Search Results (40)

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

Theological engagement with artificial intelligence has largely focused on applied ethics, addressing bias, governance, and labor displacement. While indispensable, this framing often presumes that algorithmic systems operate as external instruments acting upon already constituted subjects. This article argues that contemporary predictive architectures intervene at a deeper anthropological level by structuring attention, expectation, and habituation prior to deliberative judgment. It introduces the concept of ordo extractionis to designate a technologically mediated regime of formation characterized by behavioral trace extraction, probabilistic modeling, and recursive projection of statistically inferred continuity. Drawing on Augustine’s account of ordered love and temporality and Aquinas’s doctrine of habitus and the invisible mission of the Spirit, the article distinguishes algorithmic projection from sanctification as divergent pedagogies of temporal formation. Predictive systems stabilize continuity by extrapolating from measurable past behavior; sanctification reorders desire teleologically toward a final end not deducible from prior pattern and grounded in non-competitive divine causality. Algorithmic mediation is therefore interpreted pedagogically rather than metaphysically: it does not rival divine agency but participates creaturely in shaping the ecology within which habituation unfolds. Engagement with contemporary AI research on recommender systems, reinforcement learning, and generative models situates the argument within technological realism and resists determinism. The digital twin is analyzed as a probabilistic representation that acquires institutional authority when operationalized in ranking, profiling, and evaluative systems, without constituting a metaphysical competitor to the imago Dei. In response to anticipatory closure, Eucharistic anamnesis and epiclesis are developed as practices that re-situate memory and expectation within eschatological promise. The article concludes that the central theological question posed by AI is not whether machines can think, but how formative sovereignty over desire is exercised within technologically mediated modernity. Full article

The integration of Generative Artificial Intelligence (GenAI) into higher education represents a paradigm shift from static skill acquisition to dynamic, human–AI collaboration. However, the psychological mechanisms governing students’ adoption—specifically the interplay between pedagogical novelty, ethical awareness, and habit formation—remain underexplored. To address this, this study develops and implements a dynamic practical curriculum incorporating AI and ethical awareness, aiming to foster responsible behavioral patterns in computer programming education. Employing a quasi-experimental design, we implemented a 16-week dual-track instructional intervention (incorporating AI-integrated pedagogy and ethical scaffolding) for 148 computer science students. Structural Equation Modeling (SEM) was applied to test an extended UTAUT2 framework. The findings reveal three critical theoretical insights that redefine GenAI adoption: (1) The eclipse of utility: contrary to established models, traditional utilitarian drivers of performance expectancy (β = 0.076, p = 0.39) and effort expectancy (β = 0.125, p = 0.13) yielded non-significant effects on behavioral intention. This suggests that for digital natives, algorithmic efficiency has devolved into a baseline hygiene factor, losing its motivational power. (2) The dominance of pedagogical novelty: hedonic motivation emerged as the paramount predictor of both habit (β = 0.457, p < 0.001) and behavioral intention (β = 0.336, p = 0.001). This confirms that adoption is driven by the situational interest and interactional novelty inherent in the human–AI partnership. (3) The cognitive brake mechanism: ethical awareness exhibited a divergent regulatory role. While it significantly legitimized conscious behavioral intention (β = 0.166, p = 0.011), it showed a non-significant, negative association with habit (β = −0.032, p = 0.653). This demonstrates that ethical reasoning functions as a cognitive brake (system 2) and actively disrupts the formation of mindless, automated dependency (system 1). These results provide empirical evidence for a dual regulation model of AI adoption and suggest that sustainable education requires leveraging pedagogical novelty to drive engagement while utilizing ethical awareness to prevent blind habituation. Full article

Background/Objectives: Knee osteoarthritis (KOA) is associated with pain, functional decline, and altered biomechanics. The Step-Up and Down-Forwards (StUD-F) task provides an ecologically relevant assessment of challenging movements. This study investigated neuromuscular activation and lower-limb kinematics of leading and trailing-limbs during the StUD-F in individuals with KOA. Methods: Forty participants with KOA (65.3 ± 7.68 years; 21M/19F; BMI 28.9 ± 4.52 kg/m²) completed a 25 cm box StUD-F task. Surface electromyograph recorded bilateral activation of the vastus medialis (VM), vastus lateralis (VL), bicep femoris (BF), and semitendinosus (ST). Triplanar hip, knee, and ankle joint angles were estimated using inertial measurement units. StUD-F events (initial stance; step contact; ascent completion; descent preparation; step-down touchdown; and descent completion) were identified using custom algorithms. Pain was assessed using visual analogue scales and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