Search Results (144)

Cultural heritage is increasingly mobilized as a tool of international engagement, yet the diplomatic uses of heritage remain conceptually underdeveloped and analytically fragmented. This paper introduces the Heritage Diplomacy Spectrum, a multidimensional framework that maps how states and affiliated actors use heritage—both tangible and intangible—to pursue strategic, symbolic, and normative goals in cross-border contexts. Drawing on critical heritage studies, international relations, and memory politics, this study identifies six analytical dimensions (e.g., proactive vs. reactive, cultural vs. historical, strategic vs. moral) and develops seven ideal types of heritage diplomacy, ranging from soft power projection to post-dependency and corrective diplomacy. These ideal types, constructed in the Weberian tradition, serve as heuristic tools to illuminate the varied motivations and diplomatic postures underlying heritage-based engagement. A central matrix is presented to illustrate how each type aligns with different strategic logics and affective registers. This study argues that heritage diplomacy constitutes a distinct modality of heritage governance—one that transcends soft power narratives and encompasses conflict, reconciliation, symbolic redress, and identity assertion. The framework contributes both to theory-building and policy analysis, offering a diagnostic lens through which the ethical, political, and communicative dimensions of heritage diplomacy can be more systematically understood. Full article

This paper presents a novel approach to thermal process control in the food industry, specifically targeting the pasteurization and cooking of soft-boiled eggs. The unique challenge of this process lies in the precise temperature control required, as pasteurization and cooking must occur within a narrow temperature range. Traditional control methods, such as fuzzy logic controllers, have proven insufficient due to their limitations in handling varying loads and environmental conditions. To address these challenges, we propose the integration of robust reinforcement learning (RL) techniques, particularly the utilization of the Deep Q-Network (DQN) algorithm. Our approach involves training an RL agent in a simulated environment to manage the thermal process with high accuracy. The RL-based system adapts to different heat capacities, initial conditions, and environmental variations, demonstrating superior performance over traditional methods. Experimental results indicate that the RL-based controller significantly improves temperature regulation accuracy, ensuring consistent pasteurization and cooking quality. This study opens new avenues for the application of artificial intelligence in industrial food processing, highlighting the potential for RL algorithms to enhance process control and efficiency. Full article

Surgical procedures trigger dynamic inflammatory responses that influence postoperative pain, wound healing, and long-term outcomes. Conventional therapies rely on the systemic delivery of anti-inflammatory and analgesic agents, which often lack spatiotemporal precision and carry significant side effects. Inflammation-responsive hydrogels offer a promising alternative by enabling localized, stimulus-adaptive drug release aligned with the evolving biochemical milieu of surgical wounds. These smart biomaterials respond to endogenous triggers, such as reactive oxygen species, acidic pH, and proteolytic enzymes, allowing precise modulation of inflammation and tissue repair. This narrative review outlines the pathophysiological features of perioperative inflammation and the design principles of responsive hydrogel systems, including pH-, reactive oxygen species-, enzyme-sensitive, and multi-stimuli platforms. We evaluated the integration of key payloads, NSAIDs, corticosteroids, α₂-adrenergic agonists, and biologics, highlighting their therapeutic synergy and translational relevance. Preclinical studies across soft tissue, orthopedic, thoracic, and abdominal models have demonstrated the efficacy of these systems in modulating immune responses, reducing pain, and enhancing regeneration. Despite these encouraging results, challenges remain, including trigger fidelity, surgical compatibility, and regulatory readiness. Future advances in biosensor integration, logic-based design, and artificial intelligence-guided formulation may accelerate clinical translation. Inflammation-responsive hydrogels represent a transformative strategy for precise perioperative care. Full article

The reliability of decision-making algorithms within soft set theory is fundamentally constrained by their underlying membership functions. Traditional binary approaches overlook the implicit connections between the attributes a candidate possesses and those it lacks—connections that can be inferred from the wider candidate pool. To address this core challenge, this paper puts forward the Relational Membership Value Calculation (RMVC), an algorithmic framework whose core is a fine-grained relational membership function. Our approach moves beyond binary logic to capture these nuanced interrelationships. We provide a rigorous theoretical analysis of the proposed algorithm, including its computational complexity and robustness, which is validated through a comprehensive sensitivity analysis. Crucially, a comparative analysis using the Gini Index quantitatively demonstrates that our method provides significantly higher granularity and discriminatory power on a representative case study. The RMVC is implemented as an open-source Python program, providing a foundational tool to enhance the reasoning capabilities of AI-driven decision support and expert systems. Full article

The remarkable success of autoregressive Large Language Models (LLMs) is predicated on the causal attention mechanism, which enforces a static and rigid form of informational asymmetry by permitting each token to attend only to its predecessors. While effective for sequential generation, this hard-coded unidirectional constraint fails to capture the more complex, dynamic, and nonlinear dependencies inherent in sophisticated reasoning, logical inference, and discourse. In this paper, we challenge this paradigm by introducing Dynamic Asymmetric Attention (DAA), a novel mechanism that replaces the static causal mask with a learnable context-aware guidance module. DAA dynamically generates a continuous-valued attention bias for each query–key pair, effectively learning a “soft” information flow policy that guides rather than merely restricts the model’s focus. Trained end-to-end, our DAA-augmented models demonstrate significant performance gains on a suite of benchmarks, including improvements in perplexity on language modeling and notable accuracy boosts on complex reasoning tasks such as code generation (HumanEval) and mathematical problem-solving (GSM8k). Crucially, DAA provides a new lens for model interpretability. By visualizing the learned asymmetric attention patterns, it is possible to uncover the implicit information flow graphs that the model constructs during inference. These visualizations reveal how the model dynamically prioritizes evidence and forges directed logical links in chain-of-thought reasoning, making its decision-making process more transparent. Our work demonstrates that transitioning from a static hard-wired asymmetry to a learned and dynamic one not only enhances model performance but also paves the way for a new class of more capable and profoundly more explainable LLMs. Full article

In advanced Complementary Metal-Oxide-Semiconductor (CMOS) technologies, where diminished feature sizes amplify radiation-induced soft errors, the optimization of fault-tolerant circuit designs requires detailed transistor-level analysis of reliability–performance trade-offs. As a fundamental building block in digital systems and critical data paths, the 2:1 multiplexer, widely used in data-path routing, clock networks, and reconfigurable systems, provides a critical benchmark for assessing radiation-hardened design methodologies. In this context, this work aims to analyze the power consumption, area overhead, and delay of 2:1 multiplexer designs under transient fault conditions, employing the CMOS and Differential Cascode Voltage Switch Logic (DCVSL) logic styles and mitigation strategies. Electrical simulations were conducted using 32 nm high-performance predictive technology, evaluating both the original circuit versions and modified variants incorporating three mitigation strategies: transistor sizing, D-Cells, and C-Elements. Key metrics, including power consumption, delay, area, and radiation robustness, were analyzed. The C-Element and transistor sizing techniques ensure satisfactory robustness for all the circuits analyzed, with a significant impact on delay, power consumption, and area. Although the D-Cell technique alone provides significant improvements, it is not enough to achieve adequate levels of robustness. 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

The application of soft computing techniques, with a special emphasis on fuzzy logic, represents a modern approach to analyzing complex educational data. This paper explores the possibilities of applying soft computing to identify and interpret factors that influence the motivation and educational achievement of students in academic and professional studies, with special reference to the differences between these two groups of students in experienced subjects. Fuzzy logic enables more detailed processing of educational parameters that are subject to subjective interpretations and are often not clearly defined. By using this approach, decision support systems are developed that facilitate the understanding of students’ motivational patterns, their preferences, and challenges in mastering different types of content. Analyzing educational data seeks to identify relevant motivational factors that can contribute to shaping more effective and personalized teaching strategies. The goal of the work is to improve the quality of the educational process through the integration of soft computing methods, to raise the level of engagement and success of students in various fields of study. Full article

(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment. Full article

In order to meet the demand for high-voltage architectures of 400 V and 800 V in electric vehicle systems, high-power DC-DC converters have become a key focus of research. The Four-Switch Buck–Boost converter has gained widespread application due to its wide voltage conversion range, consistent input and output polarity, and the capability of bidirectional power transfer. This paper focuses on the energy conversion requirements in high-voltage scenarios for electric vehicles, analyzing the working principle of this converter and typical control strategies. It summarizes the issues encountered under different control strategies and presents improvements. Hard-switching multi-mode control strategies aim to improve control algorithms and logic to mitigate large duty cycle variations and voltage gain discontinuities caused by dead zones. For control strategies based on controlling the inductor current to achieve soft-switching, the discussion mainly focuses on optimizing the implementation of soft-switching, reducing overall system losses, and improving the computation speed. Finally, the paper summarizes FSBB control strategies and outlines future directions, providing theoretical support for high-voltage fast charging and onboard power supplies in electric vehicles. Full article

