Search Results (4,005)

Softness-Weakness constitutes a core category in Laozi’s philosophy, while in Yan Zun’s Laozi zhigui of the Western Han dynasty, Harmony-Weakness becomes the key concept for interpreting Laozi’s thought. This conceptual transformation from Softness-Weakness to Harmony-Weakness both reflects the intellectual background of Confucian–Daoist synthesis in the Han dynasty and marks the creative development of Daoist philosophy during this period. Building upon complete inheritance of Laozi’s Softness-Weakness thought, Yan Zun achieved a theoretical reconstruction of Daoist philosophy through introducing Harmony—this Confucian core category. At the cosmological level, Yan Zun creatively incorporated Supreme-Harmony into the sequence of the Dao’s generation, establishing its ontological position as the “progenitor” of the myriad things. Through the proposition “Harmony is its destination, Weakness is its function”, Yan Zun endowed Harmony-Weakness with a clear teleological dimension and value orientation, elevating Harmony-Weakness from a survival strategy to a fundamental principle of cosmic generation. At the practical level, through the Harmony-Weakness concept, Yan Zun constructs a complete system integrating self-cultivation and politics, developing Daoist thought from relatively dispersed wisdom discourse into systematic theory. This conceptual transformation transcends the simple opposition between Softness-Weakness and hardness-strength, achieving a unity in which hardness and Softness mutually assist each other under Harmony’s regulation. However, while the introduction of Harmony deepened the theory, it may also have somewhat weakened the critical edge of Softness-Weakness thought, and the substantialization of Supreme-Harmony may have departed from Laozi’s nihilistic spirit. This theoretical tension precisely demonstrates the theoretical dilemmas and historical choices that Daoist thought faced in its Han dynasty development. Full article

Unused, underutilized, abandoned, and residual urban spaces are increasingly recognized as potential resources for adaptive reuse, ecological improvement, and urban resilience. In this study, such areas are approached through the overarching concept of waste space, a term that captures both their underutilized condition and their transformation potential. While existing research has largely focused on the definition, classification, and emergence of such spaces, their potential for transformation across varying spatial and institutional contexts has received comparatively limited attention. Addressing this gap, this study operationalizes selected social–ecological system (SES) dynamics through spatial analysis in the metropolitan area of İzmir, Türkiye, offering a proxy-based assessment of transformation capacity rather than a direct transformation. Using district-level analysis across ten metropolitan districts, this research combines typological and morphological classification of waste spaces with four spatial indicators: the Density Index, Location Quotient, Shannon Diversity Index, and Typology Dominance Index. The results show that waste spaces are unevenly distributed across İzmir and form distinct district-level configurations shaped by infrastructure expansion, post-industrial transformation, speculative vacancy, and fragmented urban growth. This study concludes that waste spaces cannot be addressed through a uniform regeneration logic. By linking SES dynamics with measurable spatial indicators, the proposed framework offers a context-sensitive, proxy-based basis for indicating transformation capacity of waste spaces and supporting district-specific planning and policy decisions. Full article

Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this background, this study evaluates multi-station temporal forecasting models within a single-year, station-based proof-of-concept benchmark under unified data conditions. We adapt the Transformer and Informer architectures to this meteorological setting, rigorously preprocess the AWS dataset to avoid data leakage, and select predictive variables using complementary linear and nonlinear relevance criteria. Model performance is assessed using continuous and categorical precipitation metrics, including the Critical Success Index (CSI). The results show that the Informer outperforms the recurrent neural network (RNN) baselines and achieves the lowest mean MAE and RMSE together with the highest mean CSI among the evaluated models while using substantially fewer parameters than the standard Transformer. However, its sample-wise absolute error distribution remains statistically comparable to that of the standard Transformer. Overall, this study establishes a single-year, station-based proof-of-concept benchmark for comparing architectures in very-short-term (1–5 h ahead) precipitation forecasting. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

