which are being dirtied for the number of iterations. Skip model is the special case of dirty page mechanism through which consecutive iterations can avoid sending dirty pages for current iteration using skip bitmap of Xen. The skip bitmap is evaluated based on Writable Working Set (WWS) of any migration process. The proposed skip algorithm is developed to handle WWS optimally to improve performance of live migration system. The results show the state of the art to gain 91% accuracy using proposed skip model.

Internet of Things (IoT) application development is technically challenging and complex due to several factors such as the presence of different IoT protocols, interoperability issues, lack of industry standards and dealing with the heterogeneity that exists both in Physical and Internet worlds. Furthermore, developers involved in the IoT application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment (deployment complexities due to the possibilities of wide range of devices). Manual effort involved in all above three phases for heterogeneous devices is a time-consuming and error-prone process. We demonstrate IoTSuite which is an application development platform consisting of a suite of tools motivated by the need to simplify IoT application development. The IoTSuite supports several features such as automatic code generation and integration with a set of modeling languages. It takes high-level specification as input that abstract heterogeneity related complexity. It integrates compiler and deployment module to provide automation at different phases of application development process.

This paper addresses the linkage between large scale government program (“Digital India Program” and “Make in India Program”) and psychological factors contributing to entrepreneurship development. The proposed research framework denotes linkage between large scale government program, psychological factors and entrepreneurship development. It adds incremental information to the existing research studies and frameworks. Based on primary research, the authors recommend top three factors (“Creative Personality”, “Need for Achievement” and “Need for Economic Independence”) important and correlated for entrepreneurship development among students. The statistical analysis of responses from the two survey shows that large scale government program does encourage these factors. The program also brings in favourable attitude towards student entrepreneurship. Thus, these programs shall explore and get better support from educational institutes and the student community. Authors urge an increase in program volume of “tangible” and “intangible” benefits and make them alluring to student entrepreneurs. In addition, awareness via advertisement and seminars shall increase. The jobs and skills component of these programs shall include entrepreneurship skills and free IT tools/resources for entrepreneurs.

The central nervous system (CNS) is among the most frequent and main target of HIV infection in severely immunocompromised patients. Neurological manifestations occur due to either primary pathologic process of HIV or secondary to opportunistic infection. The present study was conducted to ascertain the prevalence of various neurological manifestations in HIV positive patients, correlation of CD4 levels in CNS opportunistic infection and their outcome. This was a prospective observational study of 105 HIV infected patients with clinical evidence of CNS involvement. A detailed clinical history and CNS examination was carried out. CD4counts was measured by flow cytometry method and other investigations like magnetic resonance imaging (MRI), brain/electromyography, nerve conduction studies and cerebrospinal fluid (CSF) examination were done as required for diagnosis. HIV induced primary illness was present in about 30% cases while 70% associated with secondary CNS manifestations were mainly due to opportunistic infection. The most common primary illness was distal symmetrical polyneuropathy (20.9%), followed by AIDS dementia complex (3.8%), acute inflammatory demyelinating neuropathy (3.8%). On the other hand, the most common secondary CNS infection was tuberculous bacterial (TBM; 32.3%), followed by cryptococcal meningitis (13.3%), progressive multifocal leukoencephalopathy (PML; 11.4%), and cerebral toxoplasmosis (9.5%). The commonest presenting symptoms of TBM were fever (72.38%), while headache and vomiting was 27.62 and 28.57%, respectively. Mean CD4 count was 172 ± 81.2 in distal symmetrical polyneuropathy (DSPN), 282 ± 75.3 in acute inflammatory demyelinating neuropathy (AIDP) and 95 ± 6.5 for AIDS dementia complex. Key words: HIV positive patients, CD4 count neurological manifestation.

