Search Results (361)

Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require the flexibility of frequencies and amplitudes. We developed a custom vibrational system that is equipped with flexible frequency and amplitude, adapted to a custom graphical user interface (GUI). Based on the user’s criteria, the proposed vibrational system enables a wide range of frequencies and amplitudes that can be swept under a single platform. In addition, the system uses small linear actuators that are wearable and attach to the subject without the need for restrictive straps. The vibrational system was used to model trunk proprioceptive impairment associated with low back pain. Low back pain is the leading cause of disability worldwide. It is mostly associated with impaired postural control of the trunk. For postural control, the somatosensory system transmits proprioceptive (position sense) information from the sensors in the skin, joints, muscles, and tendons. Proprioceptive studies on trunk muscles have been conducted where the application of vibration at a set amplitude and frequency across all participants resulted in altered proprioception and a kinesthetic illusion, but not in all individuals. To assess the feasibility of the system, we manipulated the trunk proprioception of five subjects, demonstrating that the vibrational system is capable of modulating trunk proprioception and the value of customizing parameters of the system to obtain maximal deficits from individual subjects. Full article

Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies in text-based LLMs. The taxonomy organizes over 300 studies into six principled categories: Training and Learning Approaches, Architectural Modifications, Input/Prompt Optimization, Post-Generation Quality Control, Interpretability and Diagnostic Methods, and Agent-Based Orchestration. Beyond mapping the field, we identify persistent challenges such as the absence of standardized evaluation benchmarks, attribution difficulties in multi-method systems, and the fragility of retrieval-based methods when sources are noisy or outdated. We also highlight emerging directions, including knowledge-grounded fine-tuning and hybrid retrieval–generation pipelines integrated with self-reflective reasoning agents. This taxonomy provides a methodological framework for advancing reliable, context-sensitive LLM deployment in high-stakes domains such as healthcare, law, and defense. Full article

This study explores the critical yet often misunderstood distinction between risk and uncertainty. The research examines how knowledge and expertise can contribute to an illusion of control in uncertain environments, leading decision-makers to misclassify uncertainty as risk. This misclassification can lead to inadequate management of unforeseen events and suboptimal decision-making outcomes. The study introduces a novel matrix framework that categorises decision-making environments into four distinct quadrants based on knowledge, expertise, risk, and uncertainty. The framework helps decision-makers navigate the trade-off between risk and uncertainty, guiding them in assessing their current position and informing their decisions. Key findings reveal that expertise, while essential, can lead decision-makers to treat uncertainty as risk. The matrix offers guidance on how to better manage risk and uncertainty. Full article

Can brightness illusions modulate ocular accommodation? Previous studies have shown that brightness illusions can influence pupil size as if caused by actual luminance increases. However, their effects on other ocular responses—such as accommodative or focusing dynamics—remain largely unexplored. This study investigates the influence of brightness illusions, under two ambient lighting conditions, on accommodative and pupillary dynamics (physiological responses), and on perceived brightness and visual comfort (subjective responses). Thirty-two young adults with healthy vision viewed four stimulus types (blue bright and non-bright, yellow bright and non-bright) under low- and high-contrast ambient lighting while ocular responses were recorded using a WAM-5500 open-field autorefractor. Brightness and comfort were rated after each session. The results showed that high ambient contrast (mesopic) and brightness illusions increased accommodative variability, while yellow stimuli elicited a greater lag under photopic condition. Pupil size decreased only under mesopic lighting. Perceived brightness was enhanced by brightness illusions and blue color, whereas visual comfort decreased for bright illusions, especially under low light. These findings suggest that ambient lighting and visual stimulus properties modulate both physiological and subjective responses, highlighting the need for dynamic accommodative assessment and visually ergonomic display design to reduce visual fatigue during digital device use. Full article

Our understanding of the molecular mechanisms by which DNA meiotic recombination occurs has significantly increased in the past decades. A more representative molecular model has also undergone repeated revisions and upgrades with the continuous expansion of experimental data. Considering several apparent issues in the field, we intend to make necessary upgrades to previous models and reanalyze those data, exploring structural details and molecular mechanisms of DNA meiotic recombination. Eligible studies were identified from PubMed/Medline (up to June 2024). Key related publications and experimental data were retrieved from eligible studies, displaying five major issues. Meanwhile, the biophysical modeling method was used to establish an enlacement model. Then, the model was used to wholly reanalyze the collected data. An updated molecular model was supplemented. In the current model, a copy choice mechanism can initiate DNA meiotic recombination. The copy choice is based on a branched structure of DNA, which results from relative motion between homologous single strands. The reanalysis of previous experimental data based on this model can lead to new interpretations that can better address the discrepancies between previous experimental observations and theoretical models, including (1) the intertwinement model having embodied the particular characteristics of the SDSA model; (2) hDNA arising from JM resolution rather than being followed by a JM; (3) strand specificity of hDNA mismatch repair seeming to be an illusion and copy choice more likely to be the actual state; (4) parity in resolution patterns of a dHJ leading to parity of gene conversion; (5) the cooperation of multiple HJs readily generating a high correlation between gene conversion and crossover; and (6) transpositional recombination and site-specific recombination seeming to have a common pathway to meiotic recombination. The results indicate that both revisions and reanalysis are necessary. The novel interpretations would be critical to the understanding of the mechanisms of DNA recombination as well as its role in DNA repair. Additionally, the work could have implications for how the field views the importance of factors such as Spo11 or the mechanisms that drive meiotic pairing. Full article

