Search Results (1,024)

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC’s) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province’s five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. Full article

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design. Full article

Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI’s ability to accelerate drug discovery for Alzheimer’s, predict neurotoxicity, personalize treatments for Parkinson’s, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research. Full article

Climate change is one of the biggest challenges the planet confronts today and is predicted to continue to face in the future. It is one of the greatest threats to humanity, seriously affecting human health, the natural environment, and security. This research aims to investigate the factors related to the perception of Greek citizens regarding climate change and green development and the obstacles that exist in Greece in the transition to enhanced green growth. For this reason, four cases related to communication and education, energy, actions to protect the environment, and each one’s responsibility were investigated. Data were collected through a survey (structured questionnaire) of 745 respondents in the regional unit of the Greek city of Larissa with an overall response rate of 69% and analyzed through a combination of factor and path analysis. The results show that communication, actions related to environmental protection, and energy-saving actions are the ones that have significant associations with the goal of green development. Full article

The Sustainable Development Goals (SDGs) have set the agenda for 2030, calling for collective global efforts to deal with climate change while seeking a balance between economic development and environmental protection. Although many countries are exploring emission reduction paths, mainly from government and corporate perspectives, addressing climate change is also an individual responsibility and requires public participation in collective action. The millennial generation constitutes the current workforce and will be the leaders in climate action for the next 30 years. Therefore, our study focuses on the Chinese millennial generation, conducting in-depth semi-structured interviews with 50 participants in qualitative research to explore their low-carbon lifestyles, the barriers, and enablers in switching to a wider range of low-carbon lifestyles. There are three main results: (1) Based on our study samples, there is an indication that Chinese millennials have a positive attitude towards transitioning to a low-carbon lifestyle. Women demonstrate a stronger willingness to adopt low-carbon behaviors in their daily household activities compared to men. However, their involvement in governance in the context of transitioning to a low-carbon society is limited, with most women assuming execution roles in climate action rather than decision-making positions. (2) Millennial’s low-carbon life transition is accompanied by technological innovation and progress. However, this progress brings some new forms of resource waste, and reasonable policy-making is essential. (3) Personal economic interests and the satisfaction of their consumption needs will drive millennials to reduce carbon emissions in their daily lives, but it requires the guidance of reasonable policy-making and synergies among various stakeholders. This research will help policymakers better understand the current status and potential issues related to people’s low-carbon actions, enabling the formulation of more rational guiding policies. It can also help other stakeholders learn about millennials’ demands and take more effective collective action toward carbon reduction. Full article

High-dimensional entanglement of optical angular momentum has shown its enormous potential for increasing robustness and data capacity in quantum communication and information multiplexing, thus offering promising perspectives for quantum information science. To make better use of optical angular momentum entangled states, it is necessary to develop a reliable platform for measuring and analyzing them. Here, we propose a hybrid metadetector of monolayer transition metal dichalcogenide (TMD) integrated with spin Hall nanoantenna arrays for identifying Bell states of optical angular momentum. The corresponding states are converted into path-entangled states of propagative polaritonic modes for detection. Several Bell states in different forms are shown to be identified effectively. TMDs have emerged as an attractive platform for the next generation of on-chip optoelectronic devices. Our work may open up a new horizon for devising integrated quantum circuits based on these two-dimensional van der Waals materials. Full article

Timing Engineering Change Order (ECO) is time-consuming in IC design, requiring multiple rounds of timing analysis. Compared to traditional methods for accelerating timing analysis, which focus on a specific design, timing ECO requires higher accuracy and generalization because the design changes considerably after ECO. Additionally, there are challenges with slow acquisition of data for large designs and insufficient data for small designs. To solve these problems, we propose TSTL-GNN, a novel approach using two-stage transfer learning based on graph structures. Significantly, considering that delay calculation relies on transition time, we divide our model into two stages: the first stage predicts transition time, and the second stage predicts delay. Moreover, we employ transfer learning to transfer the model’s parameters and features from the first stage to the second due to the similar calculation formula for delay and transition time. Experiments show that our method has good accuracy on open-source and industrial applications with an average $R^{2}score/MAE$ of 0.9952/13.36, and performs well with data-deficient designs. Compared to previous work, our model reduce prediction errors by 37.1 ps on the modified paths, which are changed by 24.27% on average after ECO. The stable $R^2$ score also confirms the generalization of our model. In terms of time cost, our model achieved results for path delays consuming up to 80 times less time compared to open-source tool. Full article

In China, the integration of livestreaming into restaurant marketing has transitioned from mere entertainment to a vital business tool. This study examines the influence of social sharing value (SSV) on customer attitudes and behavioral intentions within the context of restaurant livestreams, applying the stimulus-organism-behavior-consequences (SOBC) model. Analyzing data from 1139 livestream viewers using partial least squares-path modeling (PLS-PM), the results reveal that SSV significantly enhances viewers’ trust, satisfaction, word of mouth (WOM), and behavioral intentions. Trust was shown to elevate satisfaction, which, in turn, positively impacts WOM and purchase intentions. Among the SSV’s components, brand intimacy emerged as highly influential. Notably, behavioral intention was found to significantly influence WOM activities, underscoring the critical role of proactive customer behaviors in promoting the brand. This study extends social exchange theory by quantifying relationship quality and adapting it to digital consumer interactions in the restaurant industry. The findings highlight the importance of cultivating SSV to bolster customer trust and satisfaction, thereby enhancing loyalty and advocacy. Effective engagement through livestreaming can amplify brand intimacy, establishing it as an indispensable strategy for maintaining competitiveness in the restaurant sector. Full article

The station–city integration development strategy, guided by the Transit-Oriented Development (TOD) model, has enhanced the coupling relationship between rail transit stations and urban areas. Walking, as a core mode of low-carbon urban transport, plays a significant role in the connectivity between stations and urban areas and in the rate of passenger flow dispersal. During peak periods, large volumes of passengers flood the streets, forming complex and diverse walking paths that penetrate urban neighborhoods. Route choice is a complex decision-making process influenced by both subjective and objective factors. Previous studies on pathfinding activities have often focused on either subjective or objective factors in isolation, with limited research on their interaction. This paper establishes a comparative analysis framework based on the translation of subjective and objective information and behavioral feedback mechanisms. Using Lvcuo Station, a transit station on Xiamen’s Metro Line 1 in Fujian Province, China, as a case study, we collected walking data from 410 passengers through field surveys. We used the Kappa consistency coefficient test method to analyze the performance of the interaction between the objective environment and subjective environmental cognitive factors when pedestrians exit the rail transit station and walk to their destinations. We also used multinomial logistic regression (MLR) to analyze the correlation between subjective perception variables and objective built environment variables and to consider the influence of individual pedestrian characteristics and attributes on path selection. The results revealed the following: (1) Overall, there is a significant deviation effect of subjective and objective factors on passengers’ pathfinding behavior, with some spatial correlation with the built environment of the streets. (2) The consistency of influences on walking activities varies significantly across different travel periods, distances, and purposes. (3) Visual elements, urban life with a bustling atmosphere, street permeability, and route connectivity positively correlate with subjective perception while “supporting walking facilities” and “meeting consumption needs along the way” negatively correlate with subjective perception. These findings underscore the need to enhance the understanding of the built environment in the street spaces within rail transit station areas from the perspectives of pedestrians to improve the walkability of these streets. Full article

This paper presents an analytical exploration of sixth-generation (6G) satellite–terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem. Full article

Since the socio-economic reform in 1978, rural China has undergone drastic spatial restructuring, and the trend of multifunctional development and dynamic evolution of settlements in the countryside have become increasingly obvious. Functions and settlements are the important parts of rural areas. Rural multifunction is a new perspective to explore the diversified development paths of the countryside, and rural settlements provide basic support for rural multifunction. Clarifying the complex coupling coordination relationship between rural functional diversification (RFD) and rural settlement evolution (RSE), and identifying the spatial heterogeneity of their interactions is the key to promoting the rural revitalization strategy. This study analyzes the spatiotemporal changes in rural functions and rural settlements at the township level, alongside assessing various forms and the extent of coupled development. Therein, we consider the advantages of different coupling types of townships and propose four development paths for rural settlements to improve the adaptability of rural functions and settlements. The results show that: (1) The functions and settlements in the study area are characterized by significant spatial and temporal dynamics, indicating that the traditional mountainous countryside is in a process of rapid development and change. (2) The coupling coordination degree of RFD and RSE increases yearly, generally transitioning from the moderate imbalance to the basic coordination stage, and the coordinated townships have obvious spatial agglomeration. (3) Based on the elasticity coefficient model, this paper summarizes four coupling models of RFD and RSE to guide the optimization of rural settlement development paths. This research provides scientific guidance for developing countries in the spatial planning of rural territories and the optimization of rural settlements. Full article

In the quest for a sustainable future, energy-intensive industries (EIIs) stand at the forefront of Europe’s decarbonisation mission. Despite their significant emissions footprint, the path to comprehensive decarbonisation remains elusive at EU and national levels. This study scrutinises key sectors such as non-ferrous metals, steel, cement, lime, chemicals, fertilisers, ceramics, and glass. It maps out their current environmental impact and potential for mitigation through innovative strategies. The analysis spans across Spain, Greece, Germany, and the Netherlands, highlighting sector-specific ecosystems and the technological breakthroughs shaping them. It addresses the urgency for the industry-wide adoption of electrification, the utilisation of green hydrogen, biomass, bio-based or synthetic fuels, and the deployment of carbon capture utilisation and storage to ensure a smooth transition. Investment decisions in EIIs will depend on predictable economic and regulatory landscapes. This analysis discusses the risks associated with continued investment in high-emission technologies, which may lead to premature decommissioning and significant economic repercussions. It presents a dichotomy: invest in climate-neutral technologies now or face the closure and offshoring of operations later, with consequences for employment. This open discussion concludes that while the technology for near-complete climate neutrality in EIIs exists and is rapidly advancing, the higher costs compared to conventional methods pose a significant barrier. Without the ability to pass these costs to consumers, the adoption of such technologies is stifled. Therefore, it calls for decisive political commitment to support the industry’s transition, ensuring a greener, more resilient future for Europe’s industrial backbone. Full article