In present scenario, most of the Infrastructure as a Service (IaaS) clouds use simple resource allocation policies like immediate and best effort. Immediate allocation policy allocates the resources if available, otherwise the request is rejected. Best-effort policy also allocates the requested resources if available otherwise the request is placed in a FIFO queue. It is not possible for a cloud provider to satisfy all the requests due to finite resources at a time. Haizea is a resource lease manager that tries to address these issues by introducing complex resource allocation policies. Haizea uses resource leases as resource allocation abstraction and implements these leases by allocating Virtual Machines (VMs). Haizea supports four kinds of resource allocation policies: immediate, best effort, advanced reservation and deadline sensitive. This work provides a better way to support deadline sensitive leases in Haizea while minimizing the total number of leases rejected by it. Proposed dynamic planning based scheduling algorithm is implemented in Haizea that can admit new leases and prepare the schedule whenever a new lease can be accommodated. Experiments results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.

Xen hypervisor is used to execute and migrate the guests on different architectures using a pre-copy algorithm. There are three major categories to improve pre-copy using live migration algorithms: 1) reducing dirty pages; 2) predicating dirty pages; 3) compressing memory pages. The methods based on reducing dirty pages can lead to performance degradation so the new approach called combined approach (including prediction and compression) is proposed in this paper. The prediction of dirty pages during a migration is performed using auto-regressive integrated moving average (ARIMA) model. A least recently used (LRU) stack distance-based delta compression algorithm is proposed for compression model to achieve efficient virtual machine migration. The results show that ARIMA-based model is able to predict 93% in the case of high dirty pages environment. The combined approach is able to reduce 19.16% downtime and 10.76% total migration time on an average compared to Xen's pre-copy algorithm.

Exchange a few words by using information technology in a variety of ways manufacture big amounts of data. Such data requires dealing out and storage. The cloud is an online storage space model where data is stored on multiple virtual servers. Big data processing represents a new face up to in computing, particularly in cloud computing. Data processing involves data acquirement, storage and analysis. In this respect, there are many questions including, what is the connection between big data and cloud computing? And how is big data processed in cloud computing? The answer to these difficulties will be talk about in this paper, where the big data and cloud computing will be studied, in adding up to receiving acquainted with the relationship between them in terms of safety and challenges. It recommends a period for big data, and a model that illustrates the relationship between big data and cloud computing.

As the world recovers from the economic slowdown, the outsourcing industry has to adapt via strategic planning and right governance to cater to the changing business requirements and objectives. The authors review the transactional, contractual and relational governance of outsourcing arrangements along with its underlying characteristics, so companies can choose an appropriate governance forms. Authors emphasize the positive effect of relational governance on the outcome of the outsourcing arrangement and illustrate their arguments through a case study. Authors recommend relational governance for desired benefits with flexibility to accommodate changes and mechanisms built in to cope with change.

Data Mining Technique, namely Classification, is used to predict group membership for data samples. Ensemble learning, combining multiple classifiers using bagging, boosting or stacking, are proven data-mining methods, we have used boosting in this paper for combining multiple classifiers. Cost-sensitive classification is used for classification tasks under the Cost-Based Model (CBM), unlike the Error-Based Model (EBM). EBM does not incorporate the cost of misclassifying a sample in a model building phase, while CBM does. CBM techniques usually modify the weight update equation to incorporate the misclassification cost from cost-matrix. Cost-sensitive boosters are studied and three new extensions of CostBoost algorithm CBE1, CBE2 and CBE3 are proposed and compared with existing cost based boosting classifiers. CSE1, CSE2 and CSE3 outperformed the original CostBoost by 5%, 4% and 4% respectively, in terms of misclassification cost.

Scheduling problem has been an active area of research in computing systems since their inception. The Apache Hadoop framework has emerged as most widely adopted framework for distributed data processing because of open source and allowing use of commodity hardware. Job scheduling has become an important factor to achieve high performance in Hadoop cluster. Several scheduling algorithms have been developed for Hadoop-MapReduce model which vary widely in design and behavior, handling different issues such as locality of data, user share fairness and resource awareness. This paper highlights fundamental issues in job scheduling, presents classification of Hadoop schedulers, and discusses presented survey of existing scheduling algorithm. Moreover paper also discusses features, advantages, and limitations of the scheduling algorithms. This paper also discusses about how various resource monitoring tools or frameworks help in achieving better result from MapReduce. It also discusses customized MapReduce frameworks used for improving the performance. This paper would be useful to beginners and researchers for understanding the state-of-the-art on scheduling in Big data processing.