Accurate calculation of embodied carbon emissions in buildings (ECE) is crucial to achieving global carbon neutrality. However, fragmented data, inconsistent regional standards, and low computational efficiency have long hindered existing methods. This study innovatively integrates large language models (LLMs) with retrieval-enhanced generation (RAG) technology to establish a new intelligent accounting paradigm for embodied carbon in buildings. Through a systematic evaluation of three basic models—Kimi, Doubao, and DeepSeek-R1—in a five-level progressive input scenario, the study quantitatively reveals the “information sensitivity” patterns of LLMs. To address the illusion errors of general models in professional scenarios, an innovative three-stage closed-loop architecture of “knowledge retrieval—calculation embedding—trustworthy generation” is proposed. By dynamically invoking domain knowledge bases and embedded computing modules, zero-error verification of benchmark data is achieved. The core contributions include the following: (1) It has been clarified that the basic large model has application potential in calculating the implicit carbon emissions of buildings, but the reliability of the results is limited. (2) The influence of data elements on calculation accuracy is revealed. (3) The application path for integrating RAG with large models has been pioneered, and the results show that the RAG technology can enhance the performance of large models in calculating the implicit carbon emissions of buildings by approximately 25%. (4) The significant efficiency improvement of RAG technology is verified. (5) A supporting theoretical and application system is established. Full article

Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, particularly data leakage, systematically inflates performance metrics, and examines the broader landscape of validation practices that impact clinical readiness. Methods: We conducted a scoping review following PRISMA-ScR guidelines, with protocol pre-registered in the Open Science Framework (OSF osf.io/2s6e9). We searched PubMed, Scopus, and CINAHL databases through May 2025 for studies employing deep learning for AD diagnosis. We developed a novel three-tier risk stratification framework to assess data leakage potential and systematically extracted data on validation practices, interpretability methods, and performance metrics. Results: From 2368 identified records, 44 studies met inclusion criteria, with 90.9% published between 2020–2023. We identified a striking inverse relationship between methodological rigor and reported accuracy. Studies with confirmed subject-wise data splitting reported accuracies of 66–90%, while those with high data leakage risk claimed 95–99% accuracy. Direct comparison within a single study demonstrated a 28-percentage point accuracy drop (from 94% to 66%) when proper validation was implemented. Only 15.9% of studies performed external validation, and 79.5% failed to control for confounders. While interpretability methods like Gradient-weighted Class Activation Mapping (Grad-CAM) were used in 18.2% of studies, clinical validation of these explanations remained largely absent. Encouragingly, high-risk methodologies decreased from 66.7% (2016–2019) to 9.5% (2022–2023). Conclusions: Data leakage and associated methodological flaws create a pervasive illusion of near-perfect performance in AD deep-learning research. True accuracy ranges from 66–90% when properly validated—comparable to existing clinical methods but far from revolutionary. The disconnect between technical implementation of interpretability methods and their clinical validation represents an additional barrier. These findings reveal fundamental challenges that must be addressed through adoption of a “methodological triad”: proper data splitting, external validation, and confounder control. Full article

This paper aims to explore whether astrophysical observations, primarily galaxy rotation curves, result from covarying coupling constants (CCC) rather than from dark matter. We have shown in earlier papers that cosmological observations, such as supernovae type 1a (Pantheon+), the small size of galaxies at cosmic dawn, baryon acoustic oscillations (BAO), the sound horizon in the cosmic microwave background (CMB), and time dilation effect, can be easily accounted for without requiring dark energy and dark matter when coupling constants are permitted to evolve in an expanding Universe, as predicted by Dirac, and the redshift is considered jointly due to the Universe’s expansion and Zwicky’s tired light (TL) effect. Here, we show that the CCC parameter α is responsible for generating the illusion of dark matter and dark energy, which we call α-matter and α-energy, and is influenced by the baryonic matter density distribution. While cosmologically α is a constant determined for the homogenous and isotropic Universe, e.g., by fitting Pantheon+ data, it can vary locally due to the extreme anisotropy of the matter distribution. Thus, in high baryonic density regions, one expects α-matter and α-energy densities to be relatively low and vice versa. We present its application to a few galaxy rotation curves from the SPARC database and find the results promising. Full article

