Measurement Models For Sailboats Price vs. Features And Regional Areas

In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.Comment: 20 pages, 17 figure

Evaluation and evolution analysis of water ecosystem service value in the yangtze river delta region based on meta-analysis

Rapid economic development, industrialization and urbanization lead to environmental pollution and damage the stability of regional ecosystems. The Yangtze River Delta region is an economically developed region in China, faces the problems of water environment pollution and water ecosystem service degradation. Reasonable assessment of water ecosystem service value (ESV) is of great significance for grasping the status of regional water ecosystem services, improving water ecological environment, and realizing regional sustainable development. This study collects 119 research literature about China, including 156 observations to establish a value transfer database; specially builds a Meta-analysis model including the variables of climate conditions, environmental pollution and environmental protection, then assesses the waters ESV in the Yangtze River Delta using the model and analyzes the changes from 2009 to 2018. The study finds that the location, population density, the area of the site, average annual precipitation, literature characteristics, landscape characteristics, wastewater discharge, environmental protection expenditure, and wastewater treatment costs can affect the water ESV significantly. Based on the meta-analysis benefit transfer model to evaluate the water ESV in Yangtze River Delta region is RMB 177,126 yuan/ hha/year and the growth rate is 27.18%. The place with the highest value per unit area is Shanghai, and the total value in Jiangsu Province is the highest. Economic development, waste water discharge and wastewater treatment costs are the main reasons for the changes and differences in the value of water ecosystem services in the Yangtze River Delta region. The contribution of this study to the field of water ESV assessment is that the meta-analysis model includes a broader set of influencing variables, including landscape, population density, climate change and environmental protection. It provides a practical reference for water ESV assessment on the local scale and a scientific basis for water area management related to the development of water area and ecological compensation, as well as promote the sustainable development of water ecosystems

Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers

Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states for errors. The syndromes are subsequently interpreted by a decoding algorithm to identify and correct errors in the data qubits. This task is complex due to the multiplicity of error sources affecting both data and syndrome qubits as well as syndrome extraction operations. Additionally, identical syndromes can emanate from different error sources, necessitating a decoding algorithm that evaluates syndromes collectively. Although machine learning (ML) decoders such as multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) have been proposed, they often focus on local syndrome regions and require retraining when adjusting for different code distances. We introduce a transformer-based QEC decoder which employs self-attention to achieve a global receptive field across all input syndromes. It incorporates a mixed loss training approach, combining both local physical error and global parity label losses. Moreover, the transformer architecture's inherent adaptability to variable-length inputs allows for efficient transfer learning, enabling the decoder to adapt to varying code distances without retraining. Evaluation on six code distances and ten different error configurations demonstrates that our model consistently outperforms non-ML decoders, such as Union Find (UF) and Minimum Weight Perfect Matching (MWPM), and other ML decoders, thereby achieving best logical error rates. Moreover, the transfer learning can save over 10x of training cost.Comment: Accepted to ICCAD 2023, FAST ML for Science Workshop; 7 pages, 8 figure

Raman spectroscopy characterization of structural evolution in middle-rank coals

The second coalification jump which occurred during the middle-rank led to abrupt changes of many physical and chemical properties of coal, and the change of the aggregate structure may be the fundamental reason. In order to investigate the structural evolution characteristics of middle-rank coal and its relation with the second coalification jump in detail, the structure characteristics of six middle-rank coals (Ro,max=1.10%−1.63%) that across the second coalification jump were studied by Raman spectroscopy, and the structural parameters were calculated by fitting the first-order and second-order Raman spectrum using the fitting software. The results indicated that the evolution of Raman structural parameters with Ro,max is not linear, reflecting the complexity of the structural evolution of coal. According to the evolution characteristics of Raman structural parameters, the coalification during the stage of Ro,max=1.10%−1.63% can be divided into three stages. The turning points are located near Ro,max=1.30% and Ro,max=1.50%, respectively, which are exactly equivalent to the positions of the second and the third coalification jump discovered in previous research. It indicated that the Raman structural parameters can reflect the occurrence of the coalification jump, moreover, Raman spectroscopy is an effective method to study the coal structure. The first stage is Ro,max=1.10%−1.30%, the long-chain aliphatic structures cracked and the remained shorter-chain aliphatic hydrocarbons and aliphatic substituted structures on the aromatic rings will form new alicyclic structures, which caused the branched degree increases and hindered the alignment of aromatic systems in coal. The order degree of aromatic system is thus reached the least near Ro,max=1.30%, with the smallest WG, the largest FG/D, the smallest AD/AG, the increase of AS/A1, and the significant decrease of A(2G)R/A2. In the second stage of Ro,max=1.30%−1.50%, the aromatization of the alicyclic structures formed in the previous stage resulted in an increase in the content of aromatic C—H structure and the least of amorphous carbon structure. Besides, the degree of aromatization and aromatic structural both increased, which showed that A(GR+VL+VR)/AD, A(GR+VL+VR)/AG and FG/D decreased significantly, AD/AG increased, WG and d(G-D) increased quickly. The last stage is Ro,max=1.50%−1.63%, the condensation reaction occurred between the aromatic rings formed in the second stage, leading to the reduction of A(2G)R/A2. Meanwhile, the various bridging bonds between aromatic ring systems continued to break, resulting in the formation of some small-scale aromatic structures, as evidenced by a decrease in A(2G)R/A2, a small decrease in WG, and an increase in A(GR+VL+VR)/AD and A(GR+VL+VR)/AG. These results are the basis for deeply understanding the mechanism of coalification jump and coalification

Role of the tumor microenvironment in malignant melanoma organoids during the development and metastasis of tumors

Malignant melanoma (MM) is the most metastatic and aggressive form of skin cancer, and carries a high risk of death. Immune-checkpoint inhibitor therapy and molecular-targeted therapy can prolong the survival of patients with advanced MM significantly. However, the low response rate and inevitable drug resistance prevent further improvements in efficacy, which is closely related to the tumor microenvironment (TME). The TME refers to the tumor stroma, including fibroblasts, keratinocytes, immune cells, soluble molecules, and extracellular matrix (ECM). The dynamic interaction between the TME and tumor cells is very important for the growth, local invasion, and metastatic spread of tumor cells. A patient-derived organoid (PDO) model involves isolation of tumor tissue from patients with MM and culturing it in vitro in a three-dimensional pattern. Compared with traditional cultivation methods, the PDO model preserves the heterogeneity of the tissue structure of MM and demonstrates the interaction between MM cells and the TME. It can reproduce the characteristics of proliferation, migration, and invasion of MM cells, and better simulate the structural function of MM in vivo. This review explores the role of each TME component in development of the PDO model. This review will provide a reference for research on the drug screening and targeted treatment using PDOs, particularly for the immunotherapy of MM