In the agricultural domain, the main challenge is to present the new information and research to the farmers so that they can leverage the power of ICT to improve their agricultural practices and thereby the production. Huge amount of agriculture related data like weather data, soil health records, cropping pattern, location specific crop disease and pest are collected from different sources like services, remote satellites, and network of sensors. An agro advisory system presented in this paper helps to bridge the gap between farmers and the agriculture domain experts and developed for the cotton farmers in Gujarat region of India. The system consists of three basic components; Cotton Ontology, Web Services, and Mobile Application Development. The cotton ontology maintains domain knowledge required for answering farmer queries. The ontology contains information regarding crop, soil, cultivation process, disease, pest, and other relevant information. Protege ontology development tool is used to develop this ontology. Appropriate web services were built which help interactions with different data sources. The RESTful web services are programmed in Java using the JAX-RS/Jersey API and the Eclipse EE IDE. The services are developed and deployed on a cloud based application server provided by Heroku. The web services are invoked from the mobile device and in turn they connect to various data sources like Open Weather API, SQL database and the Ontologies. The farmers can use this application based on very simple android mobile interfaces. The prototype is developed using Java, Android SDK - v14 and Eclipse IDE.

Author focuses on a structured innovation governance mechanism at the board level for an enterprise. For the active corporate governance of “Innovation”, the author suggests an “Innovation Council”. Such a structured approach to the innovation process within the organization will help board member in multiple aspects. Multiple aspects include better information on market changes, changing consumer needs, competition, and organizational dynamics. The reports of “Innovation Council” integrate board level governance with the routine functioning of an organization. Now board member can provide inputs rather than just approvals. The exposure to “Innovation Council” helps the board to make a better decision. This approach will also help executive to get support for decisions related to new hiring, organizational changes, a new department, market trial or R&D budget and get support for the bright ideas, which are threatened by internal organizational politics.The active innovation governance process is highlighted via the case study of companies who are able to win the trust of their stakeholders, attract growth capital and deliver superior financial performance.

Ontology plays an important role not only for data processing in knowledge based systems but also, provide interoperability in heterogeneous environment and is a cornerstone of semantic web technology. The required technology is used for knowledge representation in OWL/RDF format and facilitate faster access of concepts in domain of interest. Development of ontology is a tedious job and requires a lot of man power in terms of experts' time and knowledge. Although there are various tools and techniques for light weight ontology learning; yet full automation of heavy weight ontology learning from text documents is a distant dream. In this paper we have proposed a framework for learning heavy weight ontology, using text documents written in English language. Initial experimental results are shown for demonstration of our on going research.

Cloud computing provides a computing platform for the users to meet their demands in an efficient way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines are utilized to satisfy the user needs and are placed on physical machines of the cloud for effective usage of hardware resources and electricity. Optimizing the number of physical machines used helps in cutting down the power consumption by substantial amount. An optimal technique is to map virtual machines to physical machines such that the number of required physical machines is minimized. The Virtual Machine Placement problem with the target of minimizing the total energy consumption by running physical machines is an indication of increasing resource utilization and reducing cost of a data center. Virtual Machine Migration is another approach to minimize the total energy consumption and to increase resource utilization. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. Migration support can balance the utilization of multi-dimensional resources, reduce the number of running physical machines and thus lower the energy consumption. In this paper, we present a platform to test different Virtual Machine Placement and Virtual Machine Migration algorithms on real time scenarios (Cloud) and enable them to conduct different experiments. For this purpose, OpenStack private cloud is suggested as private cloud environment and OpenStack Neat is used to provide dynamic Virtual Machine Migration. Here, we have integrated OpenStack Neat with OpenStack Cloud and shown the experiment of dynamic Virtual Machine Migration in real time.

