Query: Behavior of MVC (Model View Controller) based Web Application developed in PHP and .NET framework

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Use of Design Patterns in PHP-Based Web Application Frameworks <=SEP=> It is known that design patterns of object-oriented programming are used in the design of Web applications, but there is no sufficient information which data patterns are used, how often they are used, and the level of quality at which they are used. This paper describes the results concerning the use of design patterns in projects which develop PHP-based Web application frameworks. Documentation and source code were analysed for 10 frameworks, finding that design patterns are used in the development of Web applications, but not too much and without much consistency. The results and conclusions can be of use when planning and developing new projects because the existing experience can be taken into account. The paper also offers information which design patterns are not used because they may be artificial or hard-to-use in real projects. Alternatively, developers may simply lack information on the existence of the design patterns.

PHP FRAMEWORK FOR DATABASE MANAGEMENT BASED ON MVC PATTERN <=SEP=> PHP is a powerful language to develop dynamic and interactive web applications. One of the defining features of PHP is the ease for developers to connect and manipulate a database. PHP prepares the functions for database manipulation. However, database management is done by the Structure Query Language (SQL). Most novice programmers often have trouble with SQL syntax. In this paper, we present the PHP framework for database management based on the MVC pattern. The MVC pattern is very useful for the architecture of web applications, separating the model, view and controller of a web application. The PHP framework encapsulated, common database operations are INSERT, UPDATE, DELETE and SELECT. Developers will not be required to consider the specific SQL statement syntax, just to call it the method in the model module. In addition, we use White-Box testing for the code verification in the model module. Lastly, a web application example is shown to illustrate the process of the PHP framework.

Understanding and Using Patterns in Software Development <=SEP=> Patterns have shown to be an effective means of capturing and communicating software design experience. However, there is more to patterns than software design patterns: We believe that patterns work for software development on several levels. In this paper we explore what we have come to understand as crucial aspects of the pattern concept, relate patterns to the different models built during software design, discuss pattern forms and how we think that patterns can form larger wholes like pattern handbooks.

Patterns of Enterprise Application Architecture <=SEP=> protected void doInsert(DomainObject subject, PreparedStatement insertStatement) throws SQLException; class PersonMapper... protected String insertStatement() { return "INSERT INTO people VALUES (?, ?, ?, ?)"; } protected void doInsert( DomainObject abstractSubject, PreparedStatement stmt) throws SQLException { Person subject = (Person) abstractSubject; stmt.setString(2, subject.getLastName()); stmt.setString(3, subject.getFirstName()); stmt.setInt(4, subject.getNumberOfDependents()); } Example: Separating the Finders (Java) To allow domain objects to invoke finder behavior I can use Separated Interface (476) to separate the finder interfaces from the mappers (Figure 10.5 ). I can put these finder interfaces in a separate package that's visible to the domain layer, or, as in this case, I can put them in the domain layer itself. Figure 10.5. Defining a finder interface in the domain package.

The Research of PHP Development Framework Based on MVC Pattern <=SEP=> PHP is one of the leading web development languages, however, the development model of existing PHP organizes without a structure, which mixes the code of data access, the processing of business logic , and web presentation layer together, as a relult, it brought about many problems in the web applications , meanwhile, it could not meet the rapid development of web apply any more. In this paper, a implementation of PHP based on MVC design patterns - FDF framework was provided for PHP developers, which can offer a framework for web applications, separate the data, view and control of web applications, afford to achieve loose coupling, thereby enhanced the efficiency, reliability, maintainability and scalability of application development.

Co-regularized least square regression for multi-view multi-class classification <=SEP=> Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification. We find it performed consistently and significantly better than CoT-NB over eight data sets at different scales. We also find for CoR-LS identity coding is optimal on large data sets and BCH coding is optimal on small data sets. Optimal scoring, a data-dependent coding scheme, often provides near-optimal performance.

Introduction to Fillers. <=SEP=> BACKGROUND
Over the last few years, injectable soft-tissue fillers have become an integral part of cosmetic therapy, with a wide array of products designed to fill lines and folds and revolumize the face.


METHODS
This review describes cosmetic fillers currently approved by the Food and Drug Administration and discusses new agents under investigation for use in the United States.


RESULTS
Because of product refinements over the last few years-greater ease of use and longevity, the flexibility of multiple formulations within one line of products, and the ability to reverse poor clinical outcomes-practitioners have gravitated toward the use of biodegradable agents that stimulate neocollagenesis for sustained aesthetic improvements lasting up to a year or more with minimal side effects. Permanent implants provide long-lasting results but are associated with greater potential risk of complications and require the skilled hand of the experienced injector.


CONCLUSIONS
A variety of biodegradable and nonbiodegradable filling agents are available or under investigation in the United States. Choice of product depends on injector preference and the area to be filled. Although permanent agents offer significant clinical benefits, modern biodegradable fillers are durable and often reversible in the event of adverse effects.

Truncated Importance Sampling for Reinforcement Learning with Experience Replay <=SEP=> Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direction of a changing policy, based on preceding experience, is essential here. We propose one that uses truncated importance sampling. We derive bounds of bias of that type of estimators and prove that this bias asymptotically vanishes. In the experimental study we apply our approach to the classic ActorCritic and obtain 20-fold increase in speed of learning.

EmEx, a Tool for Automated Emotive Face Recognition Using Convolutional Neural Networks <=SEP=> The work described in this paper represents the study and the attempt to make a contribution to one of the most stimulating and promising sectors in the field of emotion recognition, which is health care management. Multidisciplinary studies in artificial intelligence, augmented reality and psychology stressed out the importance of emotions in communication and awareness. The intent is to recognize human emotions, processing images streamed in real-time from a mobile device. The adopted techniques involve the use of open source libraries of visual recognition and machine learning approaches based on convolutional neural networks (CNN).

A Novel Secure Architecture for the Internet of Things <=SEP=> The "Internet of Things"(IoT) opens opportunities for devices and softwares to share information on an unprecedented scale. However, such a large interconnected network poses new challenges to system developers and users. In this article, we propose a layered architecture of IoT system. Using this model, we try to identify and assess each layer's challenges. We also discuss several existing technologies that can be used make this architecture secure.

MIMO Performance of Realistic UE Antennas in LTE Scenarios at 750 MHz <=SEP=> Multiple-input-multiple-output (MIMO) is a technique to achieve high data rates in mobile communication networks. Simulations are performed at both the antenna level and Long-Term Evolution (LTE) system level to assess the performance of realistic handheld devices with dual antennas at 750 MHz. It is shown that MIMO works very well and gives substantial performance gain in user devices with a quarter-wavelength antenna separation.

Implementation and verification of a generic universal memory controller based on UVM <=SEP=> This paper presents a coverage driven constraint random based functional verification method based on the Universal Verification Methodology (UVM) using System Verilog for generic universal memory controller architecture. This universal memory controller is looking forward to improving the performance of the existing memory controllers through a complete integration of the existing memory controllers features in addition of providing novel features. It also reduces the consumed power through providing high power consumption control due to its proposed different power levels supported to fit all power scenarios. While implementing a worthy architecture like the proposed generic universal memory controller, UVM is the best choice to build well-constructed, high controlled and reusable verification environment to efficiently verify it. More than 200 coverage points have been covered to verify the validation of the integrated features which makes the proposed universal memory controller replaces the existing controllers on the scene as it provides all of their powerful features in addition of novel features to control two of the most dominated types of memory; FLASH and DRAM through one memory controller.

Towards Internet of Things : Survey and Future Vision <=SEP=> Internet of things is a promising research due to its importance in many commerce, industry, and education applications. Recently, new applications and research challenges in numerous areas of Internet of things are fired. In this paper, we discuss the history of Internet of things, different proposed architectures of Internet of things, research challenges and open problems related to the Internet of things. We also introduce the concept of Internet of things database and discuss about the future vision of Internet of things. These are the manuscript preparation guidelines used as a standard template for all journal submissions. Author must follow these instructions while preparing/modifying these guidelines.

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning <=SEP=> A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task. In this work, we argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation but also helps semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. Such a joint attribute learning model is then extended by following a multi-task transfer learning framework to address a more challenging unsupervised domain adaptation problem, where annotations are only available on an auxiliary dataset and the target dataset is completely unlabelled. Extensive experiments show that the proposed models, though being linear and thus extremely efficient to compute, produce state-of-the-art results on both zero-shot learning and person re-identification.

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization <=SEP=> In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural networks in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm — the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.

The structure of musical preferences: a five-factor model. <=SEP=> Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners' affective reactions to excerpts of music from a wide variety of musical genres. The findings from 3 independent studies converged to suggest that there exists a latent 5-factor structure underlying music preferences that is genre free and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as (a) a Mellow factor comprising smooth and relaxing styles; (b) an Unpretentious factor comprising a variety of different styles of sincere and rootsy music such as is often found in country and singer-songwriter genres; (c) a Sophisticated factor that includes classical, operatic, world, and jazz; (d) an Intense factor defined by loud, forceful, and energetic music; and (e) a Contemporary factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and the auditory characteristics of the music.

