31 research outputs found
Searching for scalar field dark matter with short-range gravity experiments
The nature of dark matter remains a mystery, although enormous efforts have
been made to search for dark matter candidate particles. Scalar field dark
matter is one of the most prominent options that is being explored by the
various precision experiments, such as gravitational-wave detectors, atomic
clocks and gravity experiments. We describe a direct search for scalar field
dark matter using the short-range gravity experiments, in which we investigate
the possible influences of scalar field dark matter as a function of its mass.
By analyzing the torque signals in the torsion pendulum experiments of the
HUST-18 and HUST-20, we set new constraints on the large mass regions of scalar
field dark matter parameter space. Based on the maximum reach analysis (MRA)
method, the constraints on the photon coupling parameter and
electron coupling parameter improve on limits from
previous direct searches in interferometer experiments by more than four orders
of magnitude. Further combining the HUST-18 and HUST-20 experiments, we also
present the exclusion limits that are not dependent on MRA approximation. This
work paves the way for dark-matter search in future HUST experiments, and the
projected constraints can be competitive with those limits produced by the MRA
method.Comment: 13 pages, 5 fiure
Mechanistic study of visible light-driven CdS or g-C<sub>3</sub>N<sub>4</sub>-catalyzed CâH direct trifluoromethylation of (hetero)arenes using CF<sub>3</sub>SO<sub>2</sub>Na as the trifluoromethyl source
The mild and sustainable methods for CâH direct trifluoromethylation of (hetero)arenes without any base or strong oxidants are in extremely high demand. Here, we report that the photo-generated electron-hole pairs of classical semiconductors (CdS or g-C3N4) under visible light excitation are effective to drive CâH trifluoromethylation of (hetero)arenes with stable and inexpensive CF3SO2Na as the trifluoromethyl (TFM) source via radical pathway. Either CdS or g-C3N4 propagated reaction can efficiently transform CF3SO2Na to [rad]CF3 radical and further afford the desired benzotrifluoride derivatives in moderate to good yields. After visible light initiated photocatalytic process, the key elements (such as F, S and C) derived from the starting TFM source of CF3SO2Na exhibited differential chemical forms as compared to those in other oxidative reactions. The photogenerated electron was trapped by chemisorbed O2 on photocatalysts to form superoxide radical anion (O2[rad]â) which will further attack [rad]CF3 radical with the generation of inorganic product Fâ and CO2. This resulted in a low utilization efficiency of [rad]CF3 (<50%). When nitro aromatic compounds and CF3SO2Na served as the starting materials in inert atmosphere, the photoexcited electrons can be directed to reduce the nitro group to amino group rather than being trapped by O2. Meanwhile, the photogenerated holes oxidize SO2CF3â into [rad]CF3. Both the photogenerated electrons and holes were engaged in reductive and oxidative paths, respectively. The desired product, trifluoromethylated aniline, was obtained successfully via one-pot free-radical synthesis.</p
Corrigendum to: The TianQin project: current progress on science and technology
In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article
Genomic and Proteomic Analyses of the Fungus Arthrobotrys oligospora Provide Insights into Nematode-Trap Formation
Nematode-trapping fungi are âcarnivorousâ and attack their hosts using specialized trapping devices. The morphological development of these traps is the key indicator of their switch from saprophytic to predacious lifestyles. Here, the genome of the nematode-trapping fungus Arthrobotrys oligospora Fres. (ATCC24927) was reported. The genome contains 40.07 Mb assembled sequence with 11,479 predicted genes. Comparative analysis showed that A. oligospora shared many more genes with pathogenic fungi than with non-pathogenic fungi. Specifically, compared to several sequenced ascomycete fungi, the A. oligospora genome has a larger number of pathogenicity-related genes in the subtilisin, cellulase, cellobiohydrolase, and pectinesterase gene families. Searching against the pathogen-host interaction gene database identified 398 homologous genes involved in pathogenicity in other fungi. The analysis of repetitive sequences provided evidence for repeat-induced point mutations in A. oligospora. Proteomic and quantitative PCR (qPCR) analyses revealed that 90 genes were significantly up-regulated at the early stage of trap-formation by nematode extracts and most of these genes were involved in translation, amino acid metabolism, carbohydrate metabolism, cell wall and membrane biogenesis. Based on the combined genomic, proteomic and qPCR data, a model for the formation of nematode trapping device in this fungus was proposed. In this model, multiple fungal signal transduction pathways are activated by its nematode prey to further regulate downstream genes associated with diverse cellular processes such as energy metabolism, biosynthesis of the cell wall and adhesive proteins, cell division, glycerol accumulation and peroxisome biogenesis. This study will facilitate the identification of pathogenicity-related genes and provide a broad foundation for understanding the molecular and evolutionary mechanisms underlying fungi-nematodes interactions
Effects of Off-Farm Work on Farm Household Production Choices
Using a unique panel of rice-producing Chinese households, this paper tests off-farm employmentâs effects on agricultural production. We find the sizable rural out-migration in the past two decades has had negligible effects on Chinaâs rice production. This cannot be explained by farm labor market perfection or any technological improvements financed by off-farm income; rather, evidence points to the persistence of disguised unemployment in 21st century China
Multi-task Optimization Based Co-training for Electricity Consumption Prediction
Real-world electricity consumption prediction may involve different tasks,
e.g., prediction for different time steps ahead or different geo-locations.
These tasks are often solved independently without utilizing some common
problem-solving knowledge that could be extracted and shared among these tasks
to augment the performance of solving each task. In this work, we propose a
multi-task optimization (MTO) based co-training (MTO-CT) framework, where the
models for solving different tasks are co-trained via an MTO paradigm in which
solving each task may benefit from the knowledge gained from when solving some
other tasks to help its solving process. MTO-CT leverages long short-term
memory (LSTM) based model as the predictor where the knowledge is represented
via connection weights and biases. In MTO-CT, an inter-task knowledge transfer
module is designed to transfer knowledge between different tasks, where the
most helpful source tasks are selected by using the probability matching and
stochastic universal selection, and evolutionary operations like mutation and
crossover are performed for reusing the knowledge from selected source tasks in
a target task. We use electricity consumption data from five states in
Australia to design two sets of tasks at different scales: a) one-step ahead
prediction for each state (five tasks) and b) 6-step, 12-step, 18-step, and
24-step ahead prediction for each state (20 tasks). The performance of MTO-CT
is evaluated on solving each of these two sets of tasks in comparison to
solving each task in the set independently without knowledge sharing under the
same settings, which demonstrates the superiority of MTO-CT in terms of
prediction accuracy.Comment: accepted by the 2022 IEEE International Joint Conference on Neural
Networks (IJCNN 2022