Search Results (59)

The article discusses contexts of Henry Miller’s works (“Black Spring”, “Tropic of Capricorn”) and the poem Brooklyn Bridge by Vladimir Mayakovsky, which have in common the theme and imagery of a Bridge and the avant-garde era of creation. The authors of the article analyze not so much the “intersection” as the “union” of Miller and Mayakovsky, that is, not so much coincidences and closeness as complements that allow us to trace the entire breadth of the avant-garde literary project. In Henry Miller’s works the semantics of the image of a bridge referring to Nietzche’s Thus Spake Zarathustra is primarily noted and analyzed. In the analysis of Mayakovsky’s poem, special attention is paid to the verse and thematic composition of the text; metaphors; sound repetitions and echoes and their semantics; the specific historicism; and an important concept of reconstruction from traces, remains, and reflexes, turning to which Mayakovsky comes closer to, the unknown to him, Charles S. Peirce (abduction) and Carlo Ginzburg (keys), who was not yet born in the year the text was written. Full article

Individual differences in second-language (L2) proficiency are expected to influence how listeners parse and represent continuous speech, yet their neural signatures under naturalistic conditions remain unclear. We investigated this question using task-based fMRI during continuous speech listening. A total of 43 healthy participants completed four listening runs synchronized with MRI acquisition via PsychoPy(Peirce 2007), with eyes open throughout scanning. To promote sustained attention and comprehension, participants provided a native-language oral recall after each run. Based on behavioral proficiency scores, participants were grouped into low- (LP, n = 14), moderate- (MP, n = 14), and high-proficiency (HP, n = 15) groups. We evaluated three temporal information-encoding frameworks derived from BOLD dynamics: direct temporal series, functional connectivity (FC), and self-information weighted inter-subject correlation (ISC-W). Using a 10 × 5-fold nested cross-validation scheme, we tested both categorical classification (Support Vector Machines) for discrete proficiency groups (LP, MP, HP) and continuous multivariate regression (Ridge/Lasso) for continuous proficiency scores. Furthermore, we applied ROI-based ANOVA and univariate Neural Correlation Analysis (NCA) to identify key brain regions, evaluating significance via nonparametric permutation testing (1000 permutations) and False Discovery Rate (FDR) correction. Results indicated that while categorical classification yielded numerical trends—with ISC-W performing best—it did not reach statistical significance under stringent permutation testing. However, multivariate continuous regression using ISC-W features successfully predicted continuous proficiency scores with statistical significance (p < 0.05). Exploratory ROI analysis highlighted the bilateral orbital inferior frontal gyrus (IFG_orb_bilat) as a highly sensitive region. These findings suggest that L2 proficiency is best represented as a distributed, continuous neural variable, and that self-information weighting effectively filters background noise to capture cognitive variance. Methodologically, this study provides a reproducible pipeline integrating information-theoretic feature construction with rigorous whole-brain nonparametric inference. Full article

Introduction: Large language models (LLMs) are increasingly employed as cognitive aids in research and professional inquiry, yet their fluent outputs are frequently regarded as authoritative knowledge. We contend that this practice signifies a fundamental epistemic misalignment. Methods/Approach: Building on Peirce’s theory of inquiry, Sellars’ concept of the space of reasons, Stanovich’s tripartite model of cognition, and knowledge-building theory, we develop a conceptual framework for analyzing epistemic agency in human–LLM collaboration. Results/Argument: We demonstrate that LLM outputs fail to satisfy the conditions for knowledge because they lack reflective regulation, resistance to revision, and normative commitment. While LLMs display strong autonomous and algorithmic abilities (e.g., pattern recognition and hypothesis development), reflective control remains a distinctly human function. This asymmetry supports a principled division of epistemic labour and motivates the concept of the Knowledge-Building Partner (KBP): an AI system designed to support inquiry without claiming epistemic authority. Discussion/Implications: We identify prompt-, system-, and model-level design requirements and introduce a triangulated framework for operationalizing epistemic agency through explainable AI, discourse analysis, and rational-thinking measures. These contributions collectively reposition LLM limitations as epistemic design challenges rather than technical issues. Full article