The current study aimed to investigate the impact of externally evoked mental workload on the level of an artificial hand ownership sensation, a component of the embodiment phenomenon (feeling an external object, in this case a fake upper limb, as part of one’s body). The process of embodiment is extensively investigated in the literature also to find solutions for promoting the acceptance of prosthetic limbs. Before a traditional procedure for summoning in healthy subjects a Rubber Hand Illusion (RHI), the participants performed memory-related tasks in easy or demanding conditions to generate, respectively, low and high mental workloads. Alongside the behavioral correlates of the body ownership in the form of a proprioceptive drift (the measure of the correspondence between the perceived position of the actual limb and the fake one), EEG data was also collected. The results, both behavioral and neural, suggest that a high mental workload before the RHI experience leads to a low level of body ownership, whereas a low one enhances it. This can be interpreted as a consequence of distracting mental resources (possibly a specific type of them) from the embodiment stimulation session. Full article

Surrogate models are widely recognized as effective, data-driven predictive tools for storm surge risk assessment. For such applications, surrogate models (referenced also as emulators or metamodels) are typically developed using existing databases of synthetic storm simulations, and once calibrated can provide fast-to-compute approximations of the storm surge for a variety of downstream analyses. The storm surge predictions need to be established for different geographic locations of interest, typically corresponding to the computational nodes of the original numerical model. A number of inland nodes will remain dry for some of the database storm scenarios, requiring an imputation for them to estimate the so-called pseudo-surge in support of the surrogate model development. Past work has examined the adoption of kNN (k-nearest neighbor) spatial interpolation for this imputation. The enhancement of kNN with hydraulic connectivity information, using the grid or mesh of the original numerical model, was also previously considered. In this enhancement, neighboring nodes are considered connected only if they are connected within the grid. This work revisits the imputation of peak storm surge within a surrogate modeling context and examines three distinct advancements. First, a response-based correlation concept is considered for the hydraulic connectivity, replacing the previous notion of connectivity using the numerical model grid. Second, a Gaussian Process interpolation (GPI) is examined as alternative spatial imputation strategy, integrating a recently established adaptive covariance tapering scheme for accommodating an efficient implementation for large datasets (large number of nodes). Third, a data completion approach is examined for imputation, treating dry instances as missing data and establishing imputation using probabilistic principal component analysis (PPCA). The combination of spatial imputation with PPCA is also examined. In this instance, spatial imputation is first deployed, followed by PPCA for the nodes that were misclassified in the first stage. Misclassification corresponds to the instances for which imputation provides surge estimates higher than ground elevation, creating the illusion that the node is inundated even though the original predictions correspond to the node being dry. In the illustrative case study, different imputation variants established based on the aforementioned advancements are compared, with comparison metrics corresponding to the predictive accuracy of the surrogate models developed using the imputed databases. Results show that incorporating hydraulic connectivity based on response similarity into kNN enhances the predictive performance, that GPI provides a competitive (to kNN) spatial interpolation approach, and that the combination of data completion and spatial interpolation emerges as the recommended approach. Full article

In general, Slovene clitics occur in the second, so-called Wackernagel position of the clause. However, Slovene is exceptional among Wackernagel languages in that the clitic cluster may also occupy the clause-initial position. Imperative sentences have been argued to form an exception to this exception, again allowing the clitic cluster only in the second position. In this paper, we present corpus data that speaks against this second-order exception. We categorize the imperative clauses containing initial clitic clusters found in the corpora into three classes: modally subordinated imperatives, imperatives containing the adversative or the concessive particle, and imperatives occuring as a step in an instruction. We argue that all three classes involve a covert anaphoric element residing in the clause-initial position, yielding an illusion of a clause-initial clitic cluster. In conclusion, initial clitic clusters in Slovene imperatives are not ungrammatical but merely uncommon, and their distribution is ultimately governed by the discourse. We also make a theoretical point, emphasizing that the presented analysis offers support to the view that all discursive information must be represented in syntax. Full article

