Applying the network analysis’ tools and a large dataset over 2000-2017, this paper provides insights to the patterns and structure of global and regional migration and remittances as well as the connection between these two phenomena inside a complex network. We reveal that even though the structure of migration and remittance networks is slightly altering, the major changes have only occurred in the intensive margin of the networks. In addition, there is a deepening intra-regional trend regarding the structure of migration communities. By contrast, we observe an instability in the structure of migration and remittance communities formed around the U.S.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

Triggered by the rising demand, a complex aircraft production network has been evolving during the past half century. In order to analysis the spatial-temporal evolution of this network, this paper establishes a novel and complex data by collecting 1774 major global aircraft manufacturers' production relationship from Jane's All the World' s Aircraft Development & Production (1965-2021), and visualizes the spatial temporal characteristics of the global aircraft production network by using Social Net Analysis method. The characteristics are summarized as follows: (1) the main aircraft firms from 48 counties form a complex production network centered on the United States, Europe and China; (2) The production network remains diffusion during 1965-2005, but it retracts after 2005, which documents evidences of de-globalization trend in the aircraft industry; (3)AVIC, Airbus, Boeing, Textron, Lockheed, Kawasaki, Mitsubishi, Sikorsky, Bell, and Embraer are all ranked high in the study period and play the role of leading firms in the production network and drive the cooperative connections of other companies.

Noninvasive acquisition of deep tissue temperature has important applications in home health monitoring, hyperthermia safety control, and other domains. In this work, we present here a novel magnetically mediated thermoacoustic temperature measurement method. Utilizing coil to stimulate amplitude modulated magnetic field and ultrasound transducer to receive the generated thermoacoustic wave from the inserted magnetic nanoparticles. Benefiting from the high sensitivity of thermoacoustic emission from nanoparticles and the deep penetration capability of both magnetic field and ultrasound propagation, the proposed thermoacoustic temperature measurement system enables a high measurement accuracy of 0.5 degrees Celsius in real time. This work potentially facilitates further development of closed loop magnetic hyperthermia for practical clinical applications.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Alternative bio actively chemicals may be found in natural goods and traditional herb medications, but only a few plant-information formulations have been rigorously studied and verified for their potential as medicinal therapies. The study of plant-derived elements' immunomodulation capabilities and their ability as provoke the immune system as combat various elemental disorders like cancer is, nonetheless, a promising area in current therapeutics information on plant-derived chemicals. This research showed how network pharmacology may be applied as define and validate natural individual elements or more complicated preparations as prospective cancer therapies information on their various aim capabilities in this research. We give a summary of the present state of understanding on network pharmacology, with a particular emphasis on various technical methods and their implications for cancer treatment.

Innovation has received a great attention in the creative industries literature. We propose in this study a bibliometric method to examine the literature on innovation in creative industries (ICI). A file of 656 manuscripts published on ICI between 1998 and 2022 was retrieved from the Web of Science Core Collection for analysis. The results highlight the evolution of study volume, authors, affiliated institutions and countries, author networks, keyword co-occurrences, and keyword networks. The study also includes a thematic map that highlights four types of research: driving themes (e.g., digital technology, cultural innovation, performing arts, product innovation, innovation management); core and cross-cutting themes (e.g., creativity, digitization, technology, copyright); emerging themes (e.g., gender, artificial intelligence, sustainability); and specialized and peripheral themes (e.g., gender, blockchain, digital music). We finally conclude by proposing future perspectives and a research agenda in this area.

The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.

Cancer is the leading cause of death in the world, throughout the global researches of cancer treatment, people have a deeper understanding of cancer, and the treatment methods are constantly breakthrough. Conventional surgery, chemotherapy and radiotherapy have serious adverse effects and patients' quality of life is not significantly improved. Now, photodynamic therapy, photothermal therapy andthermodynamic therapy based on nanotechnology and materials technology are booming, and the development of these novel cancer therapies and their combination therapies brings more possibilities for cancer treatment. This review summarizes the research progress of novel cancer therapies based on nano and material technology from the aspects of mechanism of action and therapeutic methods, hoping to provide reference for their clinical application.

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

This work investigates the strength and the pattern of linkages among the three markets of qualitatively differentiated milk (i.e., conventional, organic without heymilk, and organic with heymilk) in Austria using the flexible TVP-VAR frequency connectedness approach and information on prices from 2018 to 2023. The main empirical findings are: (a) Taken together, the markets have maintained a moderate degree of connectedness to each other.  (b) Price linkages have been frequency-dependent; much of the adjustments to incoming information have been completed within 3 months. (c) Connectedness has been asymmetric; the highest quality (organic with heymilk) has been a net transmitter of price shocks to organic without heymilk. (d) The intensity of connectedness has been time-varying; however, the internal structure of the market network (i.e., the net transmitters and receivers of price shocks) has remained fairly stable.

