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In this research, the HSM predictive models for collisions on urban/suburban arterials are calibrated for collision data from the City of Toronto. It has been found that the use of calibration factors for applying HSM models to Toronto intersection data is not appropriate. New collision models are therefore developed by using local data. The HSM and Toronto models are then calibrated to City of Edmonton intersection collision data to determine whether it is better to calibrate HSM models for a Canadian jurisdiction or models from another Canadian jurisdiction. A related aspect of the research is the investigation of models for crash types. There is no safety performance function (SPF) available in the HSM to predict rear end collisions. Instead, rear end collisions are estimated as a proportion of predicted multivehicle collisions. To overcome this deficiency, Toronto data are used in the estimation of models for rear end collisions.

This paper presents the research results on the contribution of user-centered data mining based on the standard principles, focusing on the analysis of survival and mortality of lung cancer cases. Researchers used anonymized data from previously diagnosed instances in the health database to predict the condition of new patients who have not had their results yet. Medical professionals specializing in this field provided feedback on the usefulness of the new software, which was constructed using WEKA data mining tools and the Naive Bayes method. The results of this article provide elements of interest to discuss the value of identifying or discovering relationships in apparently “hidden” information to propose strategies to counteract health problems or prevent future complications and thus contribute to improving the quality of care. Life of the population, as would be the case of data mining in the health area, has shown applicability in the early detection and prevention of diseases for the analysis of genetic markers to determine the probability of a satisfactory response to medical treatment, and the most accurate model was Naive Bayes (91.1%). The Naive Bayes algorithm’s closest competitor, bagging, came in second with 90.8%. The analysis found that the ZeroR algorithm had the lowest success rate at 80%.

With the increase in users of social media websites such as IMDb, a movie website, and the rise of publicly available data, opinion mining is more accessible than ever. In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. Connotation words have a different meaning than their literal meanings. While representing a word, the context in which the word is used changes the semantics of words. In this research work, categorizing movie reviews with good F-Measure scores has been investigated with Word2Vec and three different aspects of proposed features have been inspected. First, psychological features are extracted from reviews positive emotion, negative emotion, anger, sadness, clout (confidence level) and dictionary words. Second, readablility features are extracted; the Automated Readability Index (ARI), the Coleman Liau Index (CLI) and Word Count (WC) are calculated to measure the review’s understandability score and their impact on review classification performance is measured. Lastly, linguistic features are also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting 50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualized embedding of words into vectors with 50, 100, 150 and 300 dimensions.The pretrained Word2Vec model converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning (ML) algorithms are applied and evaluated according to performance measures: accuracy, precision, recall and F-Measure. The results indicate Support Vector Machine (SVM) using self-trained Word2Vec achieved 86% F-Measure and using psychological, linguistic and readability features with concatenation of Word2Vec features SVM achieved 87.93% F-Measure.

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always true.In this study, we focus on the challenges of numerous breaking-news rumors propagating on social media networks rather than long-lasting rumors. We propose new social-based and content-based features to detect rumors on social media networks. Furthermore, our findings show that our proposed features are more helpful in classifying rumors compared with state-of-the-art baseline features. Moreover, we apply bidirectional LSTM-RNN on text for rumor prediction. This model is simple but effective for rumor detection. The majority of early rumor detection research focuses on long-running rumors and assumes that rumors are always false. In contrast, our experiments on rumor detection are conducted on real-world scenario data set. The results of the experiments demonstrate that our proposed features and different machine learning models perform best when compared to the state-of-the-art baseline features and classifier in terms of precision, recall, and F1 measures.

