Hubungan Pengetahuan Keselamatan Kerja dengan Kewaspadaan Terhadap Kecelakaan Kerja Pada Karyawan Bagian Pengisian LPG PT. Pertamina (Persero) Fuel Retail Marketing Region VII Sulawesi

Dari hasil penelitian tampak bahwa nilai p= 0,004< 0,05 sehingga Ho ditolak yang menyatakan bahwa ada hubungan antara pengetahuan keselamatan kerja dengan kewaspadaan terhadap kecelakaan kerja pada karyawan. Sedangkan koefisien kontigensi sebesar 1,00 maka dapat diketahui hubungan antara pengetahuan keselamatan kerja dengan kewaspadaan terhadap kecelakaan kerja adalah sangat kuat. Keselamatan kerja adalah suatu pemikiran dan upaya untuk menjamin keutuhan dan kesempurnaan manusia baik jasmani maupun rohani serta karya dan budayanya yang tertuju pada kesejahteraan manusia pada umumnya dan tenaga kerja pada khususnya. Pengetahuan tentang keselamatan kerja seorang karyawan ini akan berpengaruh pada kewaspadaan terhadap kecelakaan kerja. Penelitian dilakukan dengan tujuan untuk mengetahui hubungan antara pengetahuan keselamatan kerja dengan kewaspadaan terhadap kecelakaan kerja pada karyawan

Properties of the Environment of Galaxies in Clusters of Galaxies CL 0024+1654 and RX J0152.7−1357

We report the results of combined analyses of X-ray and optical data of two galaxy clusters, CL 0024+1654 and RX J0152.7−1357 at redshift z = 0.395 and z = 0.830, respectively, offering a holistic physical description of the two clusters. Our X-ray analysis yielded temperature and density profiles of the gas in the intra-cluster medium (ICM). Using optical photometric and spectroscopic data, complemented with mass distribution from a gravitational lensing study, we investigated any possible correlation between the physical properties of the galaxy members, i.e. their color, morphology, and star formation rate (SFR), and their environments. We quantified the properties of the environment around each galaxy by galaxy number density, ICM temperature, and mass density. Although our results show that the two clusters exhibit a weaker correlation compared to relaxed clusters, it still confirms the significant effect of the ICM on the SFR in the galaxies. The close relation between the physical properties of galaxies and the condition of their immediate environment found in this work indicates the locality of galaxy evolution, even within a larger bound system such as a cluster. Various physical mechanisms are suggested to explain the relation between the properties of galaxies and their environment

Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in astronomical images is challenging since they are overwhelmed by the abundance of non-lenses. To address this, we have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) -- for instance, ResNet, Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision transformers (ViTs) trained on realistic galaxy-quasar lens simulations based on the Hyper Suprime-Cam (HSC) multiband images. While the individual model exhibits remarkable performance when evaluated against the test dataset, achieving an area under the receiver operating characteristic curve of $>$97.3% and a median false positive rate of 3.6%, it struggles to generalize in real data, indicated by numerous spurious sources picked by each classifier. A significant improvement is achieved by averaging these CNNs and ViTs, resulting in the impurities being downsized by factors up to 50. Subsequently, combining the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover $z>1.5$ lensed quasars with Einstein radii of $\theta_\mathrm{E}<5$ arcsec. Afterward, the ensemble classifier indicates 3080 sources with a high probability of being lenses, for which we visually inspect, yielding 210 prevailing candidates awaiting spectroscopic confirmation. These outcomes suggest that automated deep learning pipelines hold great potential in effectively detecting strong lenses in vast datasets with minimal manual visual inspection involved.Comment: Accepted for publication in the Astronomy & Astrophysics journal. 28 pages, 11 figures, and 3 tables. We welcome comments from the reade

銀河グループによる重力レンズの研究：高赤方偏移にわたる中心集中度と質量の関係

要約のみTohoku University千葉柾司課

When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multiband Imaging Data

Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ _E ≤ 1″. Visual inspection is then performed for sources with CNN scores of P _lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches