Miracle-dz/UniFAR-FTUs huggingface dataset:
On reconfiguration in case of node mobility in clustered wireless sensor networks

Wireless sensor networks are expected to be one of the key enabling technologies in the next 10 years. Protocols for such networks should be highly flexible in order to adapt to topology changes due to node mobility. In this article we focus on strategies for management of node mobility in clustered wireless sensor networks based on IEEE 802.15. 4/ZigBee standards. Taking into account that modification of the cluster structure leads to performance degradation of the network reconfiguration strategies, help in avoiding long reconnection times or missing connections is required. In our work we propose a combination of proactive and reactive reconfiguration strategies to reorganize clustered structures with node mobility, leading to increased connectivity, faster reconnection times after connection loss, and building energy-efficient compact cluster-tree topologies.
DSP Based Implementation of Fuzzy Precompensated PI Speed Controller for Vector Controlled PMSM Drive

This paper deals with the analysis and hardware implementation of a fuzzy precompensated proportional-integral (FPPI) speed controller for vector controlled (VC) permanent magnet synchronous motor (PMSM) drive using a digital signal processor. The power circuit of the PMSM drive consists of insulated gate bipolar transistor (IGBT) based voltage source inverter (VSI) and the gate driver circuit. The hardware of control circuit has current sensors and interfacing circuits. The FPPI speed controller, rotor speed estimation using sensed position signals, the vector controller and PWM current controller for the control of VSI are implemented in assembly language of DSP-ADMC401. The simulation model of the drive system is developed in MATLAB environment with simulink, PSB and FLC toolboxes to analyze the performance of PMSM drive system. Simulated results are validated with test results of the PMSM drive for starting, speed reversal and load perturbation
Activities worth living for: call to action beyond prognosis.

Given the advances in the treatment options for chronic heart failure (CHF), the estimated 5 million people in the United States living with CHF have realized gradual improvements in their median life expectancy.1 Expectantly, there has been an increased focus on how well these patients live in addition to their longevity. The measurements of the wellness of patients include both patient-reported outcome tools and provider-based assessments. The model that links disease burden to these patient-centered outcome measures is well established in the literature2 and serves as the basis for novel interventions to improve these outcomes. Activities of daily living (ADLs) often involve many factors that are intermediate variables in the determinants of health-related quality of life and are often taken for granted when not evaluated in the context of disease burden. Despite these ADLs often being differentially influenced by the specific disease and the associated symptoms, patients may have impairments in their ability to perform because of factors unrelated to the specific disease. Although ADLs are important in clinical management, the focus on measurements as well as interventions to improve these important tasks is often assessed by physical and occupational therapist, nurses, social workers, and care coordinators with less attention by physicians. Article see p 261 There are many valid and reliable instruments used to measure ADLs in research studies and clinical care. The most …
Classic reaction kinetics can explain complex patterns of antibiotic action

Chemical reaction kinetics explain three different effects of drug-mediated bacterial killing. Antibiotics, pure and simple Antibiotics are powerful tools in fighting bacterial infection, but overuse and misuse are taking their tolls, leading to development of drug-resistant bacteria. Abel zur Wiesch et al. now report that simple chemical binding kinetics can explain three effects of antibiotics previously considered to have different causes: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. They report a theoretical model that links chemical reaction kinetics to bacterial population biology and validate this model both experimentally and with data from a tuberculosis clinical trial. This model may help optimize dosing and aid rational design of antibiotic treatment strategies. Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R2 = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our “chemical reaction kinetics”–based approach may inform new strategies, which are based on rational design.
The improvement and implementation in the algorithm of finding maximal motifs in DNA sequences

DNA sequences are formed by nucleotides A;C; G; T. Often, a particular word called motif can occur in many sequences of a group. There has been an algorithm to find both the frequent and the maximal motif in the DNA sequences, but it has some disadvantages. Such as the time and place cost is very high. In the paper, we build a tree based on the motif length to improve it, and implement a small system according to the original and improved algorithm. Then we compare the latest new algorithms about the problem with them, and give the empirical result of them.
Collocation Extraction Based on Modifiability Statistics