Over the last decade, the continuous increase in supercomputers’ computing power has been made possible mainly due to the high degree of parallelism at the component level. However, due to this large component count, the Mean Time Between Failure (MTBF) of the overall machine is falling drastically and is currently in the order of few hours for the large supercomputers in the Top500 list. This has accelerated the fault tolerance research in the High-Performance Computing (HPC) field, where many directions are being explored for finding an optimal solution. The main goal is that the applications should survive failure(s), and be able to recover from them with a minimal overhead. This thesis tackles the fault tolerance issue from two directions: the data recovery and process-recovery mechanisms after process failure(s). Both these approaches complement each other to create a comprehensive solution for making fault-tolerant applications. For the data recovery, we use the classical Application-Level Checkpoint/Restart (ALCR) approach, as it inherently has the potential to reduce the checkpoint data size to a minimum. In addition, we explore, analyze, and implement various optimization techniques that further minimize its overhead impact on the application’s runtime. For process recovery, we rely on the libraries that offer the possibility to carry on a parallel program execution despite failed processes. In this regard, we first explore the PGAS-style communication library GASPI, which offers this basic fault-tolerant communication functionality based on the timeout approach. Using GASPI, we have developed a library tool that offers the possibility of integrating fault tolerance in applications in a simplified manner. In this tool, the communicator is observed by a health monitor process that manages the health checking and failure recovery operations for all other processes. The failed processes are replaced from a pool of spare processes that are started with the application but remain idle until needed. For the data recovery after process replacement, a separate library module is implemented that enables ALCR functionality for creating checkpoints of GASPI based applications at the node-level in a fault-prone environment. We have then used these tools in different applications at a large scale for testing and benchmarking purposes. As the MPI communication library is the de facto standard for highly parallel environments, we have developed a library (CRAFT) that offers the ALCR functionality and process-level FT for MPI-based applications. Here, we have also focused on the ease-of-use of the ALCR functionality so that its integration should require minimal code changes in the application. The basic and most frequently used checkpoint data-types can be used directly out-of-the-box without writing any IO-routines in the application. The library offers the IO in multiple formats and is equipped with a straightforward extension mechanism for the user-defined data-types. As a means of overhead reduction, the library offers a builtin asynchronous checkpointing mechanism and supports the Scalable Checkpoint/Restart (SCR) library for node-level checkpointing. For the process-level failure tolerance, CRAFT uses ULFM-MPI (a prototype faulttolerant MPI implementation) underneath but hides its intricate communication recovery details behind a more straightforward API. The user can choose between a shrinking or a non-shrinking communicator recovery approach based on the application. Subsequently, we use applications from various scientific branches to showcase CRAFT’s usage and benchmark its overheads on a large scale. The results show that checkpoint optimizations such as asynchronous or node-level checkpointing bring the runtime overhead to an acceptable range. In addition, the communication recovery process itself remains in the order of seconds, even for more than 2500 processes. In den letzten zehn Jahren wurde das kontinuierliche Wachstum der Rechenleistung von Supercomputern hauptsächlich aufgrund der hohen Parallelität auf der Komponentenebene ermöglicht. Aufgrund dieser großen Anzahl an Komponenten sinkt die mittlere Ausfallzeit der gesamten Maschine jedoch drastisch und liegt derzeit