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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Higher Education’s Effect on Retention: Exploring the Experiences of CPS Caseworkers
This qualitative research study was an exploration of Child Protective Services (CPS) frontline caseworkers\u27 experiences. The problem investigated was the high number of caseworkers who lacked the knowledge and skills necessary to do their job and the constant turnover of workers within the agency. Although previous researchers have explored many factors that cause caseworker turnover, the research has not focused much on the caseworkers’ perspective. Therefore, this qualitative study explored CPS caseworkers’ opinions and experiences using virtual semistructured interviews. The study specifically focused on the impact that education and training has on job performance and turnover. The study population was CPS caseworkers who had worked in a large urban community in Texas for 2 years or more. The sample included eight caseworkers who had experienced the turnover firsthand. Thematic analysis of the transcripts of the audio-recorded interviews and data coding using NVivo software led to the development of a coding system to identify patterns and common themes. The findings indicated that for caseworkers, hands-on training, gaining experience, unity between caseworkers, social service training, and leadership support may lead to decreased turnover rates and increased job performance. Agency leaders and other professionals in the social service field may consider the findings to improve caseworker retention and organizational outcomes. These improvements could prompt positive change through leadership and policy adjustments designed to support the needs of CPS caseworkers related to education, training, and retention.
Keywords: burnout, CPS Caseworker, case assignable, organizational commitment, services, statewide intake, voluntary turnove
Transverse Velocity Field Measurement in High-Resolution Solar Images Based on Deep Learning
To address the problem of the low accuracy of transverse velocity field
measurements for small targets in high-resolution solar images, we proposed a
novel velocity field measurement method for high-resolution solar images based
on PWCNet. This method transforms the transverse velocity field measurements
into an optical flow field prediction problem. We evaluated the performance of
the proposed method using the Ha and TiO datasets obtained from New Vacuum
Solar Telescope (NVST) observations. The experimental results show that our
method effectively predicts the optical flow of small targets in images
compared with several typical machine- and deep-learning methods. On the Ha
dataset, the proposed method improves the image structure similarity from
0.9182 to 0.9587 and reduces the mean of residuals from 24.9931 to 15.2818; on
the TiO dataset, the proposed method improves the image structure similarity
from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to
17.0194. The optical flow predicted using the proposed method can provide
accurate data for the atmospheric motion information of solar images. The code
implementing the proposed method is available on
https://github.com/lygmsy123/transverse-velocity-field-measurement.Comment: 14 pages, 10 figures, 4 tables. Accepted for publication in Research
in Astronomy and Astrophysic
Local dimer dynamics in higher dimensions
We consider local dynamics of the dimer model (perfect matchings) on
hypercubic boxes . These consist of successively switching the dimers
along alternating cycles of prescribed (small) lengths. We study the
connectivity properties of the dimer configuration space equipped with these
transitions. Answering a question of Freire, Klivans, Milet and Saldanha, we
show that in three dimensions any configuration admits an alternating cycle of
length at most 6. We further establish that any configuration on
features order alternating cycles of length at most . We also
prove that the dynamics of dimer configurations on the unit hypercube of
dimension is ergodic when switching alternating cycles of length at most
. Finally, in the planar but non-bipartite case, we show that
parallelogram-shaped boxes in the triangular lattice are ergodic for switching
alternating cycles of lengths 4 and 6 only, thus improving a result of Kenyon
and R\'emila, which also uses 8-cycles. None of our proofs make reference to
height functions.Comment: 14 pages, 4 figure
An Ethereum-compatible blockchain that explicates and ensures design-level safety properties for smart contracts
Smart contracts are crucial elements of decentralized technologies, but they
face significant obstacles to trustworthiness due to security bugs and
trapdoors. To address the core issue, we propose a technology that enables
programmers to focus on design-level properties rather than specific low-level
attack patterns. Our proposed technology, called Theorem-Carrying-Transaction
(TCT), combines the benefits of runtime checking and symbolic proof. Under the
TCT protocol, every transaction must carry a theorem that proves its adherence
to the safety properties in the invoked contracts, and the blockchain checks
the proof before executing the transaction. The unique design of TCT ensures
that the theorems are provable and checkable in an efficient manner. We believe
that TCT holds a great promise for enabling provably secure smart contracts in
the future. As such, we call for collaboration toward this vision
Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs
Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention
a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..
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