Coagulation and ablation patterns of high-intensity focused ultrasound on a tissue-mimicking phantom and cadaveric skin <=SEP=> High-intensity focused ultrasound (HIFU) can be applied noninvasively to create focused zones of tissue coagulation on various skin layers. We performed a comparative study of HIFU, evaluating patterns of focused tissue coagulation and ablation upon application thereof. A tissue-mimicking (TM) phantom was prepared with bovine serum albumin and polyacrylamide hydrogel to evaluate the geometric patterns of HIFU-induced thermal injury zones (TIZs) for five different HIFU devices. Additionally, for each device, we investigated histologic patterns of HIFU-induced coagulation and ablation in serial sections of cadaveric skin of the face and neck. All HIFU devices generated remarkable TIZs in the TM phantom, with different geometric values of coagulation for each device. Most of the TIZs seemed to be separated into two or more tiny parts. In cadaveric skin, characteristic patterns of HIFU-induced ablation and coagulation were noted along the mid to lower dermis at the focal penetration depth of 3 mm and along subcutaneous fat to the superficial musculoaponeurotic system or the platysma muscle of the neck at 4.5 mm. Additionally, remarkable pre-focal areas of tissue coagulation were observed in the upper and mid dermis at the focal penetration depth of 3 mm and mid to lower dermis at 4.5 mm. For five HIFU devices, we outlined various patterns of HIFU-induced TIZ formation along pre-focal, focal, and post-focal areas of TM phantom and cadaveric skin of the face and neck.

Revising Immersion: A Conceptual Model for the Analysis of Digital Game Involvement <=SEP=> Game studies literature has recently seen a renewed interest in game experience with the recent publication of a number of edited collections, dissertations and conferences focusing on the subject. This paper aims to contribute to that growing body of literature by presenting a summary of my doctoral research in digital game involvement and immersion. It outlines a segment of a conceptual model that describes and analyzes the moment by moment involvement with digital games on a variety of experiential dimensions corresponding to six broad categories of game features. The paper ends with a proposal to replace the metaphor of immersion with one of incorporation. Incorporation aims to avoid the binary notion of the player’s plunge into the virtual environment characteristic of “immersion” while dispelling the vagueness of application that all too often surrounds the term.

SpatialHadoop: towards flexible and scalable spatial processing using mapreduce <=SEP=> Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

Impact of a workplace stress reduction program on blood pressure and emotional health in hypertensive employees. <=SEP=> OBJECTIVES
This study examined the impact of a workplace-based stress management program on blood pressure (BP), emotional health, and workplace-related measures in hypertensive employees of a global information technology company.


DESIGN
Thirty-eight (38) employees with hypertension were randomly assigned to a treatment group that received the stress-reduction intervention or a waiting control group that received no intervention during the study period. The treatment group participated in a 16-hour program, which included instruction in positive emotion refocusing and emotional restructuring techniques intended to reduce sympathetic nervous system arousal, stress, and negative affect, increase positive affect, and improve performance. Learning and practice of the techniques was enhanced by heart rate variability feedback, which helped participants learn to self-generate physiological coherence, a beneficial physiologic mode associated with increased heart rhythm coherence, physiologic entrainment, parasympathetic activity, and vascular resonance. BP, emotional health, and workplace-related measures were assessed before and 3 months after the program.


RESULTS
Three months post-intervention, the treatment group exhibited a mean adjusted reduction of 10.6 mm Hg in systolic BP and of 6.3 mm Hg in diastolic BP. The reduction in systolic BP was significant in relation to the control group. The treatment group also demonstrated improvements in emotional health, including significant reductions in stress symptoms, depression, and global psychological distress and significant increases in peacefulness and positive outlook. Reduced systolic BP was correlated with reduced stress symptoms. Furthermore, the trained employees demonstrated significant increases in the work-related scales of workplace satisfaction and value of contribution.


CONCLUSIONS
Results suggest that a brief workplace stress management intervention can produce clinically significant reductions in BP and improve emotional health among hypertensive employees. Implications are that such interventions may produce a healthier and more productive workforce, enhancing performance and reducing losses to the organization resulting from cognitive decline, illness, and premature mortality.