in der Größenordnung von wenigen Stunden bei den großen Supercomputern in der Top500-Liste. Dies hat die Forschung zur Fehlertoleranz im Bereich des High Performance Computing (HPC) beschleunigt, bei der viele Richtungen erforscht werden, um eine optimale Lösung zu finden. Das Hauptziel besteht darin, dass die Anwendungen einen Ausfall überleben und mit minimalem Aufwand wiederhergestellt werden können. In dieser Dissertation befassen wir uns mit dem Fehlertoleranzproblem aus zwei Richtungen, nämlich den Datenwiederherstellungs- und Prozesswiederherstellungsmechanismen nach Prozessausfällen. Beide dieser Ansätze ergänzen sich gegenseitig, um eine umfassende Lösung für die Entwicklung fehlertoleranter Anwendungen zu ermöglichen. Für die Datenwiederherstellung verwenden wir den klassischen Checkpoint/Restart Ansatz auf Anwendungsebene (ALCR), da er von Natur aus das Potenzial hat, die Größe der Checkpoint-Daten auf ein Minimum zu reduzieren. Darüber hinaus untersuchen, analysieren und implementieren wir verschiedene Optimierungstechniken, die die Auswirkungen des Overheads auf die Laufzeit der Anwendung weiter minimieren. Für die Prozesswiederherstellung verlassen wir uns auf die Bibliotheken, die die Möglichkeit bieten, eine parallele Programmausführung trotz ausgefallener Prozesse fortzusetzen. In diesem Zusammenhang untersuchten wir zunächst die Kommunikationsbibliothek GASPI, die diese grundlegende fehlertolerante Kommunikationsfunktionalität auf der Grundlage des Timeout-Ansatzes bietet. Mit GASPI haben wir ein Bibliothekstool entwickelt, das die Möglichkeit bietet, Fehlertoleranz auf vereinfachte Weise in Anwendungen zu integrieren. In diesem Tool wird der Kommunikator von einem Lebensüberwachungsprozess beobachtet, der die Gesundheitsprüfung und Fehlerbehebungsvorgänge für alle anderen Prozesse verwaltet. Die ausgefallenen Prozesse werden aus einem Pool von Ersatzprozessen ersetzt, die mit der Anwendung gestartet werden, aber im Leerlauf bleiben, bis sie benötigt werden. Für die Datenwiederherstellung nach dem Ersetzen von Prozessen ist ein separates Bibliotheksmodul implementiert, das die ALCR-Funktionalität zur Erstellung von Checkpoints von GASPI-basierten Anwendungen auf Knotenebene in einer fehleranfälligen Umgebung ermöglicht. Wir haben diese Tools dann in verschiedenen Anwendungen in großem Umfang zum Testing und Benchmarking eingesetzt. Da die MPI-Kommunikationsbibliothek der De-facto-Standard für parallele Umgebungen ist, haben wir eine Bibliothek (CRAFT) entwickelt, die die ALCR-Funktionalität und Fehlertoleranz auf Prozessebene für MPI-basierte Anwendungen bietet. Dabei haben wir uns auch stark auf die Benutzerfreundlichkeit der ALCR-Funktionalität konzentriert, sodass für ihre Integration nur minimale Codeänderungen in der Anwendung erforderlich sind. Die grundlegenden und am häufigsten verwendeten Checkpoint-Datentypen können sofort verwendet werden, ohne dass IO-Routinen in der Anwendung geschrieben werden müssen. Die Bibliothek bietet die Checkpoint-IO Möglichkeit in mehreren Formaten und ist mit einem einfacheren Erweiterungsmechanismus für die benutzerdefinierten Datentypen ausgestattet. Um den Overhead zu reduzieren, bietet die Bibliothek einen eingebauten asynchronen Checkpointing-Mechanismus und unterstützt die SCR-Bibliothek (Scalable Checkpoint/Restart) für Checkpointing auf Knotenebene. Für die Fehlertoleranz auf Prozessebene verwendet CRAFT ULFM-MPI (eine prototypische fehlertolerante MPI-Implementierung), verbirgt aber die komplizierten Details der Kommunikationswiederherstellung hinter einer einfacheren API. Der Benutzer kann je nach Anwendung zwischen einer verkleinerten oder einer nicht-verkleinerten Wiederherstellung des Kommunikators wählen. Anschließend werden die Anwendungen aus verschiedenen wissenschaftlichen Bereichen verwendet, um die Verwendung von CRAFT zu demonstrieren und seine Overheads in großem Maßstab zu testen. Die Ergebnisse zeigen, dass die Checkpoint-Optimierungen wie asynchrones Checkpointing, oder Checkpointing auf Knotenebene den Laufzeit-Overhead in einen akzeptablen Bereich bringen. Darüber hinaus bleibt der Aufwand für die Kommunikationswiederherstellung in der Größenordnung von Sekunden, selbst bei mehr als 2500 Prozessen.

