The relationship between work-related psychological health and psychological type among clergy serving in the Presbyterian Church (USA)

This study examines the relationship between work-related psychological health and the Jungian model of psychological type among a sample of 748 clergy serving within The Presbyterian Church (USA). Psychological type was assessed by the Francis Psychological Type Scales which provide classification in terms of orientation (extraversion or introversion), perceiving (sensing or intuition), judging (thinking or feeling) and attitude toward the outer world (extraverted judging or extraverted perceiving). Work-related psychological health was assessed by the Francis Burnout Inventory which distinguishes between positive affect (the Satisfaction in Ministry Scale) and negative affect (the Scale of Emotional Exhaustion in Ministry). The data demonstrated that these clergy display high levels of negative affect coupled with high levels of positive affect. The data also confirmed that the main association between work-related psychological health and psychological type is a function of the orientations (the source of psychological energy). Compared with clergy who prefer introversion, clergy who prefer extraversion display both higher levels of satisfaction in ministry and lower levels of emotional exhaustion in ministry. These findings are consistent with the theory that the extraverted nature of ministry requires introverted clergy to operate for considerable periods of time outside their preferred orientations, with the consequent loss of energy and the consequent erosion of psychological rewards. Strategies are suggested for enabling introverted clergy to cope more effectively and more efficiently with the extraverted demands of ministry

One hundred angstrom niobium wire

Composite of fine niobium wires in copper is used to study the size and proximity effects of a superconductor in a normal matrix. The niobium rod was drawn to a 100 angstrom diameter wire on a copper tubing

Optical Flow in Mostly Rigid Scenes

The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201

A Simple Calculus for Discrete Systems, Part B

Mathematical model for man machine development cycle

Screening of organically based fungicides for apple scab (Venturia inaequalis) control and a histopathological study of the mode of action of a resistance inducer.

A range of possible substitutes for copper-based fungicides for control of apple scab (Venturia inaequalis) in organic growing were tested in laboratory and growth chamber experiments in the Danish project StopScab (2002-2004). Eighteen crude plant extracts, 19 commercial plant-based products and 6 miscellaneous compounds were tested for their ability to reduce scab symptoms on apple seedlings. Most of the compounds were also tested for their effect on conidium germination on glass slides. Fourteen of the crude plant extracts, 13 of the commercial plant products and 5 of the miscellaneous compounds showed promising control efficacies when used either preventively or curatively in the plant assay. A histopathological study was carried out on the mode of action of the resistance inducer, acibenzolar-S-methyl (ASM), which reduced scab severity and sporulation on apple seedlings in several plant assays when applied as preventive treatment. The effect of the inducer on key pre- and post-penetration events of V. inaequalis was studied and compared to these events in water-treated control leaves. The histopathological study showed that the inducer had its strongest effect on post-penetration events indicated by delayed infection and reduced stroma development. In addition, a small but significant inhibition of conidial germination and a stimulation of germ tube length were observed. This investigation provides new histopathological evidence for the mode of action of ASM against V. inaequalis and serves as a model for evaluation of the mechanisms by which the organically based fungicides reduce infection of V. inaequalis

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.Comment: CVPR 201