Search Results (70)

The emerging paradigm of “fusion of lifeforms” represents a transformative shift from conventional human–machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain–computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human–machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems. Full article

Financial markets are increasingly shaped by opaque price controls influenced by the rising prominence of algorithmic and AI-driven systems in price determination. While much of the current research on algorithmic trading and market microstructure has emphasised aspects such as efficiency, liquidity, and model clarity, there has been less focus on the broader implications of assigning inference, execution, and learning tasks to adaptive algorithms. This entry presents a conceptual framework that aims to elucidate how algorithmic systems fundamentally alter price discovery. It highlights the centralisation of epistemic authority, the diminishing of human interpretative capabilities, and the emergence of “rational opacity”. This condition allows prices to remain informationally efficient while obscuring the causal relationships between information and price formation, making them difficult to comprehend for human participants both prior to and in real-time. We introduce the Algorithmic Price Discovery Loop, a theoretical model that connects algorithmic inference, automated execution, feedback-driven learning, and the resulting asymmetry in market-wide interpretation. The framework not only provides critical theoretical insights but also proposes testable propositions and outlines various empirical avenues for investigating algorithmic authority and opacity across different market contexts. Furthermore, the discussion addresses governance implications, recognises the limitations of existing regulatory frameworks, and highlights potential crises that could arise in AI-driven financial markets. Full article

Quantum measurements play a fundamental role in quantum information. Therefore, increasing efforts are being made to construct symmetric measurement operators for qudit systems. A wide class of projective measurements corresponds to complex projective 2-designs, which include symmetric, informationally complete (SIC) POVMs and mutually unbiased bases (MUBs). In this paper, we establish a one-to-one correspondence between conical 2-designs and mutually unbiased generalized equiangular tight frames, both of which are common generalizations of SIC POVMs and MUBs to operators of arbitrary rank. It turns out that there exist rich families of operators that belong to only one of those two classes. This raises important questions about which symmetries have to be preserved for applicational prominence. Full article

With the advancement of cost informationization in construction, the automatic classification of building project costs has become a key step to improving management efficiency. Traditional rule-based or manual methods are insufficient to handle increasingly complex engineering texts. To address this issue, this study proposes a deep learning framework that integrates Convolutional Neural Networks (CNNs), Deep Pyramid Convolutional Neural Networks (DPCNNs), and Long Short-Term Memory networks (LSTMs). A standardized dataset of 12,838 records was constructed based on expert annotation. Six baseline models were trained under both character-level and word-level tokenization, and their predictions were combined through a majority voting strategy. Experimental results show that the ensemble model achieved an accuracy of 97.59% on the test set, outperforming single models, with character-level tokenization performing better. The findings confirm the effectiveness of model ensembling in enhancing classification accuracy and robustness, providing a feasible solution for intelligent text classification in cost management, and offering practical reference for digitalization and intelligent applications. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

Quantum network correlations are crucial for long-distance quantum communication, quantum cryptography, and distributed quantum computing. Detecting network steering is particularly challenging in complex network structures. We have studied the steering inequality criteria for a 2-forked 3-layer tree-shaped network. Assuming the first and third layers are trusted and the second layer is untrusted, we derived a steering inequality criterion using the correlation matrix between trusted and untrusted observables. In particular, we apply the steering criterion to three classes of measurements which are of special significance: local orthogonal observables, mutually unbiased measurements, and general symmetric informationally complete measurements. We further illustrate the effectiveness of our method through an example. Full article

We find that the whole set of known baryons of spin parity $J^P={{\displaystyle \frac{1}{2}}}^{+}$ (the ground state) and $J^P={{\displaystyle \frac{3}{2}}}^{+}$ (the first excited state) is organized in multiplets which may efficiently be encoded by the multiplets of conjugacy classes in the small finite group $G=(192, 187)$. A subset of the theory is the small group $(48, 29)\cong GL(2, 3)$ whose conjugacy classes are in correspondence with the baryon families of Gell-Mann’s octet and decuplet. G has many of its irreducible characters that are minimal and informationally complete quantum measurements that we assign to the baryon families. Since G is isomorphic to the group of braiding matrices of $SU{\left(2\right)}_2$ Ising anyons, we explore the view that baryonic matter has a topological origin. We are interested in the holographic gravity dual $Ad{S}_3/QF{T}_2$ of the Ising model. This dual corresponds to a strongly coupled pure Einstein gravity with central charge $c=1/2$ and AdS radius of the order of the Planck scale. Some physical issues related to our approach are discussed. Full article

Exploring the dynamic synergistic effect and driving mechanism of green technology innovation (GTI) and tourism green development (TGD) is of great significance for reducing the environmental footprint of tourism, enhancing the capacity of sustainable development, and promoting the practical application of green technology and industrial upgrading. This study takes 41 cities in the Yangtze River Delta (YRD) region of China as research objects, and the time span of the study is from 2007 to 2023. The dynamic synergistic effects and driving mechanisms of GTI and tourism green development (TGD) are revealed by comprehensively adopting the coupled coordination model and panel quantile regression model. The results of this study show that (1) the average value of the coupling coordination degree (CCD) shows a small upward trend, with regional differences for Shanghai > Zhejiang > Jiangsu > Anhui. (2) High coupling coordination degree (CCD) areas are spatially concentrated in provincial capital cities, with prominent spatiotemporal heterogeneity characteristics and spatial correlation. (3) Industrial structure, human capital, and tourism resource endowment have a significant contribution to the coupling coordination degree, while informationization level and transport facilities show heterogeneous influence effects in different sub-location contexts. The level of openness and environmental regulation did not show the ‘pollution shelter’ effect, confirming ‘Porter’s hypothesis’ to some extent. (4) This study not only provides new evidence for the synergistic effect of green technological innovation and the green development of tourism, but it also provides an important reference for green technological innovation to empower the high-quality development of tourism. Full article