We introduce a new, linguistically grounded measure of collocativity based on the property of limited modifiability and test it on German PP-verb combinations. We show that our measure not only significantly outperforms the standard lexical association measures typically employed for collocation extraction, but also yields a valuable by-product for the creation of collocation databases, viz. possible structural and lexical attributes. Our approach is language-, structure-, and domain-independent because it only requires some shallow syntactic analysis (e.g., a POS-tagger and a phrase chunker).
Real-Time Filtering for Pulsing Public Opinion in Social Media

When analysing social media conversations, in search of the public opinion about an unfolding event that is being discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: opinion-makers and opinion-holders. To address this problem, we propose a supervised machinelearning approach, which uses inexpensively acquired labeled data from mono-thematic Twitter accounts to learn a binary classifier for the labels “political account” (opinion-makers) and “non-political account” (opinion-holders). While the classifier has a 83% accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97% accuracy. This high accuracy derives from our decision to incorporate information about classifier probability into the classification. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread.
Remixing Work, Family and Leisure: Teleworkers' Experiences of Everyday Life

This paper explores whether and in what ways telework is associated with a reconfiguration or remixing of daily work, family and leisure activities. Semi‐structured interviews were conducted with 51 teleworkers employed in a financial organisation in Canada. For some, telework was a condition of employment, while others negotiated part‐time telework arrangements with managers. Using interpretive thematic analysis techniques, intersections and inter‐relationships between experiences of work, family and leisure were identified. Three main themes emerged, including the need to not only protect, but also containing work time and space; the significance of family and being available for children; and, the relative devaluation of leisure. Although it was anticipated that differences between involuntary and voluntary teleworkers would be evident, gender and family stage were more influential in structuring daily life. The flexibility of telework was valued, but there was little evidence of a reconfiguration of life spheres except for women with children at home.
How round is a protein? Exploring protein structures for globularity using conformal mapping

We present a new algorithm that automatically computes a measure of the geometric difference between the surface of a protein and a round sphere. The algorithm takes as input two triangulated genus zero surfaces representing the protein and the round sphere, respectively, and constructs a discrete conformal map f between these surfaces. The conformal map is chosen to minimize a symmetric elastic energy ES(f) that measures the distance of f from an isometry. We illustrate our approach on a set of basic sample problems and then on a dataset of diverse protein structures. We show first that ES(f) is able to quantify the roundness of the Platonic solids and that for these surfaces it replicates well traditional measures of roundness such as the sphericity. We then demonstrate that the symmetric elastic energy ES(f) captures both global and local differences between two surfaces, showing that our method identifies the presence of protruding regions in protein structures and quantifies how these regions make the shape of a protein deviate from globularity. Based on these results, we show that ES(f) serves as a probe of the limits of the application of conformal mapping to parametrize protein shapes. We identify limitations of the method and discuss its extension to achieving automatic registration of protein structures based on their surface geometry.
An Interagency Network Perspective on HIV Prevention

Objective: The objective of this study was to describe the network structures of agencies and individuals engaged in HIV prevention in a North Carolina county. Study Design: The authors conducted a cross-sectional study of interconnected agencies and individuals identified through snowball sampling. Participants were interviewed face to face with a structured questionnaire about interactions with others in the network. Network characteristics such as density and centralization were computed with UCINET software. Results: The network of 11 agencies had an information exchange density of 14%. Exchanges of clients and funds were even more rare. The network of 17 individuals influential in HIV prevention was better connected with a density of 42%. Conclusions: The HIV prevention agencies in the county were not functioning as a network and thus were not benefiting the community to their full potential. Any connectedness between the agencies may be attributable to the better connections between influential individuals, several of whom worked for the agencies in the agency network.
On Periodicity Detection and Structural Periodic Similarity

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive maintenance and in the medical sciences for accurate classification and anomaly detection.
Cultural Sport Psychology

Chapter 1. Introduction to Cultural Sport Psychology Chapter 2. Rationale for Developing a Cultural Sport Psychology Chapter 3. Engaging Cultural Studies and Traditional Sport Psychology Chapter 4. Understanding Your Role in Cultural Sport Psychology Chapter 5. Cultural Diversity Issues in Applied Sport Psychology Chapter 6. Strategies for Reflective Cultural Sport Psychology Research Chapter 7. Strategies for Reflective Cross-Cultural Sport Psychology Practice Chapter 8. Working With Canadian Indigenous Athletes Chapter 9. Multicultural Sport Psychology in the USA Chapter 10. Working With Athletes in Brazil Chapter 11. Sport Psychology Consulting in Russia and Sweden Chapter 12. Working With Athletes in Israel Chapter 13. A Canadian Sport Psychologist in Kuwait Chapter 14. Working With Nigerian Athletes Chapter 15. Working with Ghanaian Athletes Chapter 16. Working With Australian Aboriginal Athletes Chapter 17. Working With Singaporean Athletes Chapter 18. Working With Japanese Athletes.
Preservation of neurons of the nucleus basalis in subcortical ischemic vascular disease.

