Manganese coordination chemistry of bis(imino)phenoxide derived [2 + 2] Schiff-base macrocyclic ligands

The [2 + 2] Schiff base macrocycles [2,2'-(CH₂CH₂)(C₆H₄N)₂-2,6-(4-RC₆H₃OH)]₂ (IʳH₂), upon reaction with MnCl₂ (two equivalents) afforded the bimetallic complex [Cl₃Mn(NCMe)][MnCl(IᵗᵇᵘH₂)] (2). Under similar conditions, use of the related [2 + 2] oxy-bridged macrocycle [2,2'-O(C₆H₄N=CH)₂4-RC₆H₃OH] (IIʳH₂), afforded the bimetallic complexes [(MnCl)₂IIʳ] (R = Me 3, tBu 4), whilst the macrocycle derived from 1,2-diaminobenzene and 5,5'-di-tert-butyl-2,2'-dihydroxy-3,3'-methylenedibenzaldehyde (IIIH₄) afforded the complex [(MnCl)₂(III)]·2MeCN (5·2MeCN). For comparative studies, the salt complexes [2,6-(ArNHCH)₂-4-MeC₆H₂O][MnCl₃(NCMe)] (Ar = 2,4-Me₂C₆H₃, 6) and {[2,6-(ArNHCH)₂-4-MeC₆H₂O][MnCl}₂[MnCl₄]·8CH₂Cl₂ (Ar = 4-MeC₆H₄, 7·8CH₂Cl₂) were prepared. The crystal structures of 1 - 7 are reported (synchrotron radiation was necessary for complexes 1, 3 and 5). Complexes 1 - 7 (not 5) were screened for their potential to act as pre-catalysts for the ring opening polymerization (ROP) of ε-caprolactone; 3, 4 and 6, 7 were inactive, whilst 1 and 2 exhibited only poor activity low conversion (<15 %) at temperatures above 60 °C

Stable Hebbian learning from spike timing-dependent plasticity

We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition Changes in the synaptic connections between neurons are widely believed to contribute to memory storage, and the activitydependen

An islanding detection method for multi-DG systems based on high-frequency impedance estimation

Active islanding detection methods are generally employed for grid-connected inverter-based Distributed Generation (DG). However, there might be mutual influences and power quality issues caused by the disturbance signal when multiple inverters are involved. To address those problems, this paper analyzes the potential failure mechanism of the f-Q (frequency-reactive power) drifting active method in multiple-DG situations. Then, a novel high frequency transient injection based islanding detection method that is suitable for both single and multiple-DGs is proposed. Compared with the conventional injection methods, a high frequency impedance model for DG is provided for better theoretical analysis. By means of the intermittent Time Domain Low Voltage Condition (TDLVC) injection control, this method can achieve good accuracy and reduce disturbances to power system

Ubiquitous text interaction

Computer-based interactions increasingly pervade our everyday environments. Be it on a mobile device, a wearable device, a wall-sized display, or an augmented reality device, interactive systems often rely on the consumption, composition, and manipulation of text. The focus of this workshop is on exploring the problems and opportunities of text interactions that are embedded in our environments, available all the time, and used by people who may be constrained by device, situation, or disability. This workshop welcomes all researchers interested in interactive systems that rely on text input or output. Participants should submit a short position statement outlining their background, past work, future plans, and suggesting a use-case they would like to explore in-depth during the workshop. During the workshop, small teams will form around common or compelling use-cases. Teams will spend time brainstorming, creating low-fidelity prototypes, and discussing their use-case with the group. Participants may optionally submit a technical paper for presentation as part of the workshop program. The workshop serves to sustain and build the community of text entry researchers who attend CHI. It provides an opportunity for new members to join this community, soliciting feedback from experts in a small and supportive environment

Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data

Welcome, How Can I Help You? Design Considerations for a Virtual Reality Environment to Support the Orientation of Online Initial Teacher Education Students

Alongside the rapid and broad uptake of online learning in higher education, fully online students report feeling isolated and disconnected from their institutions. Although formal course content may be expertly designed to engage online learners, much of the information provided to support higher education students’ orientation to the institution and to study is presented online in a written static form. Such presentations may not be accessible and engaging and may contribute to feelings of disconnection. Technologies such as virtual reality (VR) are being used in higher education to engage, motivate and connect students in their learning. This paper reports on the early design stages for a VR that aims to support initial teacher education students to connect and engage with key orienting information. The design of the VR was achieved by following a user-centred, iterative engineering design process and design principles of spatiality, interaction and narrative. The VR environment emulates the School of Education’s physical, on-campus reception area to provide an immersive experience where students have a choice in the types and format of key study information they receive. This experience was designed to be utilised in online orientation but also throughout students’ first year of study. Future research directions include collecting student responses to the VR to inform how students can be involved in enhancing the VR so that it supports their learning and sense of connection. Furthermore, future research can aim for the expansion of the VR inclusive of additional information, rooms and buildings and increased capabilities such as gamification and mobile access. This will enable the creation of a valuable teaching resource for online programs