Many startups look for venture capital (VC) funding to grow their business. Anecdotally, in the Southeast Asia context, startup founders who attended an elite institute of higher learning have greater access to this kind of capital, but this has not been demonstrated quantitatively. We look at a dataset of nearly 800 startups in Southeast Asia who have received VC funding in the last 5 years and look for any correlation between the founder's educational background and the amount of VC funding received. We find that founders who have obtained a postgraduate degree from an elite university receive significantly more VC funding than those who graduated from other universities. Furthermore, this effect is only seen at later stages of funding, not at the seed stage. Potential reasons for this effect could be from stronger signaling of business acumen or technical skill; or better access to funding networks.

The digital economy industry plays a transformative role in reshaping the global production networks, the local division of production, and the integration and innovation of business forms. However, to what extent the industry enables the high-quality development and upgrading of urban economies is unclear. Based on the census data of digital economy enterprises in Hangzhou in 2008, 2013 and 2018, this paper verifies the characteristics of the digital economy industry and its enabling mechanism for high-quality urban economic development. It is found that: firstly, the digital economy industry has three main forms for the economy, namely, the sharing, scale and scope economy. Secondly, the industry has four major mechanisms to improve the quality of urban economies. That is, it can restructure the global industrial division of labor and value chains, promote the transformation of scale economy from the supply side to the demand side, enable the softening and servitization of industrial structure, and generate the scale of economies. This paper thus contributes to understanding the evolution of enterprise organizational structure and industrial competitiveness and the endogenous technological change under the digital economy.

Based on the income poverty level differences and multi-dimensional poverty perspective in China, this paper compares the performance of education poverty alleviation, employment poverty alleviation, and industry poverty alleviation. The hypothesis was tested by quantile regression and ordered probit model. The results show that: in the short term, the effect of poverty alleviation through education is not significant; however, the effect of poverty alleviation through employment and industry is significant, increasing the income of poor households to a certain extent. The effect of poverty alleviation through employment is high. In the long term, the effect of poverty alleviation through industry is good. For the deep poor households, the effect of poverty alleviation through employment is more significant in the short term. For the marginal poor, only industry poverty alleviation has a significant effect on poverty alleviation. Education poverty alleviation and industry poverty alleviation are conducive to improving multi-dimensional poverty. In the alleviation of multi-dimensional poverty, employment poverty alleviation and education poverty alleviation have a certain synergy.

Under the background of digital economy, global tax governance has become one of the important topics in today's world development and change. In recent years, new challenges, such as tax base erosion and profit transfer, and the imbalance of cross-border tax source division, have become prominent, forcing the reform of international tax rules and the reshaping of international tax order. Based on this, the article first deeply digs into the scale and cross-border characteristics of the spatial-temporal aggregation of the digital economy, and comprehensively evaluates its impact on the global tax governance pattern. After that, the article combs out the realistic picture and challenges of the current digital tax reform from the aspects of theoretical basis, basic principles and governance mechanism. Finally, based on China's basic national conditions, the article puts forward some institutional ideas and policy suggestions, with a view to improving China's participation, voice and influence in global tax governance, and also enabling governments, enterprises and residents of various countries to enjoy the fruits of world economic development fairly and promote the construction of Community of Shared Future for Mankind.

The COVID-19 pandemic wreaked havoc on economies everywhere, affecting everything from supply chain networks to international trade as a direct result of the increased vulnerability of more nations and the disruption of the activities of key traders. This research study examines how COVID-19 affected the supply chain on a worldwide and regional scale, as well as the systemic policy and economic steps governments and businesses took to rebound and maintain resilience. COVID-19 has had a significant influence, prompting governments and businesses to take steps to fortify their operations and businesses against disruptions, highlight the necessity for resilience in supply chains and emphasize the importance of managing risk. The supply value chain is at risk from the prolonged implementation of COVID-19 trade measures like border closure, export restrictions, and import sanctions. Many businesses are transitioning from a "recovery mode" to a resilient and sustainable mode" and are beginning to plan for the future. The study suggests that corporate and government leaders should prioritize proactive and adaptable policy, economic, and structural adjustment. This research will be useful to policymakers and industry leaders in their efforts to lessen the effect of COVID-19 on the global supply chain by enforcing governmental, economic, and business reforms built on resilient supply chains and mitigating economic risks during tough times.

In China, the land arrangement behavior of over 160 million rural-urban migrant workers is closely related to the optimal allocation of rural land resources and sustainable development of urban and rural areas. Although previous studies show that social capital affects migrant workers’ land arrangement behavior, few empirical studies reveal the relationship between them, and the corresponding interventions remain unclear. Using survey data collected in Henan Province, China, and a multinomial logit model, this study empirically analyzes the mechanism behind the impact of social capital on migrant workers’ land arrangement behavior from the perspective of social capital. Results illustrate that social capital has a significant impact on the land arrangement behavior of migrant workers. The behavior is significantly correlated with the scale of migrant workers’ urban social networks, the degree of urban social trust, and urban belonging. More social capital in urban areas indicates a higher tendency for migrant workers to abandon their land contracting rights and become permanent urban residents. This study reveals the mechanism of social capital affecting migrant workers’ land arrangement behavior and provides a reference for decision-making with respect to guiding migrant workers’ land management behavior for other countries facing similar social problems.

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.