Thermodynamic model for MnO-containing slags and gas-slag-metal equilibrium in ferromanganese smelting

The thermodynamics of MnO-containing slags was reviewed, and the properties of such slags were correlated by application of the cell model. This model, together with a previously developed unified interaction parameter model for the metal phase, was used to simulate the gas-slag-metal conditions in the hearth of a ferromanganese submerged arc furnace. The calculated slag and metal compositions and distribution of Mn, Si, and K between slag, metal, and fume were compared with plant data and satisfactory agreement was obtained. A steady recirculation of potassium inside the furnace was also predicted. For a potassium percentage in the raw materials of 0.63 pct, the amount of the recirculating potassium was about 160 kg per metric ton of ferromanganese. Due to the recirculation, the average potassium content in the solid mix coming down to the hearth could be as high as 7 pct.     

Limb salvage surgery for widely infiltrating bony sarcomas

OBJECTIVE: To determine whether neoadjuvant chemotherapy and radiotherapy for bony sarcomas extending into soft tissues would allow limb salvage yet maintain local disease control. DESIGN: A prospective cohort study. SETTING: A university-affiliated cancer centre in Alberta. PATIENTS: All patients with potentially curable, widely infiltrating bony sarcomas of the extremity without neurologic deficit, referred to the centre in the 6 years from January 1984 to December 1990. There were 11 patients; 1 did not complete the protocol. The mean follow-up was 24 months. INTERVENTIONS: Adriamycin (doxorubicin) was infused for 3 days at a rate of 30 mg/d. A few days later radiotherapy was given 5 days a week for 10 doses at a rate of 3.0 Gy per dose. Four to 5 weeks later the tumour was excised surgically, with placement of a bone allograft or prosthesis, allowing a 1-cm margin of healthy soft tissue and a 5-cm margin of healthy bone and marrow cavity whenever possible. MAIN OUTCOME MEASURES: Need for limb amputation, infectious complications, recurrence of local or regional disease. RESULTS: One patient underwent amputation after fracture through the tumour site. There were two postoperative infections, one acute and one chronic. All patients had full neurologic function of the distal limb. There was no local or regional recurrence of disease. CONCLUSION: Neoadjuvant chemotherapy followed by radiotherapy and tumour excision provides control of aggressive local bone sarcomas while maintaining limb integrity.     

Pathways and crossroads to institutional leadership.

When upwardly mobile persons are promoted from lower individual contributor roles to higher, managerial roles, they are confronted by the challenge of negotiating a series of 135-degree "crossroads" or shifts in their careers. Those who make the complete journey must traverse five pathways and four crossroads. These critical career crossroads consist of discontinuous and unprecedented changes in the role responsibilities and account abilities to which managers in transition must respond. At each crossroads, people are confronted by a triple challenge: letting go of anachronistic responsibilities and competencies, preserving those that continue to be useful, and adding new discontinuous responsibilities and consequences. Managers in transition can cope with these demands by making adaptive changes in their preferred activities, behavior patterns, and style. A detailed examination of the unprecedented discontinuities that ambitious, upwardly mobile managers must bridge is presented. The adaptation process that is required at each career crossroads is described, followed by a detailed set of recommended shifts in behavior patterns for the future executive at each crossroads. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

High nutrient loading and climatic parameters influence the dominance and dissimilarity of toxigenic cyanobacteria in northern bays of Lake Victoria

Eutrophication of Lake Victoria led to changes in its phytoplankton communities. However, different levels of eutrophication exist in the open lake and the bays, and between embayments. This study utilized spatial and temporal sampling of Napoleon Gulf and Murchison Bay, exhibiting different trophic conditions. Over one year, we investigated phytoplankton biomass, richness, diversity and dissimilarity, and related the dynamics of the dominant species to the limnological and climatic conditions. The results confirmed that Napoleon Gulf and Murchison Bay showed large differences in eutrophication status, with lower nutrient concentrations in Napoleon Gulf than in Murchison Bay, where a strong gradient was observed from inshore to offshore areas. These nutrient dynamics resulted in a 4 to 10 fold higher chlorophyll-a in Murchison Bay than in Napoleon Gulf. From the embayments, 135 phytoplankton taxa were recorded with no significant differences in alpha diversity. However, high dissimilarity in community structure was observed in beta diversity, mostly due to a turnover among the dominant toxigenic species. Thus, from a similar species pool, there was a shift in the dominant toxigenic cyanobacteria from Microcystis flos-aquae and M. aeruginosa in Murchison Bay, Dolichospermum circinale and Planktolyngbya circumcreta in Napoleon Gulf to D. circinale in the offshore stations. These cyanobacteria are toxigenic taxa with known health hazards. Using partial least square models, we showed that both climatic variables (e.g. wind, solar radiation) and levels of inorganic dissolved nutrients (e.g. SRP, NO₃^–, and NH₄⁺) are the main drivers of differences and dominance in cyanobacteria communities in northern Lake Victoria.     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Action recognition via bio-inspired features: The richness of center–surround interaction

