Molecular Evolution of Pseudomonas syringae Type III Secreted Effector Proteins

Diverse Gram-negative pathogens like Pseudomonas syringae employ type III secreted effector (T3SE) proteins as primary virulence factors that combat host immunity and promote disease. T3SEs can also be recognized by plant hosts and activate an effector triggered immune (ETI) response that shifts the interaction back toward plant immunity. Consequently, T3SEs are pivotal in determining the virulence potential of individual P. syringae strains, and ultimately help to restrict P. syringae pathogens to a subset of potential hosts that are unable to recognize their repertoires of T3SEs. While a number of effector families are known to be present in the P. syringae species complex, one of the most persistent challenges has been documenting the complex variation in T3SE contents across a diverse collection of strains. Using the entire pan-genome of 494 P. syringae strains isolated from more than 100 hosts, we conducted a global analysis of all known and putative T3SEs. We identified a total of 14,613 putative T3SEs, 4,636 of which were unique at the amino acid level, and show that T3SE repertoires of different P. syringae strains vary dramatically, even among strains isolated from the same hosts. We also find substantial diversification within many T3SE families, and in many cases find strong signatures of positive selection. Furthermore, we identify multiple gene gain and loss events for several families, demonstrating an important role of horizontal gene transfer (HGT) in the evolution of P. syringae T3SEs. These analyses provide insight into the evolutionary history of P. syringae T3SEs as they co-evolve with the host immune system, and dramatically expand the database of P. syringae T3SEs alleles.

A Survey of Multi-task Learning Methods in Chemoinformatics

Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.

Efficient Learning-Based Recommendation Algorithms for Top- N Tasks and Top- N Workers in Large-Scale Crowdsourcing Systems

The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers’ capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this article, we propose a two-tier data representation scheme (defining a worker--category suitability score and a worker--task attractiveness score ) to support personalized task and worker recommendations. We also extend two optimization methods, namely least mean square error and Bayesian personalized rank, to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top- N recommendation algorithms for crowdsourcing systems: (1) Top- N -Tasks recommendation algorithms for discovering the top- N most suitable tasks for a given worker and (2) Top- N -Workers recommendation algorithms for identifying the top- N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.

Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs

The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs) often face complications in such innovative processes due to the continuous development in innovations and technologies. Policy makers should propose strategies, calls for proposals with aim of supporting SMEs to invest on these technologies and making them more competitive in the marketplace.

Performance prediction of data streams on high-performance architecture

Worldwide sensor streams are expanding continuously with unbounded velocity in volume, and for this acceleration, there is an adaptation of large stream data processing system from the homogeneous to rack-scale architecture which makes serious concern in the domain of workload optimization, scheduling, and resource management algorithms. Our proposed framework is based on providing architecture independent performance prediction model to enable resource adaptive distributed stream data processing platform. It is comprised of seven pre-defined domain for dynamic data stream metrics including a self-driven model which tries to fit these metrics using ridge regularization regression algorithm. Another significant contribution lies in fully-automated performance prediction model inherited from the state-of-the-art distributed data management system for distributed stream processing systems using Gaussian processes regression that cluster metrics with the help of dimensionality reduction algorithm. We implemented its base on Apache Heron and evaluated with proposed Benchmark Suite comprising of five domain-specific topologies. To assess the proposed methodologies, we forcefully ingest tuple skewness among the benchmarking topologies to set up the ground truth for predictions and found that accuracy of predicting the performance of data streams increased up to 80.62% from 66.36% along with the reduction of error from 37.14 to 16.06%.

A Data Science-Based Analysis Points at Distinct Patterns of Lipid Mediator Plasma Concentrations in Patients With Dementia

Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.

Dependency Graph Based Detection of Semantically Equivalent Questions in Online Forums

In the present, people increasingly rely on online user forums for clearing their doubts and seeking answers to varied questions. On many online forums, users encounter a similar question asked in different formats. This results in confusion for the user and he may not be able to find appropriate answer to his question even though the appropriate answer exists on some other page, i.e., on the page resultant of a differently formed question. Currently, online forums like Quora only give suggestions to the user about the questions he could ask but do not show all the semantically equivalent questions. This article eases the work of the users searching for answers on online user forums. The proposed technique will allow the users to look at all the existing semantically equivalent questions. A dependency graph-based matching algorithm is applied to accomplish this objective.

Applications of the Whittaker smoother in NMR spectroscopy

The Whittaker smoother, a special case of penalized least square, is a multipurpose algorithm that has proven to be very useful in many scientific fields, including image processing, chromatography, and optical spectroscopy. It shares many similarities with the Savitzky-Golay algorithm, but it is significantly faster and easier to automate. Its use in nuclear magnetic resonance, however, is not widespread although several applications have recently been published. In this review, the mathematical background of the method and its main applications in nuclear magnetic resonance spectroscopy will be discussed.

NLOS Identification in WLANs Using Deep LSTM with CNN Features

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.