This paper surveys the energy literature and systematically reviews the path to an inclusive and sustainable energy transition by exploring factors that drive the current energy transitions, countries with advanced energy transition programs, and the roles of energy literacy and justice in energy transition. Utilizing an exhaustive literature search from 2001 to 2023 via the Scopus database, the study identifies strong policy frameworks, technological advancements, economic incentives, and international collaborations as pivotal factors in successful energy transitions. Case studies from the Nordic countries, Germany, and Poland highlight diverse approaches and significant progress, revealing valuable lessons for global application. Although energy literacy emerges as crucial for public acceptance and participation, fostering informed decision-making and supportive behaviors toward renewable energy initiatives, energy justice ensures equitable access to the benefits of energy transitions, addressing socio-economic impacts on marginalized communities. The study identifies a scarcity of research that articulates and integrates energy literacy and justice concurrently within the purview of energy transition. The paper recommends the adoption, integration, and institutionalization of frameworks that concurrently propagate energy literacy and guide fair and equitable energy transitions. The frameworks should encourage active community involvement, promote community ownership of renewable energy projects, ensure transparency and inclusivity, implement measures for equitable economic benefits, protect livelihoods, address historical distrust, and leverage social media to promote energy literacy and justice. Finally, the continuous monitoring and evaluation of energy transition initiatives are crucial to ensure that they meet evolving societal needs and environmental goals. Full article

Effective spin foams provide the most computationally efficient spin foam models yet and are therefore ideally suited for applications, e.g., to quantum cosmology. Here, we provide the first effective spin foam computations of a finite time evolution step in a Lorentzian quantum de Sitter universe. We will consider a setup that computes the no-boundary wave function and a setup describing the transition between two finite scale factors. A key property of spin foams is that they implement discrete spectra for the areas. We therefore study the effects that are induced by the discrete spectra. To perform these computations, we had to identify a technique to deal with highly oscillating and slowly converging or even diverging sums. Here, we illustrate that high-order Shanks transformation works very well and is a promising tool for the evaluation of Lorentzian (gravitational) path integrals and spin foam sums. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs) represent a promising green solution for energy production, traditionally relying on platinum-group-metal (PGM) electrocatalysts. However, the increasing cost and limited global availability of PGMs have motivated extensive research into alternative catalyst materials. PGM-free oxygen reduction reaction (ORR) catalysts typically consist of first-row transition metal ions (Fe, Co) embedded in a nitrogen-doped carbon framework. Key factors affecting their efficacy include intrinsic activity and catalyst degradation. Thus, alternative materials with improved characteristics and the elucidation of reaction and degradation mechanisms have been the main concerns and most frequently explored research paths. High intrinsic activity and active site density can ensure efficient reaction rates, while durability towards corrosion, carbon oxidation, demetallation, and deactivation affects cell longevity. However, when moving to the actual application in PEMFCs, electrode engineering, which involves designing the catalyst layer, and other critical operational factors affecting fuel cell performance play a critical role. Electrode fabrication parameters such as ink formulation and deposition techniques are thoroughly discussed herein, explicating their impact on the electrode microstructure and formed electrochemical interface and subsequent performance. Adjusting catalyst loading, ionomer content, and porosity are part of the optimization. More specifically, porosity and hydrophobicity determine reactant transport and water removal. High catalyst loadings can enhance performance but result in thicker layers that hinder mass transport and water management. Moreover, the interaction between ionomer and catalyst affects proton conductivity and catalyst utilization. Strategies to improve the three-phase boundary through the proper ionomer amount and distribution influence catalyst utilization and water management. It is critical to find the right balance, which is influenced by the catalyst–ionomer ratio and affinity, the catalyst properties, and the layer fabrication. Overall, understanding how composition and fabrication parameters impact electrode properties and behaviour such as proton conductivity, mass transport, water management, and electrode–electrolyte interfaces is essential to maximize electrochemical performance. This review highlights the necessity for integrated approaches to unlock the full potential of PGM-free materials in PEMFC technology. Clear prospects for integrating PGM-free catalysts will drive cleaner and more cost-effective, sustainable, and commercially viable energy solutions. Full article