The problem of truth-grounding occupies a central position in the history of metaphysics. Aquinas synthesizes Aristotelian epistemology with the Augustinian doctrine of illumination, establishing a comprehensive system in which participation in the divine intellect, expressed through the illuminative function of the agent intellect, serves as the foundational principle. Heidegger, through a critical transformation of this system, opens an alternative path to the ontological grounding of truth. Rather than standing in simple opposition, the two thinkers stand in a relation of critical engagement. Heidegger preserves the phenomenological validity of the illumination metaphor, acknowledging the fundamental structure of truth as manifestation, while simultaneously dismantling the theological framework that undergirds its transcendent guarantee. In its place, he advances the ontological concepts of the clearing (Lichtung) and appropriation (Ereignis). This conceptual transformation marks a decisive shift in the grounding of truth: from dependence on the eternal assurance of a transcendent being to the historical self-disclosure inherent in Dasein itself. Full article

Chirped fiber Bragg gratings (CFBGs) are robust diagnostic sensors that are widely used to track detonation-driven and shock wave propagation. CFBGs are inscribed with a linearly chirped periodic index of refraction changes that alter the Bragg wavelength along the length of the probe. The light return of each individual Bragg element is captured by a detector at a unique time to map the full reflected spectrum. The CFBG spectrum is measured with a dispersive Fourier transform of the reflected light that temporally stretches the spectrum to increase spatial resolution and make a one-to-one map of the wavelength on a time axis. Here, we propose an improvement of CFBG temporal resolution by incorporating two co-linear laser pulses with orthogonal polarization states and a 5 ns time offset. The two separate signals were split and tracked by two separate detectors. An oscilloscope captured good separation in the signals, and two separate spectrograms were generated and interleaved in the post-processing of the data. This novel technique doubled the CFBG temporal resolution and led to a doubled location resolution. As a proof-of-concept of this technique, the resolution improvement was compared between standard CFBG measurements and the two polarization states method on a position-sensitive CFBG sensor. CFBG resolution doubling will advance sensor capabilities and will have a direct impact on improving capture and analysis in dynamic, high-explosive experiments. Full article

This study examines the applicability of coding-theoretic tools to the digital monitoring of information space. The proposed approach treats response patterns to socially significant stimuli as binary sequences and interprets their analysis as a classification problem analogous to error correction in coding theory. To verify the feasibility of this framework, a model psychological test consisting of seven binary questions was analyzed using a procedure derived from the Hamming code (7,4). The method makes it possible to map the full space of observed answer combinations onto a smaller set of reference codewords and thereby identify stable response configurations. The obtained results show that the distributions produced after coding-based transformation are markedly non-uniform and contain recurrent maxima, indicating the presence of structured patterns in collective responses. It is also shown that permutations of question order substantially affect the resulting distributions and correlation indicators, which highlights both the sensitivity and the analytical potential of the proposed encoding scheme. The main contribution of the study is methodological: it demonstrates that error-correcting coding can be operationalized as a formal tool for detecting latent regularities in simplified monitoring data. At the same time, the present results should be regarded as proof of concept, since further work is required to validate the approach on larger datasets, compare it with baseline classification methods, and extend it to longer and multivalued response sequences. Full article