Accuracy of Markerless 3D Motion Capture Evaluation to Differentiate between On/Off Status in Parkinson's Disease after Deep Brain Stimulation

Background

Body motion evaluation (BME) by markerless systems is increasingly being considered as an alternative to traditional marker-based technology because they are faster, simpler, and less expensive. They are increasingly used in clinical settings in patients with movement disorders; however, the wide variety of systems available makes results conflicting.

Research Question

The objective of this study was to determine whether a markerless 3D motion capture system is a useful instrument to objectively differentiate between PD patients with DBS in On and Off states and controls and its correlation with the evaluation by means of MDS-UPDRS.

Methods

Six PD patients who underwent deep brain stimulation (DBS) bilaterally in the subthalamic nucleus were evaluated using BME and the Unified Parkinson's Disease Rating Scale (UPDRS-III) with DBS turned On and Off. BME of 16 different movements in six controls paired by age and sex was compared with that in PD patients with DBS in On and Off states.

Results

A better performance in the BME was correlated with a lower UPDRS-III score. There was no statistically significant difference between patients in Off and On states of DBS regarding BME. However, some items such as left shoulder flexion (p=0.038), right shoulder rotation (p=0.011), and left trunk rotation (p=0.023) were different between Off patients and healthy controls.

Significance

Kinematic data obtained with this markerless system could contribute to discriminate between PD patients and healthy controls. This emerging technology may help to clinically evaluate PD patients more objectively.

Machine Learning for the Developing World

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. This article examines the burgeoning field of machine learning for the developing world (ML4D). First, we present a review of prominent literature. Next, we suggest best practices drawn from the literature for ensuring that ML4D projects are relevant to the advancement of development objectives. Finally, we discuss how developing world challenges can motivate the design of novel machine learning methodologies. This article provides insights into systematic differences between ML4D and more traditional machine learning applications. It also discusses how technical complications of ML4D can be treated as novel research questions, how ML4D can motivate new research directions, and where machine learning can be most useful.

Nuclear Norm Clustering: a promising alternative method for clustering tasks

Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Background

Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine‐learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain.

Methods

Based on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine‐learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase.

Results

Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research.

Conclusions

The present computational functional genomics‐based approach provided a computational systems‐biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine‐learned methods provide innovative approaches to knowledge discovery from previous evidence.

Significance

We show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain.

A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.

Neuromorphic hardware is based on principles of neuroscience, and has the potential to provide higher-level brain functions. Here, the authors develop a neuromorphic network device, constructed from single-walled carbon nanotubes and polyoxometalate, that mimics nerve impulse generation.

Materialized View Selection Using Set Based Particle Swarm Optimization

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

In modern times there is an increasing trend of applications for handling Big data. However, negotiating with the concepts of the Big data is an extremely difficult issue today. The MapReduce framework has been in focus recently for serious consideration. The aim of this study is to get the task-scheduling over Big data using Hadoop. Initially, we prioritize the tasks with the help of k-means clustering algorithm. Then, the MapReduce framework is employed. The available resource is optimally selected using optimization technique in map-phase. The proposed method uses the FireFly Algorithm and BAT algorithms (FFABAT) for choosing the optimal resource with minimum cost value. The bat-inspired algorithm is a meta-heuristic optimization method developed by Xin-She Yang (2010). This bat algorithm is established on the echo-location behaviour of micro-bats with variable pulse rates of emission and loudness. Finally, the tasks are scheduled with the optimal resource in reducer-phase and stored in the cloud. The performance of the algorithm is analysed, based on the total cost, time and memory utilization.

A Survey of Data Semantization in Internet of Things

With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.

Kinetic Contributions of The Upper Limbs During Counter-Movement Verical Jumps With and Without Arm Swing

Mosier, EM, Fry, AC, and Lane, MT. Kinetic contributions of the upper limbs during countermovement. J Strength Cond Res 33(8): 2066-2073, 2019-This study examined the kinetic contributions of the upper extremities during countermovement vertical jumps (CMVJs) while using arm swing (AS) or no arm swing (NAS) conditions. Fourteen healthy men ((Equation is included in full-text article.)± SD; age = 24.1 ± 3.9 years) volunteered for this investigation. Subjects performed in random order a total of 6 jumps consisting of 3 AS and 3 NAS CMVJs. A motion capture system was used to analyze the kinetic data. Paired samples t-tests were used to examine the subjects' mean differences in the AS and NAS CMVJ trials (p<0.05). Results for all subjects were determined for each jump subjects were determined for each jump performed, with statistical analyses performed on mean values for all 3 jumps per subject. The AS significantly increased the vertical jump height (VJH) by an average of 0.07 ± 0.03 m (3.0 ± 1.3 inches). Dual-energy X-ray absorptiometry scans determined that the upper limbs were 12.0% of the total body mass. Movement of the upper limbs during the AS CMVJ produced 32.2 ± 7.0% of the total mean ground reaction force (GRF), and 11.3 ± 2.2% during the NAS CMVJ. The enhancement of performance when jumping using an AS resulted in a 13.6% increase in VJH. The contribution of the upper limbs during the AS CMVJ averaged 31.5% of the peak GRF, which occurred immediately before takeoff. The upper extremities can greatly influence vertical jump performances and the accompanying kinetics. When analyzing jump GRFs, one must be aware of how much the upper limbs contribute to these forces. In addition, proper AS mechanics must be emphasized when instructing correct jump technique.