The article has two objectives: It begins by noting that, in memory studies, indexicality has thus far played a role primarily in the analysis of photographs. Central to this was Roland Barthes’ insight that photography should be read not only as an iconic sign but also as an indexical sign: a photograph touches us not only through what it shows, i.e., not only as a pictorial representation of its referent, but also through the fact that it is an imprint of light, causing it to function like a trace in the snow. While the insight into the indexical quality of photography has been extremely fruitful, it is surprising that other indexical signs (such as shadows or echoes) have received no attention in the discussion of memory studies. The first objective of this essay is to make a start on this, and it does so in dialogue with a Norwegian comic book. In Steffen Kverneland’s Skygger (Shadows), family memories of the German occupation of Norway play a decisive role. Most studies of World War II in contemporary literature focus on how historical events are represented. Kverneland takes a different approach. He is not interested in the representation of memories but in the function of remembering. He relates memories of World War II to current crises and to the aesthetics of his artistic work. The second objective of this essay is to analyse the comic, paying close attention to the various dimensions of indexicality. Full article

This study investigates how the title of Ghassan Kenefânî’s Umm Saʿd functions as a conduit for the novel’s religious symbolism. It addresses the question of whether a literary title can symbolically mirror the sacred imagery embedded within a narrative. In Umm Saʿd, the main and subordinate titles form a meaningful intersection where the sacred, the resilience of struggle, and the preservation of identity converge. Through a semiotic approach, the study argues that the title surpasses its nominal role and operates as a multilayered sign that conveys the narrative’s ideological and thematic depth. Employing Saussure’s dyadic and Peirce’s triadic sign models, the analysis demonstrates how the title reveals the spiritual codes and sacred motifs woven into the text. The findings indicate that the title not only encapsulates the thematic core of the novel but also guides readers toward its social, cultural and religious dimensions. By remaining detached from political framings, the study underscores how the title brings together the worldly and the sacred within a unified interpretive space. Ultimately, this research uncovers the hidden religious layers of Umm Saʿd, offering an original perspective on the role of titles in mediating and transmitting religious themes in twentieth- and twenty-first-century fictional narratives. Full article

Classical thermodynamics (CT) has become integrated into everyday life, especially through its applications in engineering. In contrast, out-of-equilibrium thermodynamics (OET) is often viewed as a fundamental science that seems distant from daily experiences. While “energy” is a familiar term in households, “entropy,” which refers to degraded energy, remains enigmatic. This gap in understanding has significant implications for developing effective sustainability practices. CT typically emphasizes the efficiency of individual systems that produce work, often overlooking the entropy production that occurs within larger, interconnected systems. This paper aims to establish a philosophical framework that transforms OET into what is referred to as “lived thermodynamics.” This framework is grounded in pragmatism, particularly drawing from the early synthesis of thermodynamics and evolutionary theory proposed by Charles S. Peirce. A central aspect of this approach involves shifting the focus from traditional “systems” to out-of-equilibrium assemblages. In these assemblages, the physical trends of entropy production are often interrupted and redirected by evolutionary innovations and random events. The evolving envelope of open systems within these assemblages manifests an increasing rate of entropy production. This synthesis of thermodynamics and evolutionary theory builds on Lotka’s pioneering contributions and contemporary theories, particularly Vermeij’s work on the evolution of power. The framework introduces a sustainability criterion based on entropy. By applying this criterion, OET can evolve into “lived thermodynamics,” fostering a holistic understanding of energy use in devices and technological systems while considering the broader implications of entropy production in the out-of-equilibrium assemblages in which we live. Full article

This article examines how ethnographic methodology and literary theory can advance research engines and artificial intelligence systems beyond the reductive computational approaches that dominate contemporary AI development. Drawing on recent Stanford research revealing fundamental gaps in large language models’ ability to distinguish factual knowledge from belief, I argue that contemporary AI systems enact what I term “abducted semantics”—appropriating the inferential logic of human meaning-making while systematically attenuating the culturally embedded, phenomenologically grounded capacities that generate authentic understanding. Through close analysis of Clifford Geertz’s thick description, Charles Sanders Peirce’s triadic semiotics, and canonical literary works—Miguel de Cervantes’ Don Quixote and Gabriel García Márquez’s One Hundred Years of Solitude—I demonstrate that human understanding operates through complex semiotic processes irreducible to pattern-matching and statistical prediction. The article proposes concrete interventions to transform research engines from tools of semantic extraction into technologies that preserve and enhance interpretive richness, arguing that ethnographic and literary methodologies offer essential correctives to the epistemological impoverishment inherent in current AI architectures. Full article

