Do skilled immigrants affect the wage rate of Australian workers?

We hypothesise that skilled immigration increases the wage rate in Australia. Our alternative hypothesis is that skilled immigration decreases the wage rate in Australia. The data used for this research comes from the Australian Bureau of Statistics and Department of Immigration andCitizenship. Based on our analysis, we find that there is positive relationship between high-skilled immigration and employee earnings per hour.We also find that low-skilled immigrants have a negative effect on employee earnings per hour. We believe that low-skilled workers are easily replaced such that low-skilled immigrants are substitutes to Australian low-skilled workers. However, there are some limitations of our research. Notably, our result are restricted to three occupations

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd

GNeSF: Generalizable Neural Semantic Fields

3D scene segmentation based on neural implicit representation has emerged recently with the advantage of training only on 2D supervision. However, existing approaches still requires expensive per-scene optimization that prohibits generalization to novel scenes during inference. To circumvent this problem, we introduce a generalizable 3D segmentation framework based on implicit representation. Specifically, our framework takes in multi-view image features and semantic maps as the inputs instead of only spatial information to avoid overfitting to scene-specific geometric and semantic information. We propose a novel soft voting mechanism to aggregate the 2D semantic information from different views for each 3D point. In addition to the image features, view difference information is also encoded in our framework to predict the voting scores. Intuitively, this allows the semantic information from nearby views to contribute more compared to distant ones. Furthermore, a visibility module is also designed to detect and filter out detrimental information from occluded views. Due to the generalizability of our proposed method, we can synthesize semantic maps or conduct 3D semantic segmentation for novel scenes with solely 2D semantic supervision. Experimental results show that our approach achieves comparable performance with scene-specific approaches. More importantly, our approach can even outperform existing strong supervision-based approaches with only 2D annotations. Our source code is available at: https://github.com/HLinChen/GNeSF.Comment: NeurIPS 202

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. We use the convex $\ell_1$ and the nonconvex $\ell_1-\ell_2$ functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods

Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules

The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends

Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans tend to persuade others to change their viewpoints, attitudes or behaviors through conversations in various scenarios (e.g., persuasion for social good, arguing in online platforms). Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue system. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.Comment: 36 pages, 6 figure

Double B-type magnetic integrated transformer based input-parallel output-parallel LLC resonant converter modules

The LLC resonant converter used in high-power situations suffers from the problems of high conduction loss and current stress, which can be solved using input-parallel output-parallel (IPOP)-connected converter modules. However, this leads to a multiple increase in the number of magnetic components, which reduces power density. Magnetic integration technology is an effective way to reduce the volume of converters. Currently, the magnetic integrated transformer based on EE-type cores is widely used to realize miniaturization, and it uses leakage inductance instead of resonant inductance to improve power density. However, leakage inductance is difficult to control, and the external radiated magnetic field will produce serious eddy current loss and electromagnetic interference. This article proposes a novel double B-type magnetic integrated transformer, which can integrate the magnetic components of two LLC resonant converters simultaneously and where the resonant inductances are wound independently. The structure contains four low reluctance branches, which are used as the cores of the transformer and the resonant inductance. The decoupling integration method, which integrates the four components into a single core, has been used to increase core utilization and improve power density. On this basis, the transformer’s high- and low-voltage windings are cross-arranged to reduce the magnetic field intensity in space, further decreasing the loss and electromagnetic interference. Compared with the EE-type magnetic integrated transformer, the volume of the proposed structure is reduced by 5.9%. A 400W experimental prototype is built, and the results verify the validity of the design

Differences and Commonalities in Responses of Zygosaccharomyces rouxii to High Salt and High Temperature Stress

Complete synthetic minimal media for Zygosaccharomyces rouxii growth were designed for high temperature (40 ℃, HTS) and high salt stress (18% NaCl, HSS) in this study, and the difference in the nutritional requirements of Z. rouxii cells under long-term adverse environmental conditions was analyzed. The differences in the metabolism and gene expression of organic acids, amino acids and sugars during the period from the growth adaptation stage to the early logarithmic stage were highlighted between HSS and HTS conditions. The results showed that Z. rouxii cells exposed to HSS needed more exogenous amino acids, vitamin and amino acid supplementation alleviated HTS-induced damage in yeast cells. The adversity transcription gene MSN4 and the hypertonic regulatory protein gene HOG1 responded to high salt, while the heat shock regulatory protein gene HSF1 and the superoxide dismutase gene SOD1 responded to high temperature. In summary, different strategies for organic acid, amino acid and sugar metabolism were adopted by Z. rouxii in response to HSS and HTS. This study deepens the understanding of the mechanism of temperature tolerance in salt-tolerant Z. rouxii, which will contribute to the development of new brewing yeast cells with tolerance to both high salt and temperature

Exploring the role of the CapG gene in hypoxia adaptation in Tibetan pigs

Introduction: The CapG gene, which is an actin-binding protein, is prevalent in eukaryotic cells and is abundantly present in various pathways associated with plateau hypoxia adaptation. Tibetan pigs, which have inhabited high altitudes for extended periods, provide an excellent research population for investigating plateau hypoxia adaptation.Results: This study focused on Tibetan pigs and Yorkshire pigs residing in Nyingchi, Tibet. The blood physiological data of Tibetan pigs were found to be significantly higher than those of Yorkshire pigs, including RBC, HGB, HCT, MCH, and MCHC. The SNP analysis of the CapG gene identified six sites with mutations only present in Tibetan pigs. Notably, the transcription factors at sites C-489T, C-274T, and A-212G were found to be altered, and these sites are known to be associated with hypoxia adaptation and blood oxygen transportation. The mRNA expression of the CapG gene exhibited highly significant differences in several tissues, with the target proteins predominantly higher in the Yorkshire pig compared to the Tibetan pig. Specifically, a notable difference was observed in the lung tissues. Immunohistochemistry analysis revealed high expression levels of CapG proteins in the lung tissues of both Tibetan and Yorkshire pigs, primarily localized in the cytoplasm and cell membrane.Conclusion: The CapG gene plays a significant role in regulating hypoxia adaptation in Tibetan pigs. This study provides a theoretical basis for the conservation and utilization of Tibetan pig resources, the breeding of highland breeds, epidemic prevention and control, and holds great importance for the development of the highland livestock economy