Cloud computing is an emerging technology of cluster systems based on virtualization, which makes hypervisor to interact directly with the hardware. Live migration techniques of Virtual Machine (VM) can be useful for load balancing, energy efficiency and fault tolerance. Xen hypervisor is widely used in virtualization for its better performance and integration with different architectures. In this paper, the pre-copy approach of Xen is evaluated for Live Migration of virtual machines. The Framework of Xen-4.1 based pre-copy is configured using DRBD (Distributed Replicated Block Device) on Intel-VT architecture to get a practical understanding of the algorithm. Xen's pre-copy can have better results in terms of performance metrics-downtime, total transfer time, total pages transferred if bitmap structure and writable working set are improved. For avoiding repeated retransmission of memory pages, techniques based on prediction are used to get information about dirty pages. Prediction based two different techniques are discussed: i) LRU Stack Distance ii) Probability Prediction. Memory Compression, a faster migration technique, is the second step of a combined approach. Various compression methods named RLE, Huffman Coding, MEMCOM, WKdm and LZ are classified. Prediction and compression based combined approach is surveyed to improve pre-copy for efficient live memory migration on Xen.

Information and communication technology (ICT) initiatives for rural development in India are not new. Many of these initiatives have shown mixed responses. Most of these projects have been incubated with government support. For a development project to be successful, the approach should shift from the current "supply-driven" mode to "demand-driven mode" that is citizen centric. These challenges are predominantly in rural areas since the issues are dynamic, engulfed with digital divide issues and services need to be livelihood security centric. In order to make the e-government plans successful and sustainable in the rural areas, e-readiness as well as citizens acceptance of ICT technology needs to be managed.In this paper a citizen centric model or design uses Participatory Rural Appraisal (PRA) to create an ambience where citizens access their requirements and influence the service providers to perpetually create and deliver the required services through feasible ICT options.This paper focuses on developing a mechanism to reflect rural citizen's contributions in the e-governance systems. These contributions are elicited through PRA exercises since most of the rural citizens face educational security challenges.

Background/Aims: The incidence of adverse events (AEs) in adults who receive continuous renal replacement therapy (CRRT) is unknown. We report the incidence of mechanical, metabolic, and hemodynamic CRRT AEs. Methods: This is a retrospective study of all consecutive adult patients (≥18 years) who underwent CRRT from January 1, 2007 to December 31, 2009. Results: Out of 595 patients who underwent CRRT, 366 (62%) were male and 500 (84%) were Caucasian. Regional citrate anticoagulation was used in 98.6% of all patients. The most common clinically significant electrolyte derangements were ionized hypocalcemia (22%), ionized hypercalcemia (23%), and hyperphosphatemia (44%). Almost all (97%) patients had at least one additional AE including new onset hypotension (within the first hour after CRRT initiation) (43%), hypothermia (44%), new onset arrhythmias (29%), new onset anemia (31%) and thrombocytopenia (40%). Conclusions: ICU patients who require CRRT have a high incidence of AEs. Although the extent to which these complications are attributable to CRRT is not known, clinicians need to be cautious and aware of their high prevalence in this patient population.

We retrospectively studied medical records of all children with acute myocarditis and/or early DCM admitted to the Pediatric Critical Care Unit of our hospital between January 2010 and December 2012 were reviewed. 28 patients were included in the study, of which 12 were treated with IVIG (1 g/kg per day) for two days. The patients who received IVIG therapy had a higher left ventricular ejection fraction and a reduced left ventricular end diastolic diameter six months after treatment, as compared to children who had not received IVIG (P

