Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms

AbstractThis work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. RESULTS: To develop our ML-based Bio-NER system, we employ conditional random fields, which have performed effectively in several well-known tasks, as our underlying ML model. Adding selected conjunction features, applying numerical normalization, and employing pattern-based post-processing improve the F-scores by 1.67%, 1.04%, and 0.57%, respectively. The combined increase of 3.28% yields a total score of 72.98%, which is better than the baseline system that only uses singleton features. CONCLUSION: We demonstrate the benefits of using the sequential forward search algorithm to select effective conjunction feature groups. In addition, we show that numerical normalization can effectively reduce the number of redundant and unseen features. Furthermore, the Smith-Waterman local alignment algorithm can help ML-based Bio-NER deal with difficult cases that need longer context windows

The Evolution of Population III and Extremely Metal-Poor Binary Stars

Numerical simulations have now shown that Population III (Pop III) stars can form in binaries and small clusters and that these stars can be in close proximity to each other. If so, they could be subject to binary interactions such as mass exchange that could profoundly alter their evolution, ionizing UV and Lyman-Werner (LW) photon emission and explosion yields, with important consequences for early cosmological reionization and chemical enrichment. Here we investigate the evolution of Pop III and extremely metal-poor binary stars with the MESA code. We find that interactions ranging from stable mass transfer to common envelope evolution can occur in these binaries for a wide range of mass ratios and initial separations. Mass transfer can nearly double UV photon yields in some of these binaries with respect to their individual stars by extending the life of the companion star, which in turn can enhance early cosmological reionization but also suppress the formation of later generations of primordial stars. Binary interactions can also have large effects on the nucleosynthetic yields of the stars by promoting or removing them into or out of mass ranges for specific SN types. We provide fits to total photon yields for the binaries in our study for use in cosmological simulations

Various criteria in the evaluation of biomedical named entity recognition

BACKGROUND: Text mining in the biomedical domain is receiving increasing attention. A key component of this process is named entity recognition (NER). Generally speaking, two annotated corpora, GENIA and GENETAG, are most frequently used for training and testing biomedical named entity recognition (Bio-NER) systems. JNLPBA and BioCreAtIvE are two major Bio-NER tasks using these corpora. Both tasks take different approaches to corpus annotation and use different matching criteria to evaluate system performance. This paper details these differences and describes alternative criteria. We then examine the impact of different criteria and annotation schemes on system performance by retesting systems participated in the above two tasks. RESULTS: To analyze the difference between JNLPBA's and BioCreAtIvE's evaluation, we conduct Experiment 1 to evaluate the top four JNLPBA systems using BioCreAtIvE's classification scheme. We then compare them with the top four BioCreAtIvE systems. Among them, three systems participated in both tasks, and each has an F-score lower on JNLPBA than on BioCreAtIvE. In Experiment 2, we apply hypothesis testing and correlation coefficient to find alternatives to BioCreAtIvE's evaluation scheme. It shows that right-match and left-match criteria have no significant difference with BioCreAtIvE. In Experiment 3, we propose a customized relaxed-match criterion that uses right match and merges JNLPBA's five NE classes into two, which achieves an F-score of 81.5%. In Experiment 4, we evaluate a range of five matching criteria from loose to strict on the top JNLPBA system and examine the percentage of false negatives. Our experiment gives the relative change in precision, recall and F-score as matching criteria are relaxed. CONCLUSION: In many applications, biomedical NEs could have several acceptable tags, which might just differ in their left or right boundaries. However, most corpora annotate only one of them. In our experiment, we found that right match and left match can be appropriate alternatives to JNLPBA and BioCreAtIvE's matching criteria. In addition, our relaxed-match criterion demonstrates that users can define their own relaxed criteria that correspond more realistically to their application requirements

Clip-on Gadgets: Expanding Multi-touch Interaction Area with Unpowered Tactile Controls

ABSTRACT Virtual keyboards and controls, commonly used on mobile multi-touch devices, occlude content of interest and do not provide tactile feedback. Clip-on Gadgets solve these issues by extending the interaction area of multi-touch devices with physical controllers. Clip-on Gadgets use only conductive materials to map user input on the controllers to touch points on the edges of screens; therefore, it is batteryfree, lightweight, and low-cost. In addition, it can be used in combination with multi-touch gestures. We present several hardware designs and a software toolkit, which enable users to simply attach Clip-on Gadgets to an edge of a device and start interacting with it

Disseminated Mycobacterium kansasii Infection Associated with Skin Lesions: A Case Report and Comprehensive Review of the Literature

Mycobacteruim kansasii occasionally causes disseminated infection with poor outcome in immunocompromised patients. We report the first case of disseminated M. kansasii infection associated with multiple skin lesions in a 48-yr-old male with myelodysplastic syndrome. The patient continuously had taken glucocorticoid during 21 months and had multiple skin lesions developed before 9 months without complete resolution until admission. Skin and mediastinoscopic paratracheal lymph node (LN) biopsies showed necrotizing granuloma with many acid-fast bacilli. M. kansasii was cultured from skin, sputum, and paratracheal LNs. The patient had been treated successfully with isoniazid, rifampin, ethmabutol, and clarithromycin, but died due to small bowel obstruction. Our case emphasizes that chronic skin lesions can lead to severe, disseminated M. kansasii infection in an immunocompromised patient. All available cases of disseminated M. kansasii infection in non HIV-infected patients reported since 1953 are comprehensively reviewed

Association of Suicide Risk With Headache Frequency Among Migraine Patients With and Without Aura

Background: Migraines with aura have been associated with suicide in adolescents and young adults, but the association between suicide and migraine frequency has not been determined. This study investigated suicidal ideation and suicide attempts among patients with varying frequencies of migraines, with and without auras.Methods: This cross-sectional study analyzed 528 patients aged between 20 and 60 years from a headache outpatient clinic in Taiwan. All patients completed a set of questionnaires, including a demographic questionnaire, the Migraine Disability Assessment questionnaire, the Hospital Anxiety and Depression Scale, the Beck Depression Inventory, and the Pittsburgh Sleep Quality Index. Suicide risk was evaluated by self-reported lifetime suicidal ideation and attempts. Patients were divided into low-frequency (1–4 days/month), moderate-frequency (5–8 days/month), high-frequency (9–14 days/month), and chronic (≥15 days/month) migraine groups. The association between migraine frequency and suicidality was investigated using multivariable linear regression and logistic regression.Results: The rates of suicidal ideation and suicide attempts were the highest for chronic migraine with aura (ideation: 47.2%; attempts: 13.9%) and lowest in migraine-free controls (2.8%). Migraine frequency was an independent risk factor for suicidal ideation and attempts in patients with aura (both Ptrend < 0.001), but not in patients without auras. Migraine aura and depression were associated with higher risks of suicidal ideation and suicide attempts in patients with migraine.Conclusion: High migraine frequency has a correlation with high suicide risk in patients who experience an aura, but not in other patients with migraine