New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST "all-against-all" methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology.

A glossary for big data in population and public health: discussion and commentary on terminology and research methods

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods.

Modeling the potential impact of emerging innovations on achievement of Sustainable Development Goals related to maternal, newborn, and child health

Background

Innovations that improve the affordability, accessibility, or effectiveness of health care played a major role in the Millennium Development Goal achievements and will be critical for reaching the ambitious new Sustainable Development Goal (SDG) health targets. Mechanisms to identify and prioritize innovations are essential to inform future investment decisions.

Methods

Innovation Countdown 2030 crowdsourced health innovations from around the world and engaged recognized experts to systematically assess their lifesaving potential by 2030. A health impact modeling approach was developed and used to quantify the costs and lives saved for select innovations identified as having great promise for improving maternal, newborn, and child health.

Results

Preventive innovations targeting health conditions with a high mortality burden had the greatest impact in regard to the absolute number of estimated lives saved. The largest projected health impact was for a new tool for small-scale water treatment that automatically chlorinates water to a safe concentration without using electricity or moving parts. An estimated 1.5 million deaths from diarrheal disease among children under five could be prevented by 2030 by scaling up use of this technology. Use of chlorhexidine for umbilical cord care was associated with the second highest number of lives saved.

Conclusions

The results show why a systematic modeling approach that can compare and contrast investment opportunities is important for prioritizing global health innovations. Rigorous impact estimates are needed to allocate limited resources toward the innovations with great potential to advance the SDGs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12962-017-0074-7) contains supplementary material, which is available to authorized users.

Materialized View Selection Using Bumble Bee Mating Optimization

Decision support systems (DSS) constitute one of the most crucial components of almost every corporation's information system. Data warehouse provides the DSS with massive volumes of quality corporate data for analysis. On account of the volume of corporate data, its processing time of on-line analytical queries is huge (in hours and days). Materialized views have been used to substantially improve query performance. Nevertheless, selecting appropriate sets of materialized views is an NP-Complete problem. In this paper, a new discrete bumble bee mating inspired view selection algorithm (BBMVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental results show that BBMVSA was able to select fairly good quality Top-K views incurring a lower TVEC. Materialization of the selected views would improve the overall data analysis of DSS and would facilitate the decision making process.

Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

Advanced Materials for Health Monitoring with Skin-Based Wearable Devices

Skin-based wearable devices have a great potential that could result in a revolutionary approach to health monitoring and diagnosing disease. With continued innovation and intensive attention to the materials and fabrication technologies, development of these healthcare devices is progressively encouraged. This article gives a concise, although admittedly non-exhaustive, didactic review of some of the main concepts and approaches related to recent advances and developments in the scope of skin-based wearable devices (e.g. temperature, strain, biomarker-analysis werable devices, etc.), with an emphasis on emerging materials and fabrication techniques in the relevant fields. To give a comprehensive statement, part of the review presents and discusses different aspects of these advanced materials, such as the sensitivity, biocompatibility and durability as well as the major approaches proposed for enhancing their chemical and physical properties. A complementary section of the review linking these advanced materials with wearable device technologies is particularly specified. Some of the strong and weak points in development of each wearable material/device are highlighted and criticized. Several ideas regarding further improvement of skin-based wearable devices are also discussed.

Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management

Purpose

Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.

Design/methodology/approach

Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.

Findings

Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.

Research limitations/implications

These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.

Originality/value

The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

Hierarchical nanoporous metals as a path toward the ultimate three-dimensional functionality

Nanoporous metals prepared via dealloying or selective leaching of solid solution alloys and compounds represent an emerging class of materials. They possess a three-dimensional (3D) structure of randomly interpenetrating ligaments/nanopores with sizes between 5 nm and several tens of micrometers, which can be tuned by varying their preparation conditions (such as dealloying time and temperature) or additional thermal coarsening. As compared to other nanostructured materials, nanoporous metals have many advantages, including their bicontinuous structure, tunable pore sizes, bulk form, good electrical conductivity, and high structural stability. Therefore, nanoporous metals represent ideal 3D materials with versatile functionality, which can be utilized in various fields. In this review, we describe the recent applications of nanoporous metals in molecular detection, catalysis, 3D graphene synthesis, hierarchical pore formation, and additive manufacturing (3D printing) together with our own achievements in these areas. Finally, we discuss possible ways of realizing the ultimate 3D functionality beyond the scope of nanoporous metals.