A Sense-Compute-Control(SCC) application involves interactions among a large number of heterogeneous devices that can sense the physical world by obtaining information from sensors, compute the captured information, and control the physical world by triggering actions using actuators. SCC applications are pervasive and found in many application domains, including industrial control, robotics systems or building automation. Developing a SCC application is challenging because it implies a wide range of related issues such as lack of separation of concerns, and lack of high-level of abstractions to address heterogeneity of target devices. Moreover, stakeholders involved in the application development have to address issues that can be attributed to different life-cycles phases such as development, deployment, and evolution. Existing approaches only cover limited subsets of the above mentioned challenges. This paper proposes an integrated approach for the development of SCC applications. It separates the application development into different concerns. So, stakeholders can deal with them individually at evolution and reuse them across applications. To support actions of stakeholders, the development framework provides a set of modeling languages to specify each development concern and abstracts the heterogeneity related complexity. It integrates code generation, task-mapping, and linking techniques to provide automation at various phases of SCC application development.

Recent policy clarifications by the Centers for MedicareMedicaid Services have changed access to outpatient dialysis care at end-stage renal disease (ESRD) facilities for individuals with acute kidney injury in the United States. Tools to predict "ESRD" and "acute" status in terms of kidney function recovery among patients who previously initiated dialysis therapy in the hospital could help inform patient management decisions.Historical cohort study.Incident hemodialysis patients in the Mayo Clinic Health System who initiated in-hospital renal replacement therapy (RRT) and continued outpatient dialysis following hospital dismissal (2006 through 2009).Baseline estimated glomerular filtration rate (eGFR), acute tubular necrosis from sepsis or surgery, heart failure, intensive care unit, and dialysis access.Kidney function recovery defined as sufficient kidney function for outpatient hemodialysis therapy discontinuation.Cohort consisted of 281 patients with a mean age of 64 years, 63% men, 45% with heart failure, and baseline eGFR≥30mL/min/1.73m(2) in 46%. During a median of 8 months, 52 (19%) recovered, most (94%) within 6 months. Higher baseline eGFR (HR per 10-mL/min/1.73m(2) increase eGFR, 1.27; 95% CI, 1.16-1.39; P0.001), acute tubular necrosis from sepsis or surgery (HR, 3.34; 95% CI, 1.83-6.24; P0.001), and heart failure (HR, 0.40; 95% CI, 0.19-0.78, P=0.007) were independent predictors of recovery within 6 months, whereas first RRT in the intensive care unit and catheter dialysis access were not. There was a positive interaction between absence of heart failure and eGFR≥30mL/min/1.73m(2) for predicting kidney function recovery (P0.001).Sample size.Kidney function recovery in the outpatient hemodialysis unit following in-hospital RRT initiation is not rare. As expected, higher baseline eGFR is an important determinant of recovery. However, patients with heart failure are less likely to recover even with a higher baseline eGFR. Consideration of these factors at hospital discharge informs decisions on ESRD status designation and long-term hemodialysis care.

Traditional techniques for time series modeling can capture linear behavior of data and lack the ability to identify nonlinear patterns in time series. Therefore, machine learning techniques like Neural Network or Genetic Programming (GP) are used by practitioners for modeling nonlinear and irregular time series. GP is preferred over other techniques because it does not presume model structure a priori. This paper introduces the use of Postfix-GP, a postfix notation based GP, for real-world nonlinear time series modeling problems. The Postfix-GP uses linear genome representation and stack based evaluation to reduce space-time complexity of GP. The Postfix-GP is applied on two real time series modeling problems: sunspots and river flow series. Performance of evolved Postfix-GP models on training data and out-of-sample data are compared with those obtained by others using EGIPSYS. The obtained results indicate that Postfix-GP offers a new possibility for solving time series modeling and prediction problems.