This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article

Phosphorus is one of the most abundant minerals in the body and plays a critical role in numerous cellular and metabolic processes. Most of the phosphate is deposited in bones, 14% is present in soft tissues as various organic phosphates, and only 1% is found in extracellular space, mainly as inorganic phosphate. The plasma inorganic phosphate concentration is closely maintained between 2.5 and 4.5 mg/dL by intertwined interactions between fibroblast growth factor 23 (FGF-23), parathyroid hormone (PTH), and vitamin D, which tightly regulate the phosphate trafficking across the gastrointestinal tract, kidneys, and bones. Disruption of the strict hemostatic control of phosphate balance can lead to altered cellular and organ functions that are associated with high morbidity and mortality. In the past three decades, there has been a steady increase in the prevalence of kidney failure (KF) among populations. Individuals with KF have unacceptably high mortality, and well over half of deaths are related to cardiovascular disease. Abnormal phosphate metabolism is one of the major factors that is independently associated with vascular calcification and cardiovascular mortality in KF. In early stages of CKD, adaptive processes involving FGF-23, PTH, and vitamin D occur in response to dietary phosphate load to maintain plasma phosphate level in the normal range. However, as the CKD progresses, these adaptive events are unable to overcome phosphate retention from continued dietary phosphate intake and overt hyperphosphatemia ensues. As these hormonal imbalances and the associated adverse consequences are driven by the underlying hyperphosphatemic state in KF, it appears logical to strictly control serum phosphate. Conventional dialysis is inadequate in removing phosphate and most patients require dietary restrictions and pharmacologic interventions to manage hyperphosphatemia. However, diet control comes with many challenges with adherence and may place patients at risk for inadequate protein intake and malnutrition. Phosphate binders help to reduce phosphate levels but come with a sizable pill burden and high financial costs and are associated with poor adherence and psychosocial issues. Additionally, long-term use of binders may increase the risk of calcium, lanthanum, or iron overload or promote gastrointestinal side effects that exacerbate malnutrition and affect quality of life. Given the aforesaid challenges with phosphorus binders, novel therapies targeting small intestinal phosphate absorption pathways have been investigated. Recently, tenapanor, an agent that blocks paracellular absorption of phosphate via inhibition of enteric sodium–hydrogen exchanger-3 (NHE3) was approved for the treatment of hyperphosphatemia in KF. While various clinical tools are now available to manage hyperphosphatemia, there is a lack of convincing clinical data to demonstrate improvement in outcomes in KF with the lowering of phosphorus level. Conceivably, deleterious effects associated with hyperphosphatemia could be attributable to disruptions in phosphorus-sensing mechanisms and hormonal imbalance thereof. Further exploration of mechanisms that precisely control phosphorus sensing and regulation may facilitate development of strategies to diminish the deleterious effects of phosphorus load and improve overall outcomes in KF. Full article

Controlling systems with highly nonlinear or uncertain dynamics present significant challenges, particularly when using conventional Proportional–Integral–Derivative (PID) controllers, as they can be difficult to tune. While PID controllers can be adapted for such systems using advanced tuning methods, they often struggle with lag and instability due to their integral action. In contrast, fuzzy Proportional–Derivative (PD) controllers offer a more responsive alternative by eliminating reliance on error accumulation and enabling rule-based adaptability. However, their industrial adoption remains limited due to challenges in manual rule design. To overcome this limitation, Fuzzy Neural Networks (FNNs) integrate neural networks with fuzzy logic, enabling self-learning and reducing reliance on manually crafted rules. However, most fuzzy neural network PD (FNNPD) controllers rely on mean square error (MSE)-based training, which can be inefficient and unstable in complex, dynamic systems. To address these challenges, this paper presents a Soft Fuzzy Reinforcement Neural Network PD (SFPD) controller, integrating the Soft Actor–Critic (SAC) framework into FNNPD control to improve training speed and stability. While the actor–critic framework is widely used in reinforcement learning, its application to FNNPD controllers has been unexplored. The proposed controller leverages reinforcement learning to autonomously adjust parameters, eliminating the need for manual tuning. Additionally, entropy-regularized stochastic exploration enhances learning efficiency. It can operate with or without expert knowledge, leveraging neural network-driven adaptation. While expert input is not required, its inclusion accelerates convergence and improves initial performance. Experimental results show that the proposed SFPD controller achieves fast learning, superior control performance, and strong robustness to noise, making it effective for complex control tasks. Full article