This paper will discuss the depiction of trans women in the films Lovely Man and Midnight Swan, comparing the two main characters, Ipuy and Nagisa. Discrimination against transgender individuals persists in contemporary society, particularly in patriarchal Asian countries such as Indonesia and Japan. The films Lovely Man and Midnight Swan are of Indonesian and Japanese origin, respectively. A close analysis of both films reveals that they feature protagonists who embody the exact symbolic representations as entertainers, parents, sexual objects, and pariahs. According to Peirce’s semiotics, these four symbolic representations are determined. A thorough examination of the cinematic expressive movements in both films reveals four metaphorical expressions: the trans woman as an entertainer, the trans woman as a parent, the trans woman as a sexual object, and the trans woman as a pariah. These four metaphorical expressions are validated through extralinguistic references, reflecting socio-cultural realities in Indonesian and Japanese societies. They become arguments through the interpretation of both films. The similarities between the two films can be understood as a social critique of the issues facing trans women in Indonesia and Japan, who require greater attention to their human rights to survive in society. Full article

To implement predictive maintenance of units in the practice of metallurgical manufacturers, computer information and diagnostic systems are being developed to assess the current state of individual units throughout their entire life cycle. This publication presents the results of a study on developing an infrared diagnostic system for predictive maintenance of converter units in the non-ferrous metallurgy industry. A 3D mathematical model of the transient heat transfer in the wall of a real operating unit has been developed and numerically implemented to study, analyze, and diagnose surface temperature fields resulting from wear and local damage. To adjust the operation of the mathematical model, the design parameters and the results for operating and technological parameters from an industrial experiment are taken into consideration. Using the model, a full-factor experiment was simulated to study the surface temperature fields resulting from the erosion wear of the wall and the presence of local damage. Based on the simulation results, the optimal time range for thermographic monitoring is determined. A regression dependence was derived to predict the refractory wall wear as a function of the outer surface temperature of the converter unit. The results are part of a comprehensive investigation aimed at developing thermal imaging techniques for converter units in non-ferrous metallurgy. Full article

The precise estimation of precipitation is key to understanding and mitigating the effects of extreme weather conditions, especially in areas susceptible to Mediterranean cyclones. This work assesses the performance of the integrated multi-satellite retrievals for GPM (IMERG) precipitation products during the extreme Mediterranean cyclone “Medicane Daniel” that affected the Thessaly Plain in Central Greece in early September 2023. Three IMERG versions (final run (FR), early run (ER), and late run (LR)) were inter-compared with gauge-based interpolated rainfall estimates using inverse distance weighting (IDW) and ordinary kriging techniques. Pixel-wise and categorical verification metrics, such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS), were calculated for rainfall thresholds between 50 mm and 400 mm. It was found that the IMERG final run agreed most with the ground observations, with a correlation coefficient (R) of 0.87, RMSE of 138.8 mm, and CSI up to 0.995 at the 100 mm threshold when the IDW interpolation was used. Kriging produced slightly better spatial accuracy overall, as indicated by a lower RMSE (14.5 mm) and higher correlation (R = 0.99). The results indicate the benefit of combining satellite precipitation data with ground-based observations through spatial interpolation for the enhanced monitoring of extreme weather events over complex terrain. Kriging is suggested when greater spatial reliability is needed, while IMERG-FR is found to be a reliable satellite product for quick response analysis during heavy precipitation events. The study emphasizes the importance of blending satellite precipitation estimates and ground observations via spatial interpolation methods, i.e., kriging and IDW, allowing for a more localized and precise validation of intense weather events. Full article