IL-17 and Th17 Cells. <=SEP=> CD4+ T cells, upon activation and expansion, develop into different T helper cell subsets with different cytokine profiles and distinct effector functions. Until recently, T cells were divided into Th1 or Th2 cells, depending on the cytokines they produce. A third subset of IL-17-producing effector T helper cells, called Th17 cells, has now been discovered and characterized. Here, we summarize the current information on the differentiation and effector functions of the Th17 lineage. Th17 cells produce IL-17, IL-17F, and IL-22, thereby inducing a massive tissue reaction owing to the broad distribution of the IL-17 and IL-22 receptors. Th17 cells also secrete IL-21 to communicate with the cells of the immune system. The differentiation factors (TGF-beta plus IL-6 or IL-21), the growth and stabilization factor (IL-23), and the transcription factors (STAT3, RORgammat, and RORalpha) involved in the development of Th17 cells have just been identified. The participation of TGF-beta in the differentiation of Th17 cells places the Th17 lineage in close relationship with CD4+CD25+Foxp3+ regulatory T cells (Tregs), as TGF-beta also induces differentiation of naive T cells into Foxp3+ Tregs in the peripheral immune compartment. The investigation of the differentiation, effector function, and regulation of Th17 cells has opened up a new framework for understanding T cell differentiation. Furthermore, we now appreciate the importance of Th17 cells in clearing pathogens during host defense reactions and in inducing tissue inflammation in autoimmune disease.

Variational mesh segmentation via quadric surface fitting <=SEP=> Wepresent a new variationalmethod formesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both L2 and L2,1 metrics from a triangle to a quadric surface. The Lloyd iteration is used to minimize the energy function, which repeatedly interleaves between mesh partition and quadric surface fitting. We also integrate feature-based and simplification-based techniques in the segmentation framework, which greatly improve the performance. The advantages of our algorithm are demonstrated by comparing with the state-of-the-art methods. © 2012 Elsevier Ltd. All rights reserved.

Extracting Opinion Expressions with semi-Markov Conditional Random Fields <=SEP=> Extracting opinion expressions from text is usually formulated as a token-level sequence labeling task tackled using Conditional Random Fields (CRFs). CRFs, however, do not readily model potentially useful segment-level information like syntactic constituent structure. Thus, we propose a semi-CRF-based approach to the task that can perform sequence labeling at the segment level. We extend the original semi-CRF model (Sarawagi and Cohen, 2004) to allow the modeling of arbitrarily long expressions while accounting for their likely syntactic structure when modeling segment boundaries. We evaluate performance on two opinion extraction tasks, and, in contrast to previous sequence labeling approaches to the task, explore the usefulness of segmentlevel syntactic parse features. Experimental results demonstrate that our approach outperforms state-of-the-art methods for both opinion expression tasks.

The GUM corpus: creating multilayer resources in the classroom <=SEP=> This paper presents the methodology, design principles and detailed evaluation of a new freely available multilayer corpus, collected and edited via classroom annotation using collaborative software. After briefly discussing corpus design for open, extensible corpora, five classroom annotation projects are presented, covering structural markup in TEI XML, multiple part of speech tagging, constituent and dependency parsing, information structural and coreference annotation, and Rhetorical Structure Theory analysis. Layers are inspected for annotation quality and together they coalesce to form a richly annotated corpus that can be used to study the interactions between different levels of linguistic description. The evaluation gives an indication of the expected quality of a corpus created by students with relatively little training. A multifactorial example study on lexical NP coreference likelihood is also presented, which illustrates some applications of the corpus. The results of this project show that high quality, richly annotated resources can be created effectively as part of a linguistics curriculum, opening new possibilities not just for research, but also for corpora in linguistics pedagogy.

Programming challenges of chatbot: Current and future prospective <=SEP=> In the modern Era of technology, Chatbots is the next big thing in the era of conversational services. Chatbots is a virtual person who can effectively talk to any human being using interactive textual skills. Currently, there are many cloud base Chatbots services which are available for the development and improvement of the chatbot sector such as IBM Watson, Microsoft bot, AWS Lambda, Heroku and many others. A virtual person is based on machine learning and Artificial Intelligence (AI) concepts and due to dynamic nature, there is a drawback in the design and development of these chatbots as they have built-in AI, NLP, programming and conversion services. This paper gives an overview of cloud-based chatbots technologies along with programming of chatbots and challenges of programming in current and future Era of chatbot.

Situationally Aware In-Car Information Presentation Using Incremental Speech Generation: Safer, and More Effective <=SEP=> Holding non-co-located conversations while driving is dangerous (Horrey and Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors (Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected, and resume when over. We show that this improves both driving performance and recall of system-presented information, compared to a non-adaptive strategy.