Advanced DC zonal marine power system protection

A new Active Impedance Estimation (AIE) based protection strategy which is suitable for utilization in a DC zonal marine power distribution system is presented. This method uses two triangular current "spikes" injections for system impedance estimation and protection when faults are detected. By comparing the estimated impedance with the pre-calibrated value, the fault location can be predicted and fault can be isolated without requiring communication between two injection units. Using co¬operated double injections and line current measurement (directional fault detection), faults in the system with same impedance and different fault positions can be distinguished, located and isolated. The proposed method is validated using experimental test results derived from a 30kW, 400V, twin bus DC marine power system demonstrator. The experimental tests were applied to both faults during normal operation and faults that occur during system restoration

Voltage-Driven Conformational Switching with Distinct Raman Signature in a Single-Molecule Junction

Precisely controlling well-defined, stable single-molecule junctions represents a pillar of single-molecule electronics. Early attempts to establish computing with molecular switching arrays were partly challenged by limitations in the direct chemical characterization of metalâEuro"moleculeâEuro"metal junctions. While cryogenic scanning probe studies have advanced the mechanistic understanding of current- and voltage-induced conformational switching, metalâEuro"moleculeâEuro"metal conformations are still largely inferred from indirect evidence. Hence, the development of robust, chemically sensitive techniques is instrumental for advancement in the field. Here we probe the conformation of a two-state molecular switch with vibrational spectroscopy, while simultaneously operating it by means of the applied voltage. Our study emphasizes measurements of single-molecule Raman spectra in a room-temperature stable single-molecule switch presenting a signal modulation of nearly 2 orders of magnitude

Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities

In many cities around the world the overall air quality is improving, but at the same time nitrogen dioxide (NO2) trends show stagnating values and in many cases could not be reduced below air quality standards recommended by the World Health Organization (WHO). Many large cities have built monitoring stations to continuously measure different air pollutants. While most stations follow defined rules in terms of measurement height and distance to traffic emissions, the question remains of how representative are those point measurements for the city-wide air quality. The question of the spatial coverage of a point measurement is important because it defines the area of influence and coverage of monitoring networks, determines how to assimilate monitoring data into model simulations or compare to satellite data with a coarser resolution, and is essential to assess the impact of the acquired data on public health. In order to answer this question, we combined different measurement data sets consisting of path-averaging remote sensing data and in situ point measurements in stationary and mobile setups from a measurement campaign that took place in Munich, Germany, in June and July 2016. We developed an algorithm to strip temporal from spatial patterns in order to construct a consistent NO2 pollution map for Munich. Continuous long-path differential optical absorption spectroscopy (LP DOAS) measurements were complemented with mobile cavity-enhanced (CE) DOAS, chemiluminescence (CL) and cavity attenuated phase shift (CAPS) instruments and were compared to monitoring stations and satellite data. In order to generate a consistent composite map, the LP DOAS diurnal cycle has been used to normalize for the time of the day dependency of the source patterns, so that spatial and temporal patterns can be analyzed separately. The resulting concentration map visualizes pollution hot spots at traffic junctions and tunnel exits in Munich, providing insights into the strong spatial variations. On the other hand, this database is beneficial to the urban planning and the design of control measures of environment pollution. Directly comparing on-street mobile measurements in the vicinity of monitoring stations resulted in a difference of 48 %. For the extrapolation of the monitoring station data to street level, we determined the influence of the measuring height and distance to the street. We found that a measuring height of 4 m, at which the Munich monitoring stations measure, results in 16 % lower average concentrations than a measuring height of 1.5 m, which is the height of the inlet of our mobile measurements and a typical pedestrian breathing height. The horizontal distance of most stations to the center of the street of about 6 m also results in an average reduction of 13 % compared to street level concentration. A difference of 21 % in the NO2 concentrations remained, which could be an indication that city-wide measurements are needed for capturing the full range and variability of concentrations for assessing pollutant exposure and air quality in cities

Positive spiritual climate supports transformational leadership as means to reduce nursing burnout and intent to leave

AimTo explore the relationship between spiritual climate and transformational leadership, and examine their impact on nurses perceived emotional exhaustion and intentions to quit.BackgroundTransformational leadership is known to have a significant positive effect on work environment and job satisfaction. Additionally, promoting spiritual climate amongst staff can benefit workers by increasing self‐worth. The relationship between the two is unknown.MethodsNurse clinicians from 2 sites in the Jiangsu Province of China completed self‐report questionnaires based on spiritual climate, emotional exhaustion, clinical leadership and Turnover Intention Scales. Mediation analysis was applied to evaluate impact of spiritual climate.ResultsPerceived positive spirituality amongst nurse clinicians reinforces transformational leadership to reduce emotional exhaustion (indirect effect of −0.089, p < .01). Burnout and intention to leave showed significantly positive correlation with lower levels of perceived spirituality (r = .545, p < .01).ConclusionTransformational leadership in the workplace can reduce nurses' burnout, and a positive spiritual climate increases meaningfulness in their work. This may help in nurse retention.Implications for Nursing ManagementHealth care leaders must look beyond transformational leadership to maintain a positive and supportive clinical climate, and this may involve acknowledgement of nurses' spiritual needs