Mobile devices are used extensively by the people for communication, music, entertainment, Internet and social networking. There is a lack of applications, which can be really useful for the professionals to improve their working capabilities. Though mobile phones are used by people living in rural areas, but there are hardly any relevant applications for them to improve their productivity. In this paper, we have proposed and implemented an information system for farmers which can be operated on their mobile phones. The system is developed using Service Oriented Architecture (SOA) to process spatial data and knowledge base. The knowledge base is maintained in the form of ontologies. The system is an effort to fill the gap between farmers and agricultural experts. A farmer can provide inputs related to crops being cultivated and location specific information to get specific suggestions, alerts and recommendations to improve productivity. It will be generated using the knowledge base. Whenever a farmer observes some anomalous behavior for crops or climate, the system is able to generate recommendations based on inputs provided. We have resolved some of the queries as a part of on-going work and results are displayed on an Android based mobile devices for demonstration of the system.

The number of elderly persons with end-stage renal disease is increasing with many requiring hospitalizations. This study examines the causes and predictors of hospitalization in older haemodialysis patients.We reviewed hospitalizations of older (≥65 years) incident chronic haemodialysis patients initiating therapy between 1 January 2007 and 31 December 2009 under the care of a single Midwestern United States dialysis provider.Of 125 patients, the mean age was 76 ± 7 years and 72% were male. At first dialysis, 68% used a central venous catheter (CVC) and 51% were in the hospital. Mean follow-up was 1.8 ± 1.0 years. At least one hospitalization occurred in 89 (71%) patients and half of all patients were hospitalized once within the first 223 days. Total hospital admission rate was 1.48 per patient year with hospital days totalling 8.54 days per patient year. The three most common reasons for first admission were cardiac (33%), infection (18%) and gastrointestinal (12%). Predictors of future hospitalization included the first dialysis occurring in hospital (hazard ratios (HR) 2.1, 95% CI 1.4-3.3, P = 0.0005) and the use of a CVC at first haemodialysis (HR 2.6, CI 1.6-4.4, P 0.0001).Hospitalizations are common in older incident haemodialysis patients. Access preparation and overall burden of illness leading to the initial hospitalization appear to play a role. Identification of additional factors associated with hospitalization will allow for focused interventions to reduce hospitalization rates and increase the value of care.

Internet is providing the platform for accessing different kinds of services in a distributed and heterogeneous environment. Software components are available on Internet in the form of Web Services. Service Oriented Architecture (SOA) possesses various characteristics by executing Web Services using Simple Object Access Protocol (SOAP). SOAP has certain limitations such as tightly coupled invocation, performance in terms of response data, non-uniform interface and no hyperlink support. These limitations can be resolved by implementing principles of Representational State Transformation (REST). REST is an architectural style implemented by resources known as RESTful Web Services. At present, light-weight RESTful services are dominating the development of services due to simplicity of RESTful architecture. Service access and invocation is simple in RESTful framework as it uses Uniform Resource Identifier (URIs) and Hyperlinks. In this paper, we explore the idea of RESTful Service Mashup by integrating individual Web Services which can satisfy end users' requirements. For this work, we propose a recursive algorithm. The implementation work is on Android 2.2 (froyo), API level 8 and above. It is supported on most of Android based mobile devices.

With the evolution of Web 2.0, ICT has become the primary need of human beings. There is a gap between the farmers and the knowledge of agricultural experts. ICT can fill the gap between farmers and the experts. In this paper, we have proposed a semantic web based architecture to generate agricultural recommendations, using spatial data and agricultural knowledge bases. Our knowledge base acts as a domain expert and will send recommendations to the farmers based on climate conditions and geographic data. We have shown experimental results as a part of implementation of our proposed architecture. A farmer sends a query to the query engine, in order to get information for a specific crop. Query may be related to GIS data, crop knowledge base or both. The result of the query is displayed on a mobile device.

SPARQL is language for querying web of linked open data, and SPARQL query requires knowledge of ontological schema of RDF data source. In this work, we aim to facilitate semantic search over web of data by converting keywords into URIs, and present SPARQLGen. Through appropriate interface, semantic annotations of keywords are captured, and derive a Pseudo Basic Graph Pattern, which is basically, is similar to SPARQL BGP except that it contains keywords rather than full resource URIs. Here we propose heuristics that discover URIs for annotated keywords and build corresponding SPARQL query. SPARSQLGen takes services of falcons, a semantic search engine.