Predictive maintenance techniques <=SEP=> This article discusses the importance of PdM for industrial process applications and investigates a number of emerging technologies that enable this approach, including online energy-efficiency evaluation and continuous condition monitoring. The article gives an overview of existing and future technologies that can be used in these areas. Two methods for bearing fault detection and energy-efficiency estimation are discussed. The article concludes with focus on one pilot installation at Weyerhaeuser's Containerboard Packaging Plant in Manitowoc, Wisconsin, USA, monitoring three critical induction motors: a 75-hrho blower motor, a 50-hrho hydraulic pump motor, and a 200-hp compressor motor. Finally, the field experience gained in this plant is presented as two case studies.

Spectral analysis of surface electromyography (EMG) of upper esophageal sphincter-opening muscles during head lift exercise. <=SEP=> Although recent studies have shown enhancement of deglutitive upper esophageal sphincter opening in healthy elderly patients performing an isometric/isotonic head lift exercise (HLE), the muscle groups affected by this process are not known. A shift in the spectral analysis of surface EMG activity seen with muscle fatigue can be used to identify muscles affected by an exercise. The objective of this study was to use spectral analysis to evaluate surface EMG activities in the suprahyoid (SHM), infrahyoid (IHM), and sternocleidomastoid (SCM) muscle groups during the HLE. Surface EMG signals were recorded continuously on a TECA Premiere II during two phases of the HLE protocol in eleven control subjects. In the first phase of the protocol, surface EMG signals were recorded simultaneously from the three muscle groups for a period of 20 s. In the second phase, a 60 s recording was obtained for each of three successive trials with individual muscle groups. The mean frequency (MNF), median frequency (MDF), root mean square (RMS), and average rectified value (ARV) were used as spectral variables to assess the fatigue of the three muscle groups during the exercise. Least squares regression lines were fitted to each variable data set. Our findings suggest that during the HLE the SHM, IHM, and SCM muscle groups all show signs of fatigue; however, the SCM muscle group fatigued faster than the SHM and IHM muscle groups. Because of its higher fatigue rate, the SCM muscle group may play a limiting role in the HLE.

ZStream: a cost-based query processor for adaptively detecting composite events <=SEP=> Composite (or Complex) event processing (CEP) systems search sequences of incoming events for occurrences of user-specified event patterns. Recently, they have gained more attention in a variety of areas due to their powerful and expressive query language and performance potential. Sequentiality (temporal ordering) is the primary way in which CEP systems relate events to each other. In this paper, we present a CEP system called ZStream to efficiently process such sequential patterns. Besides simple sequential patterns, ZStream is also able to detect other patterns, including conjunction, disjunction, negation and Kleene closure.
 Unlike most recently proposed CEP systems, which use non-deterministic finite automata (NFA's) to detect patterns, ZStream uses tree-based query plans for both the logical and physical representation of query patterns. By carefully designing the underlying infrastructure and algorithms, ZStream is able to unify the evaluation of sequence, conjunction, disjunction, negation, and Kleene closure as variants of the join operator. Under this framework, a single pattern in ZStream may have several equivalent physical tree plans, with different evaluation costs. We propose a cost model to estimate the computation costs of a plan. We show that our cost model can accurately capture the actual runtime behavior of a plan, and that choosing the optimal plan can result in a factor of four or more speedup versus an NFA based approach. Based on this cost model and using a simple set of statistics about operator selectivity and data rates, ZStream is able to adaptively and seamlessly adjust the order in which it detects patterns on the fly. Finally, we describe a dynamic programming algorithm used in our cost model to efficiently search for an optimal query plan for a given pattern.

Environmental implications of plastic debris in marine settings--entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. <=SEP=> Over the past five or six decades, contamination and pollution of the world's enclosed seas, coastal waters and the wider open oceans by plastics and other synthetic, non-biodegradable materials (generally known as 'marine debris') has been an ever-increasing phenomenon. The sources of these polluting materials are both land- and marine-based, their origins may be local or distant, and the environmental consequences are many and varied. The more widely recognized problems are typically associated with entanglement, ingestion, suffocation and general debilitation, and are often related to stranding events and public perception. Among the less frequently recognized and recorded problems are global hazards to shipping, fisheries and other maritime activities. Today, there are rapidly developing research interests in the biota attracted to freely floating (i.e. pelagic) marine debris, commonly known as 'hangers-on and hitch-hikers' as well as material sinking to the sea floor despite being buoyant. Dispersal of aggressive alien and invasive species by these mechanisms leads one to reflect on the possibilities that ensuing invasions could endanger sensitive, or at-risk coastal environments (both marine and terrestrial) far from their native habitats.