Cumulative impact assessment on air quality from multiple open pit mines

Both open pit mine and smelter projects can have significant impacts on a variety of environmental resources. Among these, air quality of the surroundings is a key issue. Additionally, the environmental assessment of projects demands integrated approach taking in account all pollution sources. Therefore, specific focus has been put on the methodology for assessing impacts on air quality. This paper proposes an integrated index of environmental impact aiming to quantify the air quality impact of industrial operations around an urban area, in case of scarcity of in situ acquired data. This proposed pollution index is demonstrated by a case study conducted in the municipality of Congonhas, Brazil, in which contributions of five open pit mines and two smelters were studied. To achieve the pollution index, the wind rose and distances from the centroids of effective areas to the urban nucleus itself were considered, taking into account the wind projection areas from windward impacting sources on the leeward impacted urban site. The proposed index also includes the squared wind speed (proportional to the kinetic energy transferred in uplifting dust from ground surface). The method proposed is considered suitable for impact assignment for each one of the air pollution sources in the neighborhood of studied area. Some fluid dynamic aspects of airborne particulate matter are also studied by proposing a new equation for threshold saltation speed.     

An evolutionary approach to complex schema matching

The schema matching problem can be defined as the task of finding semantic relationships between schema elements existing in different data repositories. Despite the existence of elaborated graphic tools for helping to find such matches, this task is usually manually done. In this paper, we propose a novel evolutionary approach to addressing the problem of automatically finding complex matches between schemas of semantically related data repositories. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances. Since we only exploit the data stored in the repositories for this task, we rely on matching strategies that are based on record deduplication (aka, entity-oriented strategy) and information retrieval (aka, value-oriented strategy) techniques to find complex schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. The results show that our approach is able to find complex matches with high accuracy, similar to that obtained by more elaborated (hybrid) approaches, despite using only evidence based on the data instances.     

Nomophobia: Dependency on virtual environments or social phobia?

Background

The increasing use of new technologies and virtual communication involving personal computers (PCs), tablets and mobile phones are causing changes in individuals’ daily habits and behavior. We report the case of an individual with social phobia who developed a dependency on communication through virtual environments (i.e., nomophobia), and used a PC as a form of relating to the outside world to reduce stress and to avoid direct social relations. Nomophobia refers to the discomfort or anxiety caused by the non-availability of a mobile phone, PC or any another virtual communication device. Social phobia is described as an anxiety disorder of chronic evolution.

Objective

To study nomophobia as a manifest behavior that might serve as an indication of a possible anxiety disorder.

Methodology

The treatment consisted of the use of medication, cognitive-behavioral therapy (CBT) and the application of evaluation tools (interviews, scales, inventories and questionnaires).

Results

The individual responded satisfactorily to medication and CBT treatment, which reduced his time using the PC and increased his exposure to real-life situations.

Conclusion

Nomophobic behavior produces changes in daily habits and can reveal other aspects to be investigated, such as the presence of comorbid mental disorders.     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

Clustering interval data through kernel-induced feature space

Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this paper, a partitioning clustering method in kernel-induce feature space for symbolic interval-valued data is introduced. The distance between an item and its prototype in feature space is expanded using a two-component mixture kernel to handle intervals. Moreover, tools for the partition and cluster interpretation of interval-valued data in feature space are also presented. To show the effectiveness of the proposed method, experiments with real and synthetic interval data sets were performed and a study comparing the proposed method with different clustering algorithms of the literature is also presented. The clustering quality furnished by the methods is measured by an external cluster validity index (corrected Rand index). These experiments showed the usefulness of the kernel K-means method for interval-valued data and the merit of the partition and cluster interpretation tools.     

Handwritten Data Clustering Using Agents Competition in Networks

In this paper, we study a new type of competitive learning scheme realized on large-scale networks. The model consists of several agents walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder agents. In the end of the process, each agent dominates a community (a strongly connected subnetwork). Here, the model is described by a stochastic dynamical system. In this paper, a mathematical analysis for uncovering the system’s properties is presented. In addition, the model is applied to solve handwritten digits and letters clustering problems. An interesting feature is that the model is able to group the same digits or letters even with considerable distortions into the same cluster. Computer simulations reveal that the proposed technique presents high precision of cluster detections, as well as low computational complexity.     

Student progress assessment with the help of an intelligent pupil analysis system

Students and lecturers would like to know how well students have learned the study materials being taught. A formal test or exam would cause needless stress for students. To resolve this problem, the authors of this article have developed an Intelligent Pupil Analysis (IPA) System. A sufficient amount of studies worldwide prove an interrelation between pupil size and a person's cognitive load. The obtained research results are comparable with the results from other similar studies. The original contribution of this article, compared to the research results published earlier, is as follows: the IPA System developed by the authors is superior to the traditional pupil analysis research due to the integration of pupil analysis with subsystems of decision support, recommender and intelligent tutoring systems and innovative Models of the Model-base, which permit a more detailed analysis of the knowledge attained by a student. This article ends with a case study to demonstrate the practical operation of the IPA System.     

Congestion-aware ant colony based routing algorithms for efficient application execution on Network-on-Chip platform

Networks-on-Chip (NoC) is an interesting option in design of communication infrastructures for embedded systems. It provides a scalable structure and balanced communication between the cores. Parallel applications that take advantage of the NoC architectures, are usually are communication-intensive. Thus, a big deal of data packets is transmitted simultaneously through the network. In order to avoid congestion delays that deteriorate the execution time of the implemented applications, an efficient routing strategy must be thought of carefully. In this paper, the ant colony optimization paradigm is explored to find and optimize routes in a mesh-based NoC. The proposed routing algorithms are simple yet efficient. The routing optimization is driven by the minimization of total latency during packets transmission between the tasks that compose the application. The presented performance evaluation is threefold: first, the impact of well-known synthetic traffic patterns is assessed; second, randomly generated applications are mapped into the NoC infrastructure and some synthetic communication traffics, that follow known patterns, are used to simulate real situations; third, sixteen real-world applications of the E3S and one specific application for digital image processing are mapped and their execution time evaluated. In both cases, the obtained results are compared to those obtained with known general purpose algorithms for deadlock free routing. The comparison avers the effectiveness and superiority of the ant colony inspired routing.     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.