Social Networks have undergone a dramatic growth in recent years. Such networks provide an extremely suitable space to share information between individuals and their neighbors in the social graph instantly. Social Networks provide a powerful reflection of 21st century and the interaction of the Internet generation. Social Networks made impact on production, processing, distribution and consumption.The web 2.0 era passed leaving behind great strength to the end users for uploading, sharing and consuming the information in form of multimedia and text contents. In this paper we propose a recommendation system based on social networking site Twitter by exploiting the contents in form of tweets. We used rule based Information Extraction techniques for generating marketing related recommendations for the farmers and send them by SMS on their mobile phone.

Design of crossover operator plays a crucial role in Genetic Programming (GP). The most studied issues related to crossover operator in GP are: (1) ensuring that crossover operator always produces syntactically valid individuals (2) improving search efficiency of crossover operator. These issues become crucial when the individuals are represented using linear string representation. This paper aims to introduce postfix GP approach to symbolic regression for solving empirical modeling problems. The main contribution includes (1) a linear string (postfix notation) based genome representation method and stack based evaluation to reduce space–time complexity of GP algorithm (2) ensuring that sub-tree crossover operator always produces syntactically valid genomes in linear string representation (3) using semantic information of sub-trees, to be swapped, while designing crossover operator for linear genome representation to provide additional search guidance. The proposed method is tested on two real valued symbolic regression problems. Two different constant creation techniques for Postfix GP, one that explicitly use list of constants and another without use of the list, are presented to evolve useful numeric constants for symbolic regression problems. The results on tested problems show that postfix GP comprised of semantic sub-tree crossover offers a new possibility for efficiently solving empirical modeling problems.

People living in rural areas can not survive only on agriculture and need to migrate to nearby areas to earn sufficient for their basic survival. Many times they migrate to places where they may not get employment immediately or may not get at all. They live in poor and unhygienic conditions creating health problems. Contractors do not get required laborers with expected level of skill sets. Mobile phones can be used as an effective measure to send necessary communication among them. This on-going work aims to use mobile phones, IVRS, speech engine and messages in regional languages to establish effective communication to support livelihood for the laborers living in rural and remote areas. In this paper, architecture is proposed and prototype is implemented to realize it. Algorithms to perform matching, black listing and alert generation are implemented and tested.

There has been a rapid expansion of the use of intravenous immunoglobulin (IVIG) for an ever-growing number of conditions. IVIG is used at a 'replacement dose' (400-600 mg/kg/month) in antibody deficiencies and is used at a high dose (2 g/kg) as an 'immunomodulatory' agent in an increasing number of immune and inflammatory disorders.(1) The limitations for IVIG are the cost of the preparation and the need for intravenous infusions. Due to the cost, shortages and growing use of IVIG there have been attempts to develop evidence-based guidelines for the use of IVIG in a wide variety of immune disorders in children and neonates. This commentary provides the recommendations and recent publication regarding the use of IVIG in various conditions in children.

Social Networking sites play a prominent role in the life of people by providing a platform that allows users to share ideas, activities, events, and interests. Most of the content on the web is in the form of unstructured text which can be structured using Information Extraction techniques. In this paper we have discussed marketing potential of social networking sites in Agro-produce marketing domain. The farmers can post about their produce information on social networking site like twitter. The merchants can show their interest for any product by posting a tweet. These tweets can be analyzed and suggestions can be generated for both farmers and merchants. A domain specific information extraction system is developed for Agro-produce marketing domain for extracting entities crop name, variety, price, quantity, location and deadline from the web. The text engineering framework - General Architecture for Text Engineering (GATE) provides ANNIE, an information extraction plug-in, which is extended and modified for building the system. The core of the system is based on pattern action grammar rules.

