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Can human–machine hybrid systems (HMHs) constitute genuine collective agents? This paper defends an affirmative answer. I argue that HMHs achieve collective intentionality without shared consciousness by satisfying the following three functional criteria: goal alignment, functional complementarity, and stable interactivity. Against this functionalist account, the following two objections arise: (1) the cognitive bloat problem, that functional criteria cannot distinguish genuine cognitive integration from mere tool use; and (2) the phenomenological challenge, that AI’s lack of practical reason reduces human–AI interaction to subject–tool relations. I respond by distinguishing constitutive from instrumental functional contributions and showing that collective agency requires stable functional integration, not phenomenological fusion. The result is what I call Functional Hybrid Collective Agents (FHCAs), which are systems exhibiting irreducible collective intentionality through deep human–AI coupling.

Rural street spaces serve as primary venues for communal activities, yet emotion-based design in these spaces remains underexplored. This study delineates three typical scales of rural street spaces in China northern plains region, utilizing eye-tracking technology, investigates the constituent elements and materials of various optimized design schemes, analyzing people’s emotional perceptions of different elements and materials. The results indicate that: (1) narrower streets evoke a greater sense of security among individuals; (2) an increased variety and quantity of paving materials, landscape flower beds, seating areas, and public facilities heighten people’s visual interest, enhancing the spatial publicness and safety; (3) higher coverage of green landscape relaxes visual perceptions, leading individuals to linger and dwell in the space. Consequently, through judicious design of scale, constituent elements, and materials, rural street spaces can be effectively imbued with emotional expressions, thereby elevating the spatial quality of rural street spaces to meet people’s emotional needs.

With social and economic environment changes occurring in the world and deepening of the urbanization process, China’s urban development exhibits a new phenomenon of growth and shrinkage fluctuations. The resource-based city shrinkage phenomenon is particularly prominent. Research on the commonalities and patterns of similar groups should be enhanced. We constructed an urban shrinkage evaluation index system from the three dimensions of population, economy and space. Accordingly, we explored the spatiotemporal evolution characteristics of 175 resource-based cities in China from a multidimensional perspective with the entropy method, shrinkage model and transfer matrix method. The results indicated that most resource-based cities in China occurred in the non-shrinking state, but their development speed gradually decreased or even presented stagflation. The shrinkage measure-related results in the different dimensions revealed that the number of shrinking cities is increasing. The population, economic and comprehensive shrinkage levels were mainly slight and remained stable. The number of cities experiencing moderate and severe shrinkage was relatively small and mostly encompassed short-term shrinkage. Spatial shrinkage demonstrated a clear administrative hierarchy difference. Moreover, the spatial distribution range of shrinking cities in each dimension expanded and exhibited obviously similar characteristics, i.e., shrinking cities were relatively concentrated in Northeast China, while they were more scattered in other regions. Furthermore, the geodetector technique was applied to reveal the influencing factors of resource-based city growth and shrinkage. Based on the results, the change in the secondary industry output value share at the start of the study was the primary factor. The impact of each employment structure indicator from 2014 to 2018 was particularly significant. Comprehensive exploration of the shrinkage characteristics of this particular group of cities and their development behavior from a multidimensional perspective can provide an important reference for the transformation and high-quality development of resource-based cities.

In order to objectively assess the effectiveness of rural space design on the affective dimension, this study utilized eye-tracking and electrocardiogram (ECG) monitoring techniques to quantify users’ visual attention and emotional responses and to assess the impact of rural design on users’ affective experience. The results show that incorporating natural elements and cultural features into the design enhances the values of the subjects’ pupil diameter change rate, Heart Rate Variability Index (HRV), subjective evaluation and reduces their Saccade Velocity Average. The experimental results not only verify the application value of eye tracking and ECG monitoring techniques in assessing the design effect of rural space, but also provide a scientific assessment method based on the user’s physiological and emotional responses, thus providing a strong support for the optimization of rural space design.

