From research to practice: the effects of the jointly sponsored dissemination of an arthritis self-care nursing intervention

Various educational programs have been developed and found to be effective in the self-management of arthritis. This study reexamined the effectiveness of one such program, "Bone Up On Arthritis" (BUOA), when the program was delivered by a community-based service organization to a sample of persons (N = 154) who differed widely in disease type and demographic characteristics. Arthritis Foundation staff implemented BUOA at four national sites; data were collected and analyzed by University of Michigan nurse researchers. Investigators found improved scores on all outcome measures (self-care behavior, helplessness, pain, dysfunction, and depression). These findings suggest that "Bone Up" is an effective nursing intervention in multiple organizational and community environments and for diverse patient populations.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...     

Optimal locations of on-line and off-line rework stations in a serial production system

The production rate and product quality are two vital concerns for any manufacturing industry. Number of defective items reduces production rate and increases unit production cost. Moreover, if nonconforming items reach to the customers then manufacturer’s goodwill may drastically go down. Thus, quality inspection is treated as an inherent part of manufacturing. In this research, an N-stage serial production line with an inspection station at the end of it is considered to make decisions concerning this issue. On detecting a defective item at the end of the line it is scrapped or repaired at regular workstation or is sent to an off-line rework station for repair. Assuming each workstation produces a single type of defect a unit cost function is developed for alternative decisions on each type of defect. In order to minimise the unit cost of production and determine an appropriate decision for individual defect types, a fractional mixed integer nonlinear programming is formulated. After transformation to a mixed integer linear programming problem it is solved optimally. A small problem from garments industry is described in detail to show the solution procedure with a branch and bound method. Empirical tests with up to 40 workstations are permed to show the efficiency of the solution process.     

Inhibition of neuronal calcium channels by a novel peptide spider toxin, DW13.3

Peptide toxins have proved to be useful agents, both in discriminating between different components of native calcium channel currents and in the molecular isolation and designation of their cloned channel counterparts. Here, we describe the isolation and characterization of the biochemical and physiological properties of a novel 74-amino acid peptide toxin (DW13.3) extracted from the venom of the spider Filistata hibernalis. The subtype specificity of DW13.3 was investigated using calcium channel currents recorded from two separate expression systems and several different cultured mammalian cell preparations. Overall, DW13.3 potently blocked all native calcium channel currents studied, with the exception of T-type currents recorded from GH3 cells. Examination of transiently expressed calcium channels in oocytes showed that DW13.3 had the highest affinity for alpha1A, followed by alpha1B > alpha1C > alpha1E. The affinity of DW13.3 for alpha1B N-type currents varied by 10-fold between expressed channels and native currents. Although block occurred in a similar 1:1 manner for all subtypes, DW13.3 produced a partial block of both alpha1A currents and P-type currents in cerebellar Purkinje cells. Selective occlusion of the P/Q-type channel ligand omega-conotoxin MVIIC (but not omega-agatoxin IVA) from its binding site in Purkinje neurons suggests that DW13.3 binds to a site close to the pore of the channel. The inhibition of different subtypes of calcium channels by DW13.3 reflects a common "macro" binding site present on all calcium channels except T-type.     

The crystal structure of the immunity protein of colicin E7 suggests a possible colicin-interacting surface

The immunity protein of colicin E7 (ImmE7) can bind specifically to the DNase-type colicin E7 and inhibit its bactericidal activity. Here we report the 1.8-angstrom crystal structure of the ImmE7 protein. This is the first x-ray structure determined in the superfamily of colicin immunity proteins. The ImmE7 protein consists of four antiparallel alpha-helices, folded in a topology similar to the architecture of a four-helix bundle structure. A region rich in acidic residues is identified. This negatively charged area has the greatest variability within the family of DNase-type immunity proteins; thus, it seems likely that this area is involved in specific binding to colicin. Based on structural, genetic, and kinetic data, we suggest that all the DNase-type immunity proteins, as well as colicins, share a "homologous-structural framework" and that specific interaction between a colicin and its cognate immunity protein relies upon how well these two proteins' charged residues match on the interaction surface, thus leading to specific immunity of the colicin.     

Multimodal fusion for multimedia analysis: a survey

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

A multi-agent system model of a diesel engine using rule induction

The proliferation of a multi-agent system (MAS) and ideas from Artificial Intelligence (AI)/distributed AI have changed the way systems, in general are controlled, and operation of a system (diesel engine) in particular is automated. In this paper a distributed multi-agent architecture for a diesel engine and the knowledge sources that handle electricity generation is developed. Electronic devices and components used for data handling are described. The sensed data are presented in fuzzy logic and calculated in entropy values and depicted in a decision hierarchy. A comparative performance assessment of the proposed multi-agent based system with an existing system is presented and discussed.