Impact of IT Unit’s Decision Right on Organizational Risk Taking in IT

The objective of this study is to understand how the company’s risk-taking decisions in IT adoption are influenced by the decision right of its IT unit. The study builds a theoretical framework capturing how the IT unit’s decision right influences two determinants of risk taking, perceived risk and risk propensity. This framework also illustrates how the impacts of these two determinants on the actual actions of IT adoption are moderated by the IT unit’s decision right. The framework is empirically tested using a dataset on the adoption of electronic supply chain management (e-SCM) systems. The findings suggest that the IT unit’s decision right is not associated with the decrease in perceived risk. However, it is associated with the increase in risk propensity. Moreover, the IT unit’s decision right strengthens the positive association between risk propensity and e-SCM adoption, and weakens the negative association between perceived risk and e-SCM adoption

2-Chloro-5-chloro­meth­yl-1,3-thia­zole

In the title compound, C4H3Cl2NS, the chloro­methyl C and 2-position Cl atoms lie close to the mean plane of the thia­zole ring [deviations = 0.0568 (2) and 0.0092 (1) Å, respectively]. No classical hydrogen bonds are found in the crystal structure

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.</p

Multi-crop Contrastive Learning for Unsupervised Image-to-Image Translation

Recently, image-to-image translation methods based on contrastive learning achieved state-of-the-art results in many tasks. However, the negatives are sampled from the input feature spaces in the previous work, which makes the negatives lack diversity. Moreover, in the latent space of the embedings,the previous methods ignore domain consistency between the generated image and the real images of target domain. In this paper, we propose a novel contrastive learning framework for unpaired image-to-image translation, called MCCUT. We utilize the multi-crop views to generate the negatives via the center-crop and the random-crop, which can improve the diversity of negatives and meanwhile increase the quality of negatives. To constrain the embedings in the deep feature space,, we formulate a new domain consistency loss function, which encourages the generated images to be close to the real images in the embedding space of same domain. Furthermore, we present a dual coordinate channel attention network by embedding positional information into SENet, which called DCSE module. We employ the DCSE module in the design of generator, which makes the generator pays more attention to channels with greater weight. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proved through extensive comparison experiments and ablation research

ENAT-PT: An Enhanced NAT-PT Model

NAT-PT would allow IPv4 nodes to communicate with IPv6 nodes transparently by translating the IPv6 address into a registered V4 address. However, NAT-PT would fall flat when the pool of V4 addresses is exhausted. NAPT-PT multiplexes the registered address’ ports and will allow for a maximum of 63K outbound TCP and 63K UDP sessions per IPv4 address, but it is unidirectional. We present in this paper a novel solution ENAT-PT（an enhanced NAT-PT），which will allow for a great number of inbound sessions by using a single V4 address. By using ENAT-PT, we can visit V6 networks from a V4 network with a small address pool