The root economics spectrum (RES), a common hypothesis postulating a tradeoff between resource acquisition and conservation traits, is being challenged by conflicting relationships between root diameter, tissue density (RTD) and root nitrogen concentration (RN). Here, we analyze a global trait dataset of absorptive roots for over 800 plant species. For woody species (but not for non-woody species), we find nonlinear relationships between root diameter and RTD and RN, which stem from the allometric relationship between stele and cortical tissues. These nonlinear relationships explain how sampling bias from different ends of the nonlinear curves can result in conflicting trait relationships. Further, the shape of the relationships varies depending on evolutionary context and mycorrhizal affiliation. Importantly, the observed nonlinear trait relationships do not support the RES predictions. Allometry-based nonlinearity of root trait relationships improves our understanding of the ecology, physiology and evolution of absorptive roots. Kong et al. use a global trait dataset of 800 plant species to examine the root economics spectrum in relation to root diameter, tissue density and root nitrogen concentration. Nonlinear trait relationships were observed, suggesting allometry-based nonlinearity in root trait relationships.

One of the primary challenges faced by small rubbing filament machines is the significant variability in material sizes, particularly in the feeding direction. This variability complicates the processing of locally baled straw with a single device. To address this issue, an adjustable feeding and bale-breaking device was developed and tested to enhance the filamentous performance of baled straw. The machine integrates a series of bale-breaking knives along with a pair of feeding and bale-breaking rollers. This paper presents an overview of the machine’s structure and operating principles, alongside an analysis of the forces acting on the straw within the device, which informed the design of key components and devices. A discrete element simulation model suitable for square baled-straw has been established, providing a research foundation for the subsequent optimization of device design parameters. Effects of motor bale-breaking roller rotating speed ( x 1 ), bale-breaking roller height ( x 2 ) and bale-breaking knife quantity ( x 3 ) on the productivity of bonding bond destruction rate ( Y 1 ) and the particle average speed ( Y 2 ) were explored. Three-dimensional quadratic regression orthogonal rotation central combination experiment method combined with response surface method was used to conduct experiments and explore the interaction effects of influence factors on indicators. A regression model of influence factors and evaluation indicators was established through the analysis of variance. The significant factors affecting Y 1 were ordered of x 1 , x 2 , x 3 , and the significant factors affecting Y 2 were ordered of x 2 , x 3 , x 1 . In the interaction of factors, x 1 x 2 and x 2 x 3 had an extremely significant impact, and x 1 x 3 had a significant impact on Y 1 ; x 1 x 2 , x 1 x 3 and x 2 x 3 had a significant impact on Y 2 . The optimal structure and working parameters combination were determined to be 1448 rpm for x 1 , 268 mm for x 2 , and 14 pieces for x 3 . Verification experiments demonstrated that the actual values were 96.95% for the straw rubbing rate and 235.13 kg/(kW·h) for the per unit power productivity. The operation of the adjustable feeding and bale-breaking device developed in this study proved effective in enhancing productivity and breaking performance during the feeding of baled straw. It successfully met the design requirements for the grain size necessary for the comprehensive utilization of straw. Overall, this research establishes a foundational basis for the further development of a small, multipurpose straw rubbing filament machine.

Antibody–drug conjugates (ADCs) represent a novel class of biopharmaceuticals comprising monoclonal antibodies covalently conjugated to cytotoxic agents via engineered chemical linkers. This combination enables targeted delivery of cytotoxic agents to tumor site through recognizing target antigens by antibody while minimizing off-target effects on healthy tissues. Clinically, ADCs overcome the limitations of traditional chemotherapy, which lacks target specificity, and enhance the therapeutic efficacy of monoclonal antibodies, providing higher efficacy and fewer toxicity anti-tumor biopharmaceuticals. ADCs have ushered in a new era of targeted cancer therapy, with 15 drugs currently approved for clinical use. Additionally, ADCs are being investigated as potential therapeutic candidates for autoimmune diseases, persistent bacterial infections, and other challenging indications. Despite their therapeutic benefits, the development and application of ADCs face significant challenges, including antibody immunogenicity, linker instability, and inadequate control over the release of cytotoxic agent. How can ADCs be designed to be safer and more efficient? What is the future development direction of ADCs? This review provides a comprehensive overview of ADCs, summarizing the structural and functional characteristics of the three core components, antibody, linker, and payload. Furthermore, we systematically assess the advancements and challenges associated with the 15 approved ADCs in cancer therapy, while also exploring the future directions and ongoing challenges. We hope that this work will provide valuable insights into the design and optimization of next-generation ADCs for wider clinical applications.