Supplemental material, sj-docx-1-pie-10.1177_09544089221131147 for Thermal scrutinization of magetohydrodynamics CuO engine oil nanofluid flow across a horizontal surface via Koo–Kleinstreuer–Li modeling: A thermal case study by Syed M Hussain, Faisal Shahzad, Wasim Jamshed, Mohammad Kalimuddin Ahmad, Zulfiqar Rehman and Imran Ullah in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users; therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a StackOverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in StackOverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way.

Purpose The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains. Design/methodology/approach This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results. Findings The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature. Originality/value This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

The purpose of this study is to discover the future leadership role in the presence of Artificial Intelligence and how these smart technologies will change, assist and transform the leadership decision-making within the Welsh manufacturing sector.

Twitter is one of the most widely and frequently use microblog. Thousands of tweets are being posted daily on twitter. With the tweet, peoples are share their current location using check-ins point of interest that is known as geo-tagged tweet. However, most of the people not share their locations with the tweets. Location prediction is beneficial for various fields such as targeted advertising, rescue operations during a disaster and recommendation, etc. On the other hand, location prediction from the text of the tweet is a difficult task as it encounters many problems such as spelling mistakes, nonstandard English, etc. In this study, we propose a new set of linguistic features to predict locations from the texts of the tweet more effectively. Two twitter datasets, five popular ML models and five evaluation metrics are used in experiments to explore the impact of the proposed features. The experimental results reveal that proposed set of features outperform as compared to baseline features. In addition, AUC also demonstrates the significance of the proposed features in the form of 59.94 % and 69.65% using MSM2013 and Ritter dataset respectively. The findings indicate that proposed set of features are the most significant indicators and has proved their effectiveness and strong relationship with location prediction.

Requirement Engineering (RE) is a part being created for programming structure during the software development lifecycle. Software product line development is a new topic area within the domain of software engineering. It also plays important role in decision making and it is ultimately helpful in rising business environment for productive programming headway. Decisions are central to engineering processes and they hold them together. It is argued that better decisions will lead to better engineering. To achieve better decisions requires that they are understood in detail. In order to address the issues, companies are moving towards Software Product Line Engineering (SPLE) which helps in providing large varieties of products with minimum development effort and cost. This paper proposed a new framework for software product line and compared with other models. The results can help to understand the needs in SPL testing, by identifying points that still require additional investigation. In our future scenario, we will combine this model in a controlled environment with industrial SPL projects which will be the new horizon for SPL process management testing strategies., {"references":["Y. Alsultanny, and A. M. Wohaishi \"Requirements of Software Quality Assurance Model. Second International Conference on Environmental and Computer Science\" 2009 pp. 23.","J. Bayer, O. Flege, P. Knauber, R. Laqua, D. Muthig, K. Schmid, T. Wide, and J. M. DeBaud, methodology to develop software product lines, in: Proceedings of the 5th ACM SIGSOFT Symposium on Software Reusability, 2011 pp. 122–131.","J. Bosch, and P. M. Bosch-Sijtsema \"Introducing agile customer-centered development in a legacy software product line\". Software: Practice and Experience, 2011 41(8), 871–882.","J. Erickson, and K. Siau. \"Theoretical and Practical complexity of modeling methods,\" in ACM Communications, vol. 50, 2007 pp. 46-51.","T. Gorschek, A. Gomes., A. Pettersson, and R. Torkar, Introduction of a process maturity model for marketdriven product management and requirements engineering. Journal of Software: Evolution and Process, 2012 24(1), 83–113.","I. Inayat, L. Moraes, M. Daneva, and Salim, S. S. \"A Reflection on Agile Requirements Engineering\": Solutions Brought and Challenges Posed. Scientific Workshop Proceedings of the XP2015, Trondheim, 2015p. 6.","S. Lim, and A. Finkelstein Stake Rare: Using Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation. IEEE Transactions on Software Engineering, 2012 38(3), 707–735.","I García, Pacheco C, Mendoza E, Calvo‐Manzano JA, Cuevas G, San Feliu T. Managing the software process with a software process improvement tool in a small enterprise. Journal of Software: Evolution and Process. 2012 24(5): 481-91.","L. Antovski and F. Imeri. \"Review of Software Reuse Processes\" International Journal of Computer Science Issues 2013 volume 10, 6(2), 83-88.\n[10]\tB. B. Morales, S. Crespo and Clifton C. \"Reuse of Use Cases Diagrams: An Approach based on Ontologies and Semantic Web Technologies\" International Journal of Computer Science Issues 2012 volume 9, 1(2),24-29.\n[11]\tK. Wnuk, D. Callele, E.-A. Karlsson, and B. Regnell, Controlling Lost Opportunity Costs in Agile Development–The Basic Lost Opportunity Estimation Model for Requirements Scoping, in Software Business, Springer, 2012 pp. 255–260.\n[12]\thttp://www.biglever.com, The Software Product Line (SPL) Lifecycle Framework: Overview of the framework', 2017. (Online). Available: http://www.biglever.com/newsletters/SPL_Lifecycle_Part_2.html. (Accessed: 13- Dec- 2017).\n[13]\thttp://www.methodsandtools.com, Software Product Line Engineering with Feature Models' 2017. (Online). http://www.methodsandtools.com/archive/archive.php?id=49. (Accessed: 13- Dec- 2017).\n[14]\tYang, J. Hu, Y. Q. Wang, G. Ruhe, and H. Wang ―Value-based portfolio scoping: an industrial case study, ‖ in Proceedings of the Third International Workshop on Product LinE Approaches in Software Engineering, 2012 pp. 45–48.\n[15]\tK. Pohl, The three dimensions of requirements engineering. In Seminal Contributions to Information Systems Engineering 2013 pp. 63–80. \n[16]\tLinden, F. van der, J. Bosch, E. Kamsties, K. Kansala and H. Obbink, Software product family evaluation, in: Proceedings of the 3rd International Conference on Software Product Lines 2014 pp. 110–129. \n[17]\tM. Khurum, N. Uppalapati, and R. C. Veeramachaneni,.―\"Software requirements triage and selection: state-of-the-art and state-of-practice\", ‖ in Software Engineering Conference (APSEC), 2012 19th Asia-Pacific, vol. 1, 2012 pp. 416–421.\n[18]\tF. S. N, Danuza. A requirements engineering process for software product lines, Universidade Federalde Pernambuco Centrode Informática 2009.\n[19]\tD. F. S. Neiva, A requirements engineering process for software product lines, MSc Dissertation, Page 51 august 2009."]}

Optimization of the extrusion process for apple pomace blended snacks was done using RSM. A blend of apple pomace, maize and soy flour was extruded at five levels of moisture content (12.6–19.4%), barrel temperature (116–184oC) and screw speed (232–568 rpm). The effects of these variables on product responses, viz. bulk density, expansion ration, colour, water absorption index, water solubility index, crispiness, sensory score of overall acceptance, and specific mechanical energy were determined and analyzed. Feed moisture had significant effect on all product responses, whereas screw speed and barrel temperature, both had significant effect on SME, ER and crispiness of the product. The response surface models fitted to all responses were highly significant, and had adequate precisionmore than 12 (>4 desirable), without having any lack of fit. Optimized extrusion parameters for preparation of quality snacks were 14% moisture, 130oC barrel temperature and 300 rpm screw speed.

Bu çalışmada sıcak iklim koşullarında, Hubbord ırkı etlik piliçlerde dört farklı yerleşim sıklığının üretim performansı (canlı ağırlık artışı, yem tüketimi, yemden yararlanma oranı ve ölüm oranı) üzerine etkileri araştırılmıştır. Çalışmada yanları açık, doğal olarak havalandırılan ve fanlarla hava çıkışının sağlandığı kümeste, 35ºC - 45ºC olan çevre sıcaklığı; kümes içi sıcaklığın stres oluşturabilecek düzeylere kadar artışına neden olmuştur. Denemede A, B, C ve D olmak üzere dört farklı yerleşim sıklığına göre sırasıyla, 10 piliç/m², 12 piliç/m², 9 piliç/m² ve 8 piliç/m² olacak şekilde gruplar oluşturulmuştur. Deneme 2-6 haftalık yaş arasında sürdürülmüştür (7 gün adaptasyon periyodu ve 35 gün deneme periyodu). Yem ve su ad libitum olarak sağlanmıştır. Günde 23 saatlik aydınlatma uygulanmıştır. A, B, C ve D gruplarında canlı ağırlık kazancı (2-6 hafta) sırasıyla, 1851.337, 1823.556, 1745.777 ve 1834.663 g ortalama yem tüketimi 3987.333, 4098.667, 4081.333 ve 4123.667 g, yemden yararlanma oranı 2.154, 2.248, 2.338 and 2.248 olarak kaydedilmiştir. Ölüm oranı ise A, B, C ve D gruplarında sırasıyla %6.6 10.0 3.3 ve 0.0 olarak belirlenmiştir. Canlı ağırlık kazancı, yem tüketimi, yemden yararlanma oranı ve ölüm oranı bakımından yerleşim sıklığının istatistik açıdan önemli bir etkisi olmamıştır. Ancak, yoğun yerleşim sıklığı ölüm oranında görünür bir artışa yol açmıştır, The experiment was conducted to investigate the effect of four stocking densities on the production performance i.e. weight gain, feed consumption, feed conversion ratio (FCR) and mortality of Hubbard broilers chicks under hot climate. The ambient temperature 35ºC to 45ºC caused the increase in actual experimental house temperature irrespective of set point house temperature, as naturally ventilated open sided house with extraction fans was used for the experiment. Four of the stocking density treatments; 10 birds/m², 12 birds/m², 9 birds/m² and 8 birds/m² were provided to the four groups A, B, C and D, respectively. The experiment was conducted for 2 to 6 week of age of the birds (7 days adaptation period and 35 days experimental period). Water and feed were provided ad libitum. Twenty-three hours light was provided. Weight gain per bird (2-6 weeks) of groups A, B, C and D was recorded as 1851.337, 1823.556, 1745.777 and 1834.663g, respectively. The average feed consumption per bird (2-6 weeks) was 3987.333, 4098.667, 4081.333 and 4123.667g for the groups A, B, C and D, respectively. Feed conversion ratio (FCR) per the bird (2-6 weeks) was 2.154, 2.248, 2.338 and 2.248 for the group A, B, C and D, respectively. Mortality for the four groups A, B, C and D was recorded as 6.667, 10.000, 3.333 and 0.000 percent, respectively. When the data calculated for weight gain, feed intake, FCR and mortality was analyzed statistically, the difference between the groups of four stocking density was found to be non-significant. However, the birds those were highly stocked showed more mortality apparently.

