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