Understanding the geographic patterns and potential drivers of leaf stoichiometry is critical for modelling the nutrient fluxes of ecosystems and to predict the responses of ecosystems to global changes. This study aimed to explore the altitudinal patterns and potential drivers of leaf C∶N∶P stoichiometry. We measured the concentrations of leaf C, N and P in 175 plant species as well as soil nutrient concentrations along an altitudinal transect (500-2300 m) on the northern slope of Changbai Mountain, China to explore the response of leaf C∶N∶P stoichiometry to plant growth form (PGF), climate and soil. Leaf C, N, P and C∶N∶P ratios showed significant altitudinal trends. In general, leaf C and C∶N∶P ratios increased while leaf N and P decreased with elevation. Woody and herbaceous species showed different responses to altitudinal gradients. Trees had the largest variation in leaf C, C∶N and C∶P ratios, while herbs showed the largest variation in leaf N, P and N∶P ratio. PGF, climate and soil jointly regulated leaf stoichiometry, explaining 17.6% to 52.1% of the variation in the six leaf stoichiometric traits. PGF was more important in explaining leaf stoichiometry variation than soil and climate. Our findings will help to elucidate the altitudinal patterns of leaf stoichiometry and to model ecosystem nutrient cycling.

Hyperspectral images’ (HSIs) classification research has seen significant progress with the use of convolutional neural networks (CNNs) and Transformer blocks. However, these studies primarily incorporated Transformer blocks at the end of their network architectures. Due to significant differences between the spectral and spatial features in HSIs, the extraction of both global and local spectral–spatial features remains incomplete. To address this challenge, this paper introduces a novel method called TransHSI. This method incorporates a new spectral–spatial feature extraction module that leverages 3D CNNs to fuse Transformer to extract the local and global spectral features of HSIs, then combining 2D CNNs and Transformer to capture the local and global spatial features of HSIs comprehensively. Furthermore, a fusion module is proposed, which not only integrates the learned shallow and deep features of HSIs but also applies a semantic tokenizer to transform the fused features, enhancing the discriminative power of the features. This paper conducts experiments on three public datasets: Indian Pines, Pavia University, and Data Fusion Contest 2018. The training and test sets are selected based on a disjoint sampling strategy. We perform a comparative analysis with 11 traditional and advanced HSI classification algorithms. The experimental results demonstrate that the proposed method, TransHSI algorithm, achieves the highest overall accuracies and kappa coefficients, indicating a competitive performance.

Background Alzheimer’s disease (AD), the most common form of dementia, is caused by the degeneration of the central nervous system (CNS). A previous study reported that signal transducer and activator of transcription 3 (STAT3) is activated during AD development; nonetheless, the related mechanism remains unknown. Thus, this study used a cell model to explore whether and how the protein inhibitor of activated STAT3 (PIAS3) is involved in AD development. Methods Cerebrospinal fluid (CSF) specimens of 30 patients with AD and 10 normal participants were included in this study. SH-SY5Y cells were used to constructed AD model. Relevant indices were then detected and analyzed. Results The results showed that compared with the control group, PIAS3 expression was substantially decreased in patients with AD and amyloid beta (Aβ)-treated SH-SY5Y cells. PIAS3 overexpression was able to reverse the detrimental effects of Aβ treatment on cell survival and growth. Further, it could also ameliorate apoptosis and oxidative stress in Aβ-treated SH-SY5Y cells. Additionally, PIAS3 was shown to reduce the activated form of STAT3 and increase the activity of the downstream Nestin/nuclear factor erythroid 2-related factor/heme oxygenase-1 pathway. Conclusions STAT3 reactivation by colivelin treatment negated the influence of PIAS3 on the survival, growth, apoptosis, and oxidative stress of Aβ-treated SH-SY5Y cells.