Motion is a key feature for a wide class of computer vision approaches to recognize actions. In this article, we show how to define bio-inspired features for action recognition. To do so, we start from a well-established bio-inspired motion model of cortical areas V1 and MT. The primary visual cortex, designated as V1, is the first cortical area encountered in the visual stream processing and early responses of V1 cells consist in tiled sets of selective spatiotemporal filters. The second cortical area of interest in this article is area MT where MT cells pool incoming information from V1 according to the shape and characteristic of their receptive field. To go beyond the classical models and following the observations from Xiao et al. [61], we propose here to model different surround geometries for MT cells receptive fields. Then, we define the so-called bio-inspired features associated to an input video, based on the average activity of MT cells. Finally, we show how these features can be used in a standard classification method to perform action recognition. Results are given for the Weizmann and KTH databases. Interestingly, we show that the diversity of motion representation at the MT level (different surround geometries), is a major advantage for action recognition. On the Weizmann database, the inclusion of different MT surround geometries improved the recognition rate from 63.01 ± 2.07% up to 99.26 ± 1.66% in the best case. Similarly, on the KTH database, the recognition rate was significantly improved with the inclusion of MT different surround geometries (from 47.82 ± 2.71% up to 92.44 ± 0.01% in the best case). We also discussed the limitations of the current approach which are closely related to the input video duration. These promising results encourage us to further develop bio-inspired models incorporating other brain mechanisms and cortical areas in order to deal with more complex videos.     

Method for determining musculotendon parameters in subject-specific musculoskeletal models of children developed from MRI data

Accurate knowledge of muscle-tendon parameters in biomechanical models is critical for accurate simulation and analyses of human movement. An excellent example of this is the creation of subject-specific models from magnetic resonance imaging (MRI). When Hill-type muscle models are used to calculate muscle forces, the determination of muscle attachment points, optimal fiber length, tendon slack length and maximum isometric force all have a significant influence on the joint moment-angle behavior of the model. In the present study a method was developed for customizing the values of muscle-tendon parameters in a generic model to create subject-specific biomechanical models from MRI. The method was applied by generating musculoskeletal models for the biomechanical simulation platform OpenSim, but the workflow is equally well applicable to other simulation platforms. New computational algorithms are described for identifying joint centers and for reconstructing the centroids of the muscle bellies from MRI. A?process is also described for the extraction of the muscle paths and for identifying the positions of ??via-points?? used to model muscles wrapping over bones. Finally, a new algorithm is described for adjusting the values of optimal fiber length, tendon slack length and maximum isometric force based on a comparison of the model results with experiment. We tested our computational algorithms by developing subject-specific biomechanical models of five typically developed children (age 9.5±1.7?years) from MRI. The joint moment-angle relationships calculated for the subject-specific models were similar to those determined for corresponding scaled generic models. The results indicate that the proposed methodology is suitable for developing subject-specific models of healthy children. Future studies should investigate how abnormalities of the musculoskeletal system, such as tibial torsion and muscle spasticity, can be integrated into the modeling process.     

A fast audio similarity retrieval method for millions of music tracks

We present a filter-and-refine method to speed up nearest neighbor searches with the Kullback–Leibler divergence for multivariate Gaussians. This combination of features and similarity estimation is of special interest in the field of automatic music recommendation as it is widely used to compute music similarity. However, the non-vectorial features and a non-metric divergence make using it with large corpora difficult, as standard indexing algorithms can not be used. This paper proposes a method for fast nearest neighbor retrieval in large databases which relies on the above approach. In its core the method rescales the divergence and uses a modified FastMap implementation to speed up nearest-neighbor queries. Overall the method accelerates the search for similar music pieces by a factor of 10–30 and yields high recall values of 95–99% compared to a standard linear search.     

How landscape ruggedness influences the performance of real-coded algorithms: a comparative study

Ruggedness has a strong influence on the performance of algorithms, but it has been barely studied in real-coded optimization, mainly because of the difficulty of isolating it from a number of involved topological properties. In this paper, we propose a framework consisting of increasing ruggedness function sets built by a mechanism which generates multiple funnels. This mechanism introduces different levels of sinusoidal distortion which can be controlled to isolate the singular influence of some related features. Some commonly used measures of ruggedness have been applied to analyze these sets of functions, and a numerical study to compare the performance of some representative algorithms has been carried out. The results confirm that ruggedness has an influence on the performance of the algorithm, proving that it depends on the multi-funnel structure and peak features, such as height and relative size of the global peak, and not on the number of peaks.     

The Neurovascular Unit Dysfunction in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common neurodegenerative disease worldwide. Histopathologically, AD presents with two hallmarks: neurofibrillary tangles (NFTs), and aggregates of amyloid β peptide (Aβ) both in the brain parenchyma as neuritic plaques, and around blood vessels as cerebral amyloid angiopathy (CAA). According to the vascular hypothesis of AD, vascular risk factors can result in dysregulation of the neurovascular unit (NVU) and hypoxia. Hypoxia may reduce Aβ clearance from the brain and increase its production, leading to both parenchymal and vascular accumulation of Aβ. An increase in Aβ amplifies neuronal dysfunction, NFT formation, and accelerates neurodegeneration, resulting in dementia. In recent decades, therapeutic approaches have attempted to decrease the levels of abnormal Aβ or tau levels in the AD brain. However, several of these approaches have either been associated with an inappropriate immune response triggering inflammation, or have failed to improve cognition. Here, we review the pathogenesis and potential therapeutic targets associated with dysfunction of the NVU in AD.