OBJECTIVE To investigate loss of neurons in the nucleus basalis (NB) of Meynert in patients with subcortical ischemic vascular disease (SIVD) compared with healthy controls, patients with Alzheimer disease (AD), and patients with mixed AD and SIVD. DESIGN Autopsied cases drawn from a longitudinal observational study of patients with SIVD, patients with AD, and healthy controls. SETTING Multi center, university-affiliated, program project neuropathology core. PATIENTS Patients with pathologically defined SIVD (n = 16), AD (n = 20), and mixed AD and SIVD (n = 10) and healthy controls matched by age and educational level (n = 17) were studied. MAIN OUTCOME MEASURES The NB neuronal cell counts in each group and their correlation with the extent of magnetic resonance imaging white matter lesions and Clinical Dementia Rating (CDR) scores closest to death. RESULTS No significant loss of neurons was found in SIVD patients compared with age-matched controls in contrast to the AD and mixed groups, who had significant neuronal loss. A significant inverse correlation between NB neurons and CDR scores was found in the AD group but not in the SIVD and mixed groups. The NB cell counts were not correlated with either the extent of white matter lesions or cortical gray matter volume in the SIVD or AD groups. CONCLUSIONS These findings inveigh against primary loss of cholinergic neurons in SIVD patients but do not rule out the possibility of secondary cholinergic deficits due to disruptions of cholinergic projections to cerebral cortex.
CA8 Mutations Cause a Novel Syndrome Characterized by Ataxia and Mild Mental Retardation with Predisposition to Quadrupedal Gait

We describe a consanguineous Iraqi family in which affected siblings had mild mental retardation and congenital ataxia characterized by quadrupedal gait. Genome-wide linkage analysis identified a 5.8 Mb interval on chromosome 8q with shared homozygosity among the affected persons. Sequencing of genes contained in the interval revealed a homozygous mutation, S100P, in carbonic anhydrase related protein 8 (CA8), which is highly expressed in cerebellar Purkinje cells and influences inositol triphosphate (ITP) binding to its receptor ITPR1 on the endoplasmatic reticulum and thereby modulates calcium signaling. We demonstrate that the mutation S100P is associated with proteasome-mediated degradation, and thus presumably represents a null mutation comparable to the Ca8 mutation underlying the previously described waddles mouse, which exhibits ataxia and appendicular dystonia. CA8 thus represents the third locus that has been associated with quadrupedal gait in humans, in addition to the VLDLR locus and a locus at chromosome 17p. Our findings underline the importance of ITP-mediated signaling in cerebellar function and provide suggestive evidence that congenital ataxia paired with cerebral dysfunction may, together with unknown contextual factors during development, predispose to quadrupedal gait in humans.
Hit me baby one more time: the engendering of violence in children's discourse in South African schools

This article explores the disjuncture between discourses of equity, equality, and consideration as evident in the formal secondary schooling curriculum (DoE, 2003) and learners' physical and speech behaviour in two schools in KwaZulu-Natal between 1999–2005, despite critical literacy interventions in these schools. The article draws on Shefer, Shefer and Foster's (2000) research on Discourses of Power and Violence in Students' Talk at UWC. Discourses which are intimidatory, harassing and threatening are often used to girls, and when boys talk together girls are often referred to in a demeaning manner. Our research reveals that the abuse is subtle and generally underplayed by the school. In particular, we argue that both the educational and home environments support the patriarchal stereotypes which remain evident in children's language and behaviour.
Journal of Theoretical and Applied Information Technology an Efficient Algorithm for Clustering Intrusion Alert