Drought stress hampers firm crop stand establishment and yield in arid and semiarid regions. The present study was conducted to examine the drought tolerance of various mung bean cultivars/lines based on the seed germination characteristics in relation with the seedling’s antioxidative potential and nutrient uptake. Activities of germination enzymes, seed germination attributes, seedling biomass production and nutrient uptake of studied cultivars/lines were adversely affected due to PEG-induced drought but the total soluble proteins (TSP) and malondialdehyde (MDA) contents were increased. The activities of catalse (CAT) and ascorbate peroxidase (APX) increased in all cultivars/lines being the maximum in cv./line NM-2006 and 8005. The activities of superoxide dismutase (SOD) and peroxidase (POD) were increased in cv./line NM-2006 and 8005 under drought condition. The ascorbic acid (AsA) and total phenolic content (TPC) decreased and total flavonoid content (TFC) increased in all cultivars/lines due to drought. Significant reduction in N, P, K, Ca and Mg was found in all cvs./lines but Fe remain unchanged. In conclusion, cultivars/lines NM-2006, 8005 were recommended as drought tolerant and 97,006 and 97,001 as drought sensitive ones. The study outcomes will likely be helpful for the farmers growing mung bean in rain fed areas for the better productivity.

Session initiation protocol SIP provides the basis for establishing the voice over internet protocol sessions after authentication and exchanging signaling messages. SIP is one of the significant and extensively used protocols in the multimedia protocol stack. Since the RFC2617 was put forth, numerous schemes for SIP authentication have been presented to overcome the flaws. Recently, in 2012, Tang and Liu proposed SIP based authentication protocol and claimed for eliminating the threats in Arshad and Ikram protocol. However the scheme can be made more robust by making further improvements, as the former scheme may come under a threat by adversaries through impersonating a server, given that the user password is compromised. We have proposed an improved protocol for SIP authentication by using elliptic curve cryptography that encounters the previous threat with enhanced security. The analysis shows that proposed scheme is suitable for applications with higher security requirements. Copyright © 2013 John Wiley & Sons, Ltd.

Online question and answer forums have proven to be of great success in knowledge sharing. In these communities, askers post questions and wait for others to answer based on their personal knowledge. This process may lead to misguidance as the provided information has very less authenticity. Due to increasing demand of such forums, it is vital to find experts. Therefore, the shared information will be accurate and authentic in reality. In this paper, FB-Rank technique is presented with an aim to find expertise level of users. For this purpose, content based novel features are used. In addition, H-index, I-index and G-index methods are also used in this research domain. Experiments have been performed on StackOverflow dataset to evaluate the performance of FB-Rank. Kendall's, Spearman's rank and O sim correlation techniques are used to evaluate the effectiveness of the FB-Rank in a comparative manner.

