Search

Although researchers have found that patient weight bias negatively impacts health care professionals, research is limited on client weight bias toward counselors. Given that a client's perception of their counselor impacts the therapeutic alliance, more research is needed to understand client weight bias toward counselors. To fill this research gap, we conducted a quasi-experimental study examining people's weight bias toward a hypothetical counselor who was overweight, average weight, or underweight. Participants (N = 189) received a random assignment to a questionnaire featuring one of the three hypothetical counselors. Participants indicated their willingness to trust them, select them as a counselor, and follow their counsel. Results from a Welch ANOVA analysis showed a statistically significantly greater preference for average-weight and overweight counselors than those who are underweight. Additionally, the participants were less willing to follow counsel from overweight and underweight counselors. Implications for counselors are discussed. [ABSTRACT FROM AUTHOR]

The experimental plot is placed in the orchard "Codru-ST" Ltd. founded in 2006. The study subject of the experience was Idared apple variety grafted on M 9 rootstock. The plant spacing is 3.5 x 1.2 m. The research was conducted during the period of 2014-2017 years. The active substances applied were NAD (Geramid-New), NAA (Dirager) and BA (Gerba 4 LG), using different doses and different thinning periods. During the research, such indicators as the number and average weight of the fruit, the yield per tree and per unit area and the average fruit diameter were studied. It was established that, the spray with Geramid-New in dose 1.2 l/ha when 80% of the petals have fallen + 2-3 days, Dirager in dose 0.2 l/ha when the king fruit diameter was 8-9 mm, and Gerba 4 LG in dose 2.0 l/ha when the king fruit diameter was 10-12 mm have a significant effect on the number of the fruits, average weight, yield and fruits diameter. [ABSTRACT FROM AUTHOR]

The Multi-Criteria Decision-Making (MCDM) process of milling SNCM439 steel is presented in this study. In this experimental study, 3 cutting tool parameters, namely the number of pieces, cutting piece material, and tip radius were considered and 3 cutting mode parameters, i.e. cutting speed, feed rate, and depth of cut changed in each experiment. SR and MRR are selected as the output parameters of the milling process. The PARIS method was used for MCDM, in which, the weights of SR and MRR were determined by 3 methods, namely AW, EW, and MW. Twenty-seven sets of ranking results for 27 alternatives (experiments) are presented. The GINI index was used to evaluate the stability of ranking alternatives. The results have determined the value of 6 input parameters to ensure the minimum SR and the maximum MRR simultaneously. [ABSTRACT FROM AUTHOR]

Density-Based clustering, an important clustering analysis approach, has several advantages of discovering clusters with arbitrary shapes and identifying noise samples etc. However, existing density-based clustering approaches cannot effectively identify clusters in density-sparse region. In this paper, we propose a density-based clustering approach for dataset with density-sparse region by using density connection weight. Firstly, we define a series of new concepts, such as density connection weight and average weight. And data objects are divided into core objects, boundary objects and noise objects according to average weight, which can effectively distinguish core objects and boundary objects in density-sparse region. Secondly, a density clustering assignment strategy is constructed based on density connection weight. A density clustering algorithm, together with the dimensionality reduction using t-SNE, judiciously handles the low-density clusters in multi-density datasets and the minimal clusters in extremely imbalanced datasets. In the end, the experimental results on the UCI and synthetic datasets validate that our algorithm exhibits prominent clustering performance, especially while being adapted in diverse clustering tasks, including the multi-density, imbalanced and uniform distribution datasets. • New concepts of density connection weight etc., are defined for selecting core objects. • A filtering method of dividing data objects is proposed using average weight. • A cluster allocation strategy is constructed using density connection weight. • A density connection weight-based clustering algorithm is proposed. [ABSTRACT FROM AUTHOR]

Quantifying recreational fishing harvest by weight is vital for stock assessments and fisheries management. As it is impractical to obtain weights of all fish caught by recreational fisheries, harvest is often calculated from estimated catch in numbers multiplied by an estimate of average weight (usually as an arithmetic mean). The average weight may be derived from measured weights, as well as imputed weights from measured lengths using established length-weight relationships. This study evaluates the impact of uncertainty in lengths and weights from three independent data sources in determining average weights and estimated recreational harvest of four demersal species in Western Australia. Data sources included measured lengths and weights from on-site surveys of boat-based fishers, self-reported lengths from charter-boat logbooks and lengths from biological samples voluntarily donated by recreational fishers for stock assessments. For the latter two data sources, weights were imputed from length measurements. Generalised linear models were used to obtain estimates of standardised average weight and standard errors for each data source, across four years (2011/12, 2013/14, 2015/16 and 2017/18) and three spatial management zones within the West Coast Bioregion of Western Australia (Mid West, Metropolitan and South West). For each species, harvest estimates and 95% confidence intervals were derived from standardised average weights (from aforementioned surveys) and estimated catch in numbers (from off-site surveys). Standardised average weights from GLMs were found to be more precise than arithmetic means and data from the charter-boat logbooks and biological samples generally produced higher harvest estimates. The application of standardised weights from management zones to estimate recreational harvest at the bioregion level reduced the error of estimates. Addressing uncertainty from self-reporting, data sets (charter-boat logbooks and biological samples) and small sample sizes (on-site surveys) can increase confidence in recreational harvest estimates. [ABSTRACT FROM AUTHOR]