This paper proposes a theoretical model of meaning-making grounded in proprioceptive awareness and embodied imagination, arguing that human cognition is inherently multimodal, anticipatory, and sensorimotor. Drawing on Peircean semiotics, Lotman’s model of cultural cognition, and current research in neuroscience, we show that readiness to act—a proprioceptively grounded anticipation of movement—plays a fundamental role in the emergence of meaning, from perception to symbolic abstraction. Contrary to traditional approaches that reduce language to a purely symbolic or visual system, we argue that meaning arises through the integration of sensory, motor, and affective processes, structured by axial proprioceptive coordinates (vertical, horizontal, sagittal). Using Peirce’s triadic model of interpretants, we identify proprioception as the modulatory interface between sensory stimuli, emotional response, and logical reasoning. A study on skilled pianists supports this view, showing that mental rehearsal without physical execution improves performance via motor anticipation. We define this process as proprioceptive resonance, a dynamic synchronization of embodied states that enables communication, language acquisition, and social intelligence. This framework allows for a critique of linguistic abstraction and contributes to ongoing debates in semiotics, enactive cognition, and the origin of syntax, challenging the assumption that symbolic thought precedes embodied experience. Full article

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R² assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R², and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m³/s. The test results were 0.681, 0.91, 0.828, and 41.237 m³/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article

One recurrent criticism of the Madhyamaka doctrine of emptiness (śūnyatā) is its equation with a potential axiological nihilism that undermines, inter alia, the telos of Buddhist practice. Here, I speculate that Madhyamaka non-foundationalism could be compatible with the naturalized teleology of C.S. Peirce. In brief, Peirce argues on pragmatic grounds that the ‘final cause’ of events does not refer to a predetermined finis ultimis or summum bonum with any ‘intrinsic nature’ (‘svabhāva’). Rather, a final cause is a general continuum of lawfulness (‘Third’/future) that mediates between indeterminate possibility (‘First’/present) and determinate actuality (‘Second’/past). Therefore, while a continuum of ‘purposiveness’ is a rational precondition for all temporal events, its futural significance means it can only ever be asymptotically realized; indeed, the constitutively general form of each ‘final’ cause is, practically speaking, fundamentally vague and open-ended to some degree. Finally, I show that the so-called strange attractors of dynamical systems theory provide an imperfect model for this naturalized ‘groundless teleology’. Full article

Gold is a significant revenue source for custom copper smelters facing profitability challenges due to low treatment and refining charges, stricter regulations, and rising costs. Gold is also often blended with copper concentrates, but precise recovery rates from smelting processes are poorly documented despite gold critical economic importance. This paper aims to provide the first comprehensive estimates of gold first-pass recovery across various operational units within the copper sulfide concentrate processing flowsheet. It evaluates the effectiveness of different copper smelting and converting technologies in recovering gold. Optimizing gold first-pass recovery is especially important to enhance immediate financial returns and responsiveness to market dynamics, allowing companies to capitalize on favorable gold prices without delays. Given the absence of direct measurements for gold recovery rates, this research develops an estimation method based on understanding gold loss mechanisms during smelting. This study identifies and analyzes key input and output parameters by examining data from various copper producers. By correlating these parameters with gold loss, the research estimates gold first-pass recovery rates within the copper smelting process. Among integrated smelting-converting routes, the flash smelting to Peirce–Smith converting route achieves the highest gold first-pass recovery (98.8–99.5%), followed by the Mitsubishi continuous smelting and converting process (94.3–99.8%), bottom-blowing smelting to bottom-blowing converting (95.8%), flash smelting to flash converting (95.5%), Teniente smelting to Peirce–Smith converting (95.2%), and the Noranda continuous smelting and converting process (94.8%). The final recovery rates are expected to be higher considering the by-products’ internal recirculation and post-processing within the copper flow sheet. Additionally, superior gold recoveries are attributed to advanced metallurgical practices and control systems, which vary even among companies with similar technologies. This research demonstrates that copper smelting can effectively recover over 99% of gold from sulfide concentrates. Gold accumulates up to 1000 times its original concentration in anode slime during electrolytic refining, generating 5–10 kg of slime per ton of copper, which is further processed to recover gold and other by-products. Major smelters operate precious metal plants where recovering gold from highly concentrated anode slime is both cost-effective and efficient. Full article

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m⁻² day⁻¹), ANHP increased by 9.39 kg ha⁻¹, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha⁻¹ more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C₃ mid-grasses in taller structured ecosites compared to the C₄ short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R² values varied by ecosite ranging from 0.49 to 0.67, while RAP R² values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP. Full article