SaaS (Software as a Service) is a modern approach to deliver large scalable enterprise software as a service on Internet. Cloud computing platform provides the scalability, availability and utility computing for services on Internet. There are many technical challenges involved in SaaS development. One of them is multi-tenancy, which allows single instance of software to serve multiple organizations by accommodating their unique requirements through configuration at the same time. SaaS architecture requires the both configuration and some level of customization to achieve higher maturity model. In this paper, we proposed a metadata driven SaaS architecture, which is independent of underlying cloud infrastructure. Proposed architecture includes monitoring, tenant management, tenant administration, tenant configuration and large data management services. In our proposed architecture, we used Memcached concept and it is supported by almost all databases to boost the performance. In addition to this, we have considered application pooling on web server to manage priority among tenants.

Intravenous self-infusion of tap water has never been reported in the literature.nbsp; We present a 24-year-old healthy man who self-administered 2.5 L of tap water over 2 hours and developed acute illness including fever, change of mental status, acute hemolysis, low-grade disseminated intravascular coagulation, and acute kidney injury.nbsp;nbsp

Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). It provides an on-demand, pay-as-you-go computing resources and had become an alternative to traditional IT Infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of SLA templates need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. SLA template contains many parameters like cloud's resources (physical memory, main memory, processor speed etc.) and properties (availability, response time etc.). This work addresses the issue of matching SLA parameters to find suitable cloud provider for particular application.

Due to features like on demand services, scalability and usage based pricing, today cloud computing has attracted many customers to move their applications to cloud. But cloud service providers are using different standards and frameworks which customer has to follow. There may be a case where customer wants to migrate data and application to another cloud service provider. In that case, code of application and structure of database must be modified according to the standard provided by new service provider which is very costly. This issue is regarded as cloud vendor lock-in. Work has already been done for providing techniques which can facilitate migration of data. But that technique is not providing support for transformation of query from one cloud to other. By taking Google App engine as one cloud service provider, algorithm can be developed to transform query supported by relational databases, SQL, to query supported by Google app engine datastore, Google Query Language (GQL). After that same algorithm can be generalized for any cloud datastore.

To examine associations between biomarkers of joint tissue metabolism and whole blood lead (Pb), separately for men and women in an African American and Caucasian population, which may reflect an underlying pathology.Participants in the Johnston County Osteoarthritis Project Metals Exposure Sub-Study (329 men and 342 women) underwent assessment of whole blood Pb and biochemical biomarkers of joint tissue metabolism. Urinary cross-linked N telopeptide of type I collagen (uNTX-I) and C-telopeptide fragments of type II collagen (uCTX-II), serum cleavage neoepitope of type II collagen (C2C), serum type II procollagen synthesis C-propeptide (CPII), and serum hyaluronic acid (HA) were measured using commercially available kits; the ratio of [C2C:CPII] was calculated. Serum cartilage oligomeric matrix protein (COMP) was measured by an in-house assay. Multiple linear regression models were used to examine associations between continuous blood Pb and biomarker outcomes, adjusted for age, race, current smoking status, and body mass index. Results are reported as estimated change in biomarker level for a 5-unit change in Pb level.The median Pb level among men and women was 2.2 and 1.9μg/dL, respectively. Correlations were noted between Pb levels and the biomarkers uNTX-I, uCTX-II, and COMP in women, and between Pb and uCTX-II, COMP, CPII, and the ratio [C2C:CPII] in men. In adjusted models among women, a 5-unit increase in blood Pb level was associated with a 28% increase in uCTX-II and a 45% increase in uNTX-I levels (uCTX-II: 1.28 [95% CI: 1.04-1.58], uNTX-I: 1.45 [95% CI:1.21-1.74]). Among men, levels of Pb and COMP showed a borderline positive association (8% increase in COMP for a 5-unit change in Pb: 1.08 [95% CI: 1.00-1.18]); no other associations were significant after adjustment.Based upon known biomarker origins, the novel associations between blood Pb and biomarkers appear to be primarily reflective of relationships to bone and calcified cartilage turnover among women and cartilage metabolism among men, suggesting a potential gender-specific effect of Pb on joint tissue metabolism that may be relevant to osteoarthritis.