With the rapid development of the education industry and the expansion of university enrollment scale, it is difficult for the original teaching resource operation and maintenance management mode and utilization efficiency to meet the demands of teachers and students for high-quality teaching resources. OpenStack and Ceph technologies provide a new solution for optimizing the utilization and management of educational resources. The educational resource private cloud platform built by them can achieve the unified management and self-service use of the computing resources, storage resources, and network resources required for student learning and teacher instruction. It meets the flexible and efficient use requirements of high-quality teaching resources for student learning and teacher instruction, reduces the construction cost of informationization investment in universities, and improves the efficiency of teaching resource utilization. Full article

In view of the lack of soil bins for studying the surface interaction between aircraft wheels and soil, this study designed an indoor test bench for aircraft wheels and soil, including a soil container, loading vehicle, and intelligent measurement and control system, to test key parameters such as tire speed and wheel frictional resistance. The test system is capable of achieving speed regulation ranging from 0 to 30 km/h. The vertical load adjustment range with an adjustment interval of 10 kg spans from 90 to 140 kg. The soil type, compaction degree, and other conditions can be modified as per requirements to vary multiple test conditions, thereby enabling us to explore their influence on the driving resistance of the wheels. Moreover, the test data can be collected and processed in real time. A performance test of a wheel–soil table was carried out. The results show that the wheel–soil table test system is stable and reliable and can determine the relationship between the tire and soil, and the structural design of the test system meets the use requirements. In addition, it achieves the target test speed, data acquisition frequency, and stability. In terms of functionality and operational difficulty, the data acquisition of the entire test process is automated, and the test system achieves better informationization than previous methods. The overall operation of the wheel–soil platform is stable and powerful; thus, the model test platform design goal is achieved, and the testing requirements are met. Full article

By leveraging the Variational Quantum Eigensolver (VQE), the “quantum equation of motion” (qEOM) method established itself as a promising tool for quantum chemistry on near-term quantum computers and has been used extensively to estimate molecular excited states. Here, we explore a novel application of this method, employing it to compute thermal averages of quantum systems, specifically molecules like ethylene and butadiene. A drawback of qEOM is that it requires measuring the expectation values of a large number of observables on the ground state of the system, and the number of necessary measurements can become a bottleneck of the method. In this work, we focus on measurements through informationally complete positive operator-valued measures (IC-POVMs) to achieve a reduction in the measurement overheads by estimating different observables of interest through the measurement of a single set of POVMs. We show with numerical simulations that the qEOM combined with IC-POVM measurements ensures satisfactory accuracy in the reconstruction of the thermal state with a reasonable number of shots. Full article

This study investigates the determinants of non-hydro renewable energy (NHRE) consumption across 31 provincial regions in China, spanning from 2015 to 2022. Utilizing fixed effects and moderating effects, the research analyzes the impacts of research and development intensity (RDI), urbanization (UR), and human capital (HC) on NHRE consumption (NHREC). Additionally, the moderating roles of industrial structure (IS) and tax (TA) are examined, along with control variables such as informationization and economic development. The findings reveal that increased RDI significantly boosts NHREC by enhancing technological advancements. UR also positively affects NHREC, particularly in rapidly urbanizing regions, while HC provides a skilled workforce that drives renewable energy projects. However, the study identifies that concentrated industrial structures and high taxes can negatively moderate these positive impacts, highlighting the complex interplay of these factors. Policy recommendations include creating “Renewable Energy Innovation Hubs” in underdeveloped regions to attract startups and researchers, developing “Solar Cities” with mandated solar panels on all buildings, and introducing a “Carbon Offset Lottery” to incentivize investments in renewable energy. Full article

In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields. Full article

Some recent studies have posed the hypothesis according to which the grammatical stage that precedes the cultured trend of Brazilian Portuguese is Colonial Brazilian Portuguese, not Classical Portuguese. Therefore, there are still few works systematically comparing these two varieties. This is the goal of the present paper, which focuses on word order. By undertaking a corpus-based study using the same textual genre from the contemporary authors Eusébio de Matos and António Vieira, we have looked for all word order patterns while paying special attention to the X*VS order (with one or more constituents preceding the verb and a postverbal subject), given that it is quite typical of Classical Portuguese as a V2-like grammar, unlike the modern Portuguese grammars. We have observed that, although the colonial text follows the general trends of the classical language, it starts to depart from a V2-like grammar because it shows a higher frequency of non-V2 orders and a preference for informationally marked constructions involving internal positions to the clause. From a parameter hierarchy viewpoint, the main conclusion is that such differences represent frequency divergences which are consistent with nano- or microparametric changes which took place later. Full article