Hepatocellular carcinoma (HCC) represents a major global health challenge and the third leading cause of cancer-related mortality worldwide. Its epidemiological burden is rapidly increasing, largely driven by the rising prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD), which is now recognized as the most common chronic liver disease globally. Notably, MASLD frequently coexists with type 2 diabetes mellitus (T2DM), sharing several features, including the interplay of common genetic, metabolic, and environmental factors, thus contributing to a complex multifactorial pathogenesis. Relevantly, patients affected by both conditions represent a subgroup at particularly high risk of liver disease progression and hepatocarcinogenesis. In this population, metabolic and inflammatory disturbances act synergistically to create a pro-tumorigenic hepatic environment where insulin resistance (IR) plays a crucial role, by driving hepatic lipotoxicity, mitochondrial dysfunction, and inflammatory signaling with oxidative stress, thereby establishing a permissive environment for worsening steatosis and malignant transformation. Increasing evidence supports the concept of MASLD as a multisystem disorder reflecting the systemic nature of metabolic dysfunction. Within this framework, beyond IR, extrahepatic factors have also emerged as important contributors to steatosis progression, worsening of T2DM, and modulation of HCC risk. In particular, the gut–liver axis has gained recognition as a key regulator of hepatic homeostasis, integrating signals from the intestinal microbiota, immune responses, and metabolic pathways. Dysregulation of this crosstalk promotes systemic inflammation and metabolic imbalance, exacerbating IR and fostering a pro-oncogenic hepatic environment. This review examines the interconnected metabolic and immune mechanisms linking IR and gut–liver axis dysfunction to HCC development in patients with MASLD and T2DM, highlighting their implications for risk stratification and precision-based therapeutic strategies. Full article

The use of long-distance transoceanic cables equipped with high-loss loopbacks enables distributed sensing with a resolution determined by amplifier spacing, typically in the order of 50–100 km. Microwave-frequency fiber interferometry is a promising trans-mission technique for investigating long links supported by periodic optical amplification. In this paper, we propose a variant of this technique that ensures compatibility with links containing high-loss loopbacks, thereby transforming the integrated sensing approach into a distributed one. We highlight the critical modifications required to overcome challenges associated with the detection of multiple return signals, and we conduct a proof-of-principle experiment using a two-loop configuration. We demonstrate the concept by detecting and localizing low-frequency (<10 Hz) events—whether human-generated or induced by fiber stretchers—with span-level resolution. This validates the potential of the modified microwave-frequency interferometry approach for transoceanic cable monitoring in scenarios where high-loss loopbacks are present. We also present a theoretical analysis that evaluates the limits of the technique across different frequency ranges, in comparison with optical interferometry methods based on high-spectral-purity fiber lasers. The analysis shows that for long amplifier spacings (~100 km), micro-wave-frequency fiber interferometry exhibits a signal-to-noise ratio advantage at sub-Hz frequencies (<0.1 Hz) compared to state-of-the-art optical interferometers. Full article

Clothing pattern-making is an important part of transforming design concepts into finished products; however, the traditional manual pattern-making process is not only time-consuming, but also suffers from inefficiency, which seriously restricts the automation and precision of clothing production. This study proposes an automated clothing pattern-making method, the core of which lies in the organic combination of an improved Pix2Pix model and a conditional diffusion model. The improved Pix2Pix model effectively captures the complex structural information in clothing patterns by introducing a multi-scale discriminator and a new composite loss function. Due to limited data, the improved Pix2Pix falls short in terms of image generation quality, so a conditional diffusion model was introduced to enhance the detail and overall integrity of the generated images. Experiments were conducted on pattern-making tasks for the sleeves and back panels of various typical clothing styles. The sleeve components primarily validated the model’s basic generation capabilities. The results showed that the improved Pix2Pix-generated initial template could capture the basic contour structure, and after diffusion model repair, the lines became clearer and the details more complete; the back panels components validated the model’s robustness. Quantitative results showed that the proposed method achieved SSIM, PSNR, and LPIPS values of 0.869, 22.31, and 0.1318, respectively. Compared with the results of other advanced models, the proposed method exhibits the highest accuracy and clarity in the generated images, confirming its practicality and effectiveness in automated apparel pattern-making. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of death worldwide. Therefore, early diagnosis and treatment of these diseases are of critical importance. Stethoscopes are the easiest and fastest medical devices for the initial diagnosis of cardiovascular diseases. However, interpreting heart sounds requires considerable expertise. The use of artificial intelligence in healthcare for decision support has increased and become popular recently. Methods: The popular 2016 PhysioNet/CinC Challenge dataset, consisting of phonocardiogram (PCG) signals, was used to implement the proposed approach. Spectrogram and continuous wavelet transform (CWT) images of the PCG signals were first generated. This increased the distinguishability of the data in terms of both time and frequency components. These two-input images were tested on the developed Dual Time–Frequency Swin Transformer–ConvNeXt Attention Network (DTF-STCANet) model. To further improve classification accuracy, the Weighted KNN algorithm was preferred during the classification phase. Results: With the proposed approach, a 99.29% classification accuracy was achieved. Performance was compared with other state-of-the-art models. Conclusions: The proposed approach, through the integration of PCG signals with artificial intelligence, further strengthens the concept of early diagnosis of heart disease. Full article