Intrusion Detection System is an emerging technology for detecting the unauthorized users and malicious behavior in a system. Alert supervision is tedious in intrusion system, so Meta alerts are created. Meta alerts are generated for appropriate clusters and they form a generalization of alerts. The objective is to identify origin of these alerts. In this paper, we propose a hybrid clustering algorithm which is applied to the data set to cluster the alert. Online alert aggregation is applied to this data which identifies the intruder . Redundant data are filtered during the process of clustering and aggregation, which substantially reduces the false positive rate. From the observed false positive, the origin of the alert are reduced. 1. INTRODUCTION Protecting our data in the internet is a great threat. Intruders and hackers make an attempt to pilfering our data. Intrusion can be viewed in many traditions. Most of them have their own conventions to find attacks. Intrusion process begins when an unauthorized person endeavors to access data. Rapid growth of threat to data causes evolution of Intrusion Detection System (IDS). Many kinds of Intrusion Detection systems are available. James Anderson was first to recommend that the audit trail should be monitored for threats. A generic intrusion detection model was first proposed by Dorothy Denning [21]. It is a standard method, which uses a rule-based pattern matching to find the behavior of the jarring. It generates a statistical model and can split the audit logs into system calls and report the information. Generally, there are Physical IDS and Digital IDS. Physical IDS are used for particular area say a building or room whereas Digital IDS are used to produce alarm for computer network or computers. Intrusion detection usually provides the same function as an alarm system. Misuse and Anomaly are the two common approaches used by IDS. Misuse Detection follows a pattern to known attacks and therefore a major concern is to create library for the known attacks. Depending upon the known intrusion in library, pattern matching-based intrusion detection works. Pattern matching intrusion detection is capable of finding the common attacks and security problems as patterns are known. Events are monitored and it is more efficient than statistical analysis. Performance depends on the size of the database used. Machine learning is not utilized in pattern matching systems. Pattern databases need to be updated for finding the new type of attacks and complexities to …
Complement‐mediated tumor‐specific cell lysis by antibody combinations targeting epidermal growth factor receptor (EGFR) and its variant III (EGFRvIII)

Monoclonal antibodies (mAb) against variant III of epidermal growth factor receptor (EGFRvIII) hold promise for improving tumor selectivity of EGFR‐targeted therapy. Here, we compared Fc‐mediated effector functions of three mAb against EGFRvIII (MR1‐1, ch806, 13.1.2) with those of zalutumumab, a high affinity EGFR mAb in advanced clinical trials. MR1‐1 and ch806 demonstrated preferential and 13.1. 2 exclusive binding to EGFRvIII, in contrast to zalutumumab, which bound both wild‐type and EGFRvIII. All four human IgG1κ mAb mediated antibody‐dependent cellular cytotoxicity (ADCC) of EGFRvIII‐expressing cells with mononuclear cells and isolated monocytes, while only zalutumumab in addition triggered ADCC by polymorphonuclear cells. Interestingly, combinations of zalutumumab and EGFRvIII mAb specifically mediated complement‐dependent cytotoxicity (CDC) of EGFRvIII‐transfected but not wild‐type cells. Moreover, EGFRvIII‐specific CDC was significantly enhanced when zalutumumab was combined with a Fc‐engineered variant of MR1‐1 (K326A/E333A). These observations confirm the immunotherapeutic potential of antibody combinations against EGFR, and demonstrate that tumor selectivity can be improved by combining therapeutic EGFR mAb with an antibody against EGFRvIII. (Cancer Sci 2011; 102: 1761–1768)
Spatial and Temporal Groundwater Level Variation Geostatistical Modeling in the City of Konya, Turkey

Groundwater is one of the most important resources used for drinking and utility and irrigation purposes in the city of Konya, Turkey, as in many areas. The purpose of this study is to evaluate spatial and temporal changes in the level of groundwater by using geostatistical methods based on data from 91 groundwater wells during the period 1999 to 2003. Geostatistical methods have been used widely as a convenient tool to make decisions on the management of groundwater levels. To evaluate the spatial and temporal changes in the level of the groundwater, a vector‐based geographic information system software package, ArcGIS 9.1 (Environmental Systems Research Institute, Redlands, California), was used for the application of an ordinary kriging method, with cross‐validation leading to the estimation of groundwater levels. The average value of variogram (spherical model) for the spatial analysis was approximately 2150 m. Results of ordinary kriging for groundwater level drops were underestimated by 17%. Cross‐validation errors were within an acceptable level. The kriging model also helps to detect risk‐prone areas for groundwater abstraction.