605 research outputs found
The Economic Stall in Turkey: Causes and Impacts
In recent years, multiple dilemmas in regards of politics, security, diplomacy and economy, especially the lasting economic stall are faced by Turkey. Why did Turkey fell from the dazzling emerging economy into a stagnant economy? It cannot be ignorable that the stalling economy has highlighted the internal contradictions in domestic political and economic systems. The reasons why Turkish “economic miracle” suffered crisis mainly are the weakening of internal momentum, over-reliance on foreign investment, declining of the driving force of reform, as well as multiple internal and external crises. The stalling economy has largely weakened the economic foundation of the Turkish model, promoting the turbulence and conservative trend of domestic politics and speeding up the authoritarianization of Turkish president Erdoğan. The stalling economy, coupled with worsening diplomatic dilemma and dim democracy, led to the crisis of the Turkish model. The crisis of the model has impacted on Turkey’s international influence and its relations with other powers as well. It is also important to explore Turkey's economic problems for understanding the current Middle East and the changes of emerging economies in the world. Keywords: Turkey; Economic stall; multiple dilemmas; the Turkish mode
Oil and Beyond: Sino-Saudi Strategic Economic Relationship
The purpose of this paper is to analyze the dynamics and the expanding strategic economic contents of Sino-Saudi economic relationship in new era. With the risings of China and Saudi Arabia and expansion of the fields of cooperation, Sino-Saudi economic relationship is becoming strategically prominent in 21st Century. The development of Sino-Saudi economic relationship is pushed forward by the bilateral economic and strategic demands, driven by the combination of the complementarities of the bilateral economy, the “Looking Eastward” Policy of Saudi Arabia, the rise of China and the mutual perception. Saudi Arabia plays a crucial role in China’s energy security, the economic ties between China and GCC countries, Arab countries and Islamic countries and the construction of “One Belt and One Road” Project as well. What’s more, Saudi Arabia is a key partner in the cooperation between China and emerging powers and in promoting global economic governance. As a result, Sino-Saudi economic relationship is increasingly having strategic meaning beyond bilateral scope that has become the foundation for the Sino-Saudi strategic cooperation. In the future, works must be done to go beyond the single field of oil and consolidate the all-round cooperation of Sino-Saudi strategic economic relationship. Key Words: China; Saudi Arabia; Oil; Strategic Economic Relationship
Identifying outliers in astronomical images with unsupervised machine learning
Astronomical outliers, such as unusual, rare or unknown types of astronomical
objects or phenomena, constantly lead to the discovery of genuinely unforeseen
knowledge in astronomy. More unpredictable outliers will be uncovered in
principle with the increment of the coverage and quality of upcoming survey
data. However, it is a severe challenge to mine rare and unexpected targets
from enormous data with human inspection due to a significant workload.
Supervised learning is also unsuitable for this purpose since designing proper
training sets for unanticipated signals is unworkable. Motivated by these
challenges, we adopt unsupervised machine learning approaches to identify
outliers in the data of galaxy images to explore the paths for detecting
astronomical outliers. For comparison, we construct three methods, which are
built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE)+
KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets
are created based on the Galaxy Zoo image data published online to evaluate the
performance of the above methods. Results show that attCAE KNN achieves the
best recall (78%), which is 53% higher than the classical KNN method and 22%
higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior
to KNN (4 hours) and equal to CAE+KNN(10 minutes) for accomplishing the same
task. Thus, we believe it is feasible to detect astronomical outliers in the
data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN
to available survey datasets to assess its applicability and reliability
A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph
The Ecological Civilization Pattern Recommendation System (ECPRS) aims to
recommend suitable ecological civilization patterns for target regions,
promoting sustainable development and reducing regional disparities. However,
the current representative recommendation methods are not suitable for
recommending ecological civilization patterns in a geographical context. There
are two reasons for this. Firstly, regions have spatial heterogeneity, and the
(ECPRS)needs to consider factors like climate, topography, vegetation, etc., to
recommend civilization patterns adapted to specific ecological environments,
ensuring the feasibility and practicality of the recommendations. Secondly, the
abstract features of the ecological civilization patterns in the real world
have not been fully utilized., resulting in poor richness in their embedding
representations and consequently, lower performance of the recommendation
system. Considering these limitations, we propose the ECPR-MML method.
Initially, based on the novel method UGPIG, we construct a knowledge graph to
extract regional representations incorporating spatial heterogeneity features.
Following that, inspired by the significant progress made by Large Language
Models (LLMs) in the field of Natural Language Processing (NLP), we employ
Large LLMs to generate multimodal features for ecological civilization patterns
in the form of text and images. We extract and integrate these multimodal
features to obtain semantically rich representations of ecological
civilization. Through extensive experiments, we validate the performance of our
ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to
state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG.
Furthermore, multimodal data can indeed enhance recommendation performance.
However, the data generated by LLM is not as effective as real data to a
certain extent
HS DIC-system application for strain and displacement measurements under static-dynamic coupling loading
To study the deformation and fracture of sandstone under static-dynamic coupled load, a cylindrical specimen under pre-static axial and confining pressure was dynamically loaded using an improved split Hopkinson pressure bar (SHPB). Through the application of a special shape striker, stress equilibrium and nearly constant strain rate in specimen were achieved. During dynamic tests, the failure process of the specimen was completely monitored (7 frames at a time resolution of 25 s) by a high speed (HS) camera. Furthermore, the recorded images were matched with the loading steps through a specified trigger mode, based on which both full-field displacement values and the corresponding surface in-plane strain were obtained via digital image correlation (DIC) system. Finally, analysis on the surface deformation and failure mode of specimen shows that the sample presents an interaction of tension-shear failure and expansion failure under the axial static pressure of 72 MPa, which reflects the effect of axial static pressure on the dynamic fracture mode of the sample surface
PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
We propose a Multimodal Machine Learning method for estimating the
Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long
been the subject of many investigations. Our method includes two main models,
i.e. the feature transformation model by multimodal representation learning,
and the photometric redshift estimation model by multimodal transfer learning.
The prediction accuracy of the photometric redshift was significantly improved
owing to the large amount of information offered by the generated spectral
features learned from photometric data via the MML. A total of 415,930 quasars
from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1
and 5, were screened for our experiments. We used |{\Delta}z| =
|(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and
demonstrated a 4.04% increase in accuracy. With the help of the generated
spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach
84.45% of the total test samples, whereas it reaches 80.41% for single-modal
photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown
to decreases from 0.1332 to 0.1235. Our method has the potential to be
generalized to other astronomical data analyses such as galaxy classification
and redshift prediction. The algorithm code can be found at
https://github.com/HongShuxin/PhotoRedshift-MML .Comment: 10 pages, 8 figures, accepted for publication in MNRA
High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice
Table S4. Annotated function of differentially expressed genes identified between parents. (XLSX 1232 kb
Comparative genomic analysis of the Tribolium immune system
The annotation, and comparison with homologous genes in other species, of immunity-related genes in the Tribolium castaneum genome allowed the identification of around 300 candidate defense proteins, and revealed a framework of information on Tribolium immunity
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