The principles of least effort and the illusion of control may influence the decision-making process. It is challenging for a decision-maker to maintain complete independence when assessing the membership and non-membership degrees of indicators. However, existing neutrosophic sets and q-rung orthopair fuzzy sets assume full independence of such information. In view of this, this paper proposes a new neutrosophic set, namely the q-type semi-dependent neutrosophic set (QTSDNS), based on the classical neutrosophic set, whose membership and non-membership degrees are interrelated. QTSDNS is a generalized form of classical semi-dependent fuzzy sets, such as the intuitionistic neutrosophic set. It contains a regulatory parameter, which allows for decision-makers to flexibly adjust the model. Furthermore, a multi-attribute group decision-making (MAGDM) algorithm is proposed by integrating QTSDNS with evidence theory to solve the supplier selection problem. The algorithm first utilizes QTSDNS to represent the preference information of experts, then employs the q-TSDNWAA (or q-TSDNWGA) operator to aggregate the evaluation information of individual experts. Following the analysis of the mathematical relationship between QTSDNS and evidence theory, evidence theory is used to aggregate the evidence from each expert to obtain the group trust interval. Then, the best supplier is determined using interval number ranking methods. Finally, a numerical example is provided to demonstrate the feasibility of the proposed method. Full article

The scientific study of emotion remains fraught with conceptual ambiguity, methodological limitations, and epistemological blind spots. This theoretical paper argues that existing paradigms frequently capture synthetic rather than natural emotional states—those shaped by social expectations, cognitive scripting, and performance under observation. We propose a conceptual framework that distinguishes natural emotion—spontaneous, embodied, and interoceptively grounded—from synthetic forms that are adaptive, context-driven, and often unconsciously rehearsed. These reactions often involve emotional scripts rather than genuine, spontaneous affective experiences. Drawing on insights from affective neuroscience, psychological measurement, artificial intelligence, and neurodiversity, we examine how widely used tools such as EEG, polygraphy, and self-report instruments may capture emotional conformity rather than authenticity. We further explore how affective AI systems trained on socially filtered datasets risk replicating emotional performance rather than emotional truth. By recognizing neurodivergent expression as a potential site of emotional transparency, we challenge dominant models of emotional normalcy and propose a five-step agenda for reorienting emotion research toward authenticity, ecological validity, and inclusivity. This post-synthetic framework invites a redefinition of emotion that is conceptually rigorous, methodologically nuanced, and ethically inclusive of human affective diversity. Full article

Some of Spain’s greatest humanists—Juan Luis Vives, Antonio de Nebrija, Juan de Ávila, Luis de León, and Benito Arias Montano—were from a converso background. Recent scholarship suggests that two of the three most influential religious movements in sixteenth-century Spain—Juan de Ávila’s evangelical movement and Teresa of Ávila’s Barefoot Carmelites—were founded by conversos and presented converso membership, whose winds of religious innovation to tame Christian Orthodoxy and Counter-Reformation Spanish society, through the influence of Italian Humanism and reform, prioritized spiritual practice, social toleration, and religious concord. Indeed, Santa Teresa de Ávila, a major innovator within the Spanish Church, was herself from a converso family with Jewish ancestry. She became a key female theologist who transcended as an identity marker of the Spanish Baroque, conceived as quintessential of the Spanish Golden Age. Coopted in different periods, she “reappeared” in the 1930s as Patron of the Sección Femenina de la Falange y de las JONS, the women’s branch of the new radical right, turning into a role model of femininity for highly conservative religious women. Consecrated as “Santa de la Raza”, she became the undisputable womanized icon of the so-called “Spanish Crusade”, the slogan which General F. Franco implemented, with the approval of the Spanish Catholic Church, to re-cast in a pseudo-theological narrative the rebellion against the Spanish Second Republic in July 1936. This article examines different appropriations of the figure of Teresa de Ávila as a pillar of “Hispanidad”, in the last centuries within the changing sociopolitical contexts and theological debates in which this instrumentalization appeared. By highlighting the plasticity of this converso figure, the article suggests possible lines of research regarding the Jewish origins of some national icons in Spain. Full article

Various parameters and observation conditions contribute to the emergence of color. This phenomenon poses a challenge in modern visual communication systems, which are continuously being enhanced through new insights gained from research into specific psychophysical effects. One such effect is the psychophysical phenomenon of simultaneous contrast. Nearly 90 years ago, Kurt Koffka described one of the earliest illusions related to simultaneous contrast. This study examined the perception of gray tone variations in the Koffka ring against different background color combinations (red, blue, green) displayed on a computer screen. The intensity of the effect was measured using lightness difference ΔL₀₀ across light-, medium-, and dark-gray tones. The results were analyzed using descriptive statistics, while statistically significant differences were determined using the Friedman ANOVA and post hoc Wilcox tests. The strongest visual effect was observed the for dark-gray tones of the Koffka ring on blue/green and red/green backgrounds, indicating that perceptual organization and spatial parameters influence the illusion’s magnitude. The findings suggest important implications for digital media design, where understanding these effects can help avoid unintended color tone distortions caused by simultaneous contrast. Full article