Search Results (881)

In the Chan Buddhism koan (gong’an 公案) tradition, the act of “slaying the snake” functions as a signature gesture imbued with complex, historically layered cultural meanings. Rather than merely examining its motivations, this paper emphasizes tracing the semantic transformations that this motif has undergone across different historical contexts. It argues that “snake-slaying” operated variously as an imperial narrative strategy reinforcing ruling class ideology; as a form of popular resistance by commoners against flood-related disasters; as a dietary practice among aristocrats and literati seeking danyao (elixirs) 丹藥 for reclusion and transcendence; and ultimately, within the Chan tradition, as a method of spiritual cultivation whereby masters sever desires rooted in attachment to both selfhood and the Dharma. More specifically, first, as an imperial narrative logic, snake-slaying embodied exemplary power: both Liu Bang 劉邦 and Guizong 歸宗 enacted this discursive strategy, with Guizong’s legitimacy in slaying the snake deriving from the precedent set by Liu Bang. Second, as a folk strategy of demystification, snake-slaying acquired a moral aura—since the snake was perceived as malevolent force, their slaying appeared righteous and heroic. Finally, as a mode of self-cultivation among the aristocracy, snake-slaying laid the groundwork for its later internalization. In Daoism, slaying the snake was a means of cultivating the body; in Chan Buddhism, the act is elevated to a higher plane—becoming a way of cultivating the mind. This transformation unfolded naturally, as if predestined. In all cases, the internalization of the snake-slaying motif was not an overnight development: the cultural genes that preceded its appearance in the Chan tradition provided the fertile ground for its karmic maturation and discursive proliferation. Full article

The rapid rise of the silver economy in the new era has become a new driving force for socio-economic development. From the perspective of Marxist political economy theory, this paper analyzes the intrinsic logic of the silver economy’s development through three dimensions: surplus value, labor market, and capital. The study finds that the silver economy in the new era faces challenges such as insufficient supply of high-quality elderly care services, simultaneous shortages in both total talent quantity and structural imbalances, and contradictions between capital’s profit-seeking nature and social welfare. By introducing the multiple streams model, the paper elucidates the coupling process of these three streams and the timing of policy window openings. It proposes targeted strategies, including strengthening technological innovation, deepening labor market reforms, and optimizing capital allocation, to promote the robust development of China’s silver economy and inject strong momentum into sustainable and high-quality economic growth. Full article

This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of requirements and logic expression generation; (4) dynamic visualization of cause–effect graphs; (5) path-sensitization-driven optimization of test sequences. Validated on VCU software from a leading OEM, the method achieves 97.3% variable matching accuracy and 100% test case executability, reducing invalid cases by 63% compared to conventional NLP approaches. This framework provides an explainable and traceable automated solution for intelligent vehicle software validation, significantly enhancing efficiency and reliability in automotive testing. Full article

Evaluating the approach of the apple’s maturity is a crucial aspect of enhancing agricultural efficiency, especially in the context of harvesting. Traditional approaches depend on fixed criteria that fail to account for the natural growth conditions of the fruit. To address this limitation, a fuzzy logic-based system was introduced to evaluate apple ripeness. This model highlights a notable disparity between these factors and maturity. It incorporates the essential elements anticipated to correlate with ripeness, while maintaining the integrity of the inputs to create a holistic framework for assessing maturity. This system ensures that apples are harvested at the optimal time, thereby improving their overall quality. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Choosing a rural destination today often comes down to one thing: how authentic it feels. In countries like Romania, where tradition is still woven into daily life, travelers are looking for something real and sustainable—but what exactly does that mean? And how can we measure it? This study takes a different approach. We created an Authenticity Index using fuzzy logic, a method that makes space for in-between answers and soft boundaries. It helped us capture how people actually perceive things like local food, architecture, and natural scenery—without forcing their opinions into rigid categories. We tested the index with real guest feedback from rural accommodation. The results showed that guests consistently valued sensory experiences—like nature and food—more than activities that required deeper cultural involvement, such as workshops or folk demonstrations. Instead of just producing a number, the index turned out to be a guide. It gives hosts a better idea of what really matters to their guests—even when those preferences are not always easy to define. More than that, it brings together what theory says with what visitors actually feel, supporting more sustainable tourism practices. And in rural tourism, that connection can make all the difference. Full article

Adoptive cell therapies have transformed the treatment landscape for hematologic malignancies. Yet, translation to solid tumors remains constrained by antigen heterogeneity, an immunosuppressive tumor microenvironment (TME), and poor persistence of conventional CAR-T cells. In response, innate immune cell platforms, particularly chimeric antigen receptor–engineered natural killer (CAR-NK) cells and chimeric antigen receptor–macrophages (CAR-MΦ), have emerged as promising alternatives. This review summarizes recent advances in the design and application of CAR-NK and CAR-MΦ therapies for solid tumors. We highlight key innovations, including the use of lineage-specific intracellular signaling domains (e.g., DAP12, 2B4, FcRγ), novel effector constructs (e.g., NKG7-overexpressing CARs, TME-responsive CARs), and scalable induced pluripotent stem cell (iPSC)-derived platforms. Preclinical data support enhanced antitumor activity through mechanisms such as major histocompatibility complex (MHC)-unrestricted cytotoxicity, phagocytosis, trogocytosis, cytokine secretion, and cross-talk with adaptive immunity. Early-phase clinical studies (e.g., CT-0508) demonstrate feasibility and TME remodeling with CAR-MΦ. However, persistent challenges remain, including transient in vivo survival, manufacturing complexity, and risks of off-target inflammation. Emerging combinatorial strategies, such as dual-effector regimens (CAR-NK⁺ CAR-MΦ), cytokine-modulated cross-support, and bispecific or logic-gated CARs, may overcome these barriers and provide more durable, tumor-selective responses. Taken together, CAR-NK and CAR-MΦ platforms are poised to expand the reach of engineered cell therapy into the solid tumor domain. Full article

Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and efficiently through teleoperation. The lightweight, wearable myoelectric armband, due to its portability and environmental robustness, provides a natural human–robot gesture interaction interface. However, current myoelectric teleoperation gesture control faces two major challenges: (1) poor intuitiveness due to visual-motor misalignment; and (2) low efficiency from discrete, single-degree-of-freedom control modes. To address these challenges, this study proposes an integrated myoelectric teleoperation interface. The interface integrates the following: (1) a novel hybrid reference frame aimed at effectively mitigating visual-motor misalignment; and (2) a finite state machine (FSM)-based control logic designed to enhance control efficiency and smoothness. Four experimental tasks were designed using different end-effectors (gripper/dexterous hand) and camera viewpoints (front/side view). Compared to benchmark methods, the proposed interface demonstrates significant advantages in task completion time, movement path efficiency, and subjective workload. This work demonstrates the potential of the proposed interface to significantly advance the practical application of wearable myoelectric sensors in human–robot interaction. Full article

Traditional villages are critical to preserving cultural heritage and promoting sustainable rural development. This study evaluates the development potential of 47 traditional villages in Jilin Province from the perspectives of spatial planning, architectural conservation, and rural real estate revitalization. A Development Potential Assessment (DPA) framework is constructed based on five dimensions: geographical position, cultural resources, socio-economic factors, natural ecology, and living environment. The AHP-entropy weighting method is applied to ensure objectivity in scoring, while kernel density analysis and coefficient of variation techniques identify spatial patterns and internal disparities. To further inform strategic planning and targeted investment, an Importance–Performance Analysis (IPA) model is introduced, aligning resource advantages with development performance. Key findings include the following: (1) significant spatial heterogeneity, with higher potential concentrated in the southeast and lower levels in the northwest; (2) cultural and socio-economic dimensions are the most influential factors in differentiating development types; and (3) a subset of villages shows a disconnect between resource endowment and realized potential, indicating the need for tailored design interventions and investment strategies. This research offers a visual and data-driven basis for differentiated revitalization strategies, integrating urban science methods, architectural thinking, and real estate development logic. It supports refined policy implementation, spatial design decisions, and the activation of underutilized rural assets through context-sensitive planning. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

Digitalisation can positively impact the efficiency of real-world business processes, but may also introduce new cybersecurity challenges. One area that is particularly vulnerable to cyber-attacks is the business logic embedded in processes in which flaws may exist. This is especially the case when these processes are within web-based applications and services, which is increasingly becoming the norm for many organisations. Business logic vulnerabilities (BLVs) can emerge following the software development process, which may be difficult to detect by vulnerability detection tools. Through a systematic literature review and interviews with industry practitioners, this study identifies key BLV types and the challenges in detecting them. The paper proposes an eight-stage operational framework that leverages Artificial Intelligence (AI) for enhanced BLV detection and mitigation. The research findings contribute to the rapidly evolving theory and practice in this field of study, highlighting the current reliance on manual detection, the contextual nature of BLVs, and the need for a hybrid, multi-layered approach integrating human expertise with AI tools. The study concludes by emphasizing AI’s potential to transform cybersecurity from a reactive to a proactive defense against evolving vulnerabilities and threats. Full article

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

Heortia vitessoides Moore (Lepidoptera: Pyralidae), the dominant outbreak defoliator of Aquilaria sinensis (Myrtales: Thymelaeaceae, the agarwood-producing tree), poses a severe threat to the sustainable development of the agarwood industry. Current research has preliminarily revealed its biological traits and gene functions. However, significant gaps persist in integrating climate adaptation mechanisms, control technologies, and host interaction networks across disciplines. This review systematically synthesizes the multidimensional mechanisms underlying H. vitessoides outbreaks through the logical framework of “Fundamental Biology of Outbreaks—Environmental Drivers—Control Strategies—Molecular Regulation—Host Defense.” First, we integrate the biological characteristics of H. vitessoides with its climatic response patterns, elucidating the ecological pathways through which temperature and humidity drive population outbreaks by regulating development duration and host resource availability. Subsequently, we assess the efficacy and limitations of existing control techniques (e.g., pheromone trapping, Beauveria bassiana application), highlighting the critical bottleneck of insufficient mechanistic understanding at the molecular level. Building on this, we delve into the molecular adaptation mechanisms of H. vitessoides. Specifically, detoxification genes (e.g., HvGSTs1) and temperature stress-responsive genes (e.g., HvCAT, HvGP) synergistically enhance stress tolerance, while chemosensory genes mediate mating and host location behaviors. Concurrently, we reveal the host defense strategy of A. sinensis, involving activation of secondary metabolite defenses via the jasmonic acid signaling pathway and emission of volatile organic compounds that attract natural enemies—an “induced resistance–natural enemy collaboration” mechanism. Finally, we propose future research directions: deep integration of gene editing to validate key targets, multi-omics analysis to decipher the host–pest–natural enemy interaction network, and development of climate–gene–population dynamics models. These approaches aim to achieve precision control by bridging molecular mechanisms with environmental regulation. This review not only provides innovative pathways for managing H. vitessoides but also establishes a paradigm for cross-scale research on pests affecting high-value economic forests. Full article