Shift-invert and other methods for computing inner eigenvalues often require the solution of linear systems. This may become a problem if the linear systems are very ill-conditioned and the matrix dimension precludes the use of direct solvers. Then eigensolvers with polynomial acceleration become particularly attractive because they avoid the solution of linear systems. We discuss techniques for increasing the efficiency of subspace iteration with polynomial filtering. These include reducing the polynomial degree by adapting Chebyshev-based filter functions, optimizing the size of the search space, and using tailored high-performance computational kernels.

The use of medicinal plants for treatment of infections is an old age practice. On the basis of this important practice, this study was initiated to evaluate the antibacterial effects of medicinal plants, commonly used by the community of Hazara region of Pakistan. Antibacterial spectrum of traditionally used medicinal plants was determined by disc diffusion method under incubation period of 24 to 48 h at 37°C. Four medicinal plants were tested against the different species of human pathogenic bacteria. It was observed that Incarvillea emodi have good activity against Proteus and significantly active against Staphylococcus . Vernonia anthelmintica revealed significant activity against Staphylococcus and Proteus . However, Pseudomonas is resistant to Adiantum capilus-veneries and this plant is negligibly active against other tested microorganism. Mentha longifolia is significantly active against Proteus , while negligibly active against other tested microorganisms. Escherichia coli is however, resistant to M longifolia . Keywords: Biological activity, medicinal plants, Incarvillea emodi , Vernonia anthelmintica , Adiantum capilus-veneries , Mentha longifolia , Hazara, Pakistan

The effect of various process parameters on drying behavior of Potato (Solanum tuberosum) was studied, and the optimization of process parameters based on quality was investigated. Experiments were conducted to characterize the drying behavior of Potato cubes at various temperatures, potato cube sizes and blanching chemical treatments. The various drying models were evaluated for their suitability and Midilli’s model was found to be best describing the drying characteristics of Potato cubes. A full second order model was developed for all the response variables, viz. Rehydration ratio, shrinkage percentage and mean sensory scores of overall acceptability and found to be significant at 1% level of significance. The effect of process variables was investigated at individual, linear, interactive and quadratic levels for each response. The data was optimized by ‘Design Expert 7.0’ for all the responses. Compromise optimum level of process variables for the responses were 80°C (1), 1cm cube size (-0.87) & KMS (0). The corresponding values of the responses were 4.584, 24.979 & 5.000 respectively.

Clear cell sarcoma (CSS) is a unique malignant soft tissue tumor that mainly occurs from the aponeurotic tissue and tendons of extremities. It is rare in the pediatric population. The tumor does not respond well to chemotherapy or irradiation. Complete surgical resection offers the best chance for a cure. Most studies have demonstrated poor prognosis of this tumor, if it is >5 cm. The literature suggests that local recurrence and distant metastasis are not uncommon even with wide resection and that late recurrence and metastasis commonly occur. This case report discusses CSS in the jaw of a pediatric patient. To our knowledge, this is the only case of CSS of the jaw.

Ontologies provide formed conceptualization of the concepts of domain under study. It is emerging research area due to its unique features of sharing knowledge and its rich structure to adopt structured as well unstructured content. Ontology utilization has extended its scope to other fields like retrieval systems including content and image retrieval. In this paper, we have proposed new framework for image retrieval system based on the domain ontology. The proposed domain in this paper is regarding national heroes of Pakistan. The ontology of diverse nature has been constructed including various contexts related to certain resources, who are personalities in this domain. We have discussed the ontology creation process, implementation of the complete image retrieval system using latest tools including Jena API architecture for inferencing mechanism. The results depict that semantic technique gives better results as compared to keyword search. This work encourages us to add the concept of result ranking for the results achieved using domain ontology.

Classification of software is very important job in sense it gives better understanding to users about their distinctive features. Classification provides the ability to reason about categories; whether automated or not. For designing the Classification Model for Software Workbenches, the software workbenches related to various areas is used. Then the common features in the particular software workbench were identified. After the identification of the features, the classes or categories was defined on the basis of these features and assigned the rank. Comprehensive analysis of software is helped in making a classification for software workbenches of different domains. After designing the model, use the different type of software to test the model. The result of the testing is defined how the software is ranked out in the particular software workbench.