With the high penetration of distributed photovoltaics (PV) in distribution areas, transformer capacity limits and source–load fluctuations have become key factors constraining PV accommodation. To accurately assess the PV hosting capacity under energy storage regulation, this paper proposes an assessment method based on operating region analysis. First, a coordinated operation model for the distribution area is established, incorporating the transformer capacity, energy storage constraints, and power balance. On this basis, the calculation boundaries for the PV hosting capacity are discussed in two scenarios: Model 1 ignores power curve uncertainty, characterizing the geometry of the conventional operating region to find the maximum deterministic hosting capacity ($S_1$) that keeps the region non-empty. Model 2 introduces box-type uncertainty sets for the source and load, proposes the concept of a “Self-Balanced Operating Region”, and constructs a robust feasibility determination model ($f_3$) based on a Min–Max–Min structure. To solve this multi-layer nested non-convex model, an iterative algorithm based on duality theory and Benders decomposition is employed to determine the robust hosting capacity under uncertainty ($S_2$) at the critical point where $f_3$ shifts from zero to non-zero. Case studies show that source–load uncertainty leads to a significant contraction of the operating region, and the robust hosting capacity under uncertainty requirements is strictly less than the deterministic hosting capacity ($S_1>S_2$). This method quantifies the reduction effect of uncertainty on the accommodation capability, providing a theoretical basis for planning high-renewable penetration distribution areas and energy storage configuration. Full article

As global digital transformation accelerates, artificial intelligence (AI) and blockchain technologies have evolved from theoretical concepts into practical tools within the field of accounting, particularly in financial reporting. This study conducts a systematic review of 62 sources drawn from major academic databases to develop a comprehensive framework for classifying application scenarios. The findings indicate that the application of artificial intelligence and blockchain technology can help improve the efficiency of financial report generation, enhance the reliability of data, and promote innovation in the auditing process. Nevertheless, persistent challenges remain, including concerns related to data security, technological limitations, and regulatory gaps. The study proposes a structured roadmap for the implementation of these technologies, underscoring their transformative potential in advancing the digital evolution of accounting, while also identifying key directions for future research. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

This article addresses recent critiques of secularisation as a linear explanatory model for religious change in European societies, proposing that contemporary artistic creation is a fertile site for observing new interrelations between the secular and the religious. Focusing on João Madureira’s Missa de Pentecostes (2010), composed for the ensemble ‘Sete Lágrimas’ and part of a cultural project by the Roman Catholic community of ‘Capela do Rato’ (Lisbon), the study analyses how this work creatively reconfigures the traditional Mass form. By juxtaposing the Ordinary sections (e.g., Kyrie, Gloria) with the Proper sections (e.g., Introitus, Sequentia), which incorporate non-canonical Portuguese poetic texts, the composition creates a hybrid space in which ritual and artistic modes interact and mutually re-legitimise each other. Using a heterological interpretative framework inspired by Michel de Certeau, the article highlights the tensions and exchanges between ritual and aesthetic logics. The analysis draws on key theoretical concepts including Jean Rancière’s notions of consensus and dissensus, Pierre Bourdieu’s theory of ritual and habitus, Paul Ricoeur’s philosophy of translation as hospitality, and Pierre Lévy’s concept of universalism without totality. The findings suggest that Madureira’s work enacts a process of poetic re-signification of religious memory, opening new possibilities for hybrid ritual–artistic practices. These practices transform ritual time-space into an interface that fosters plural and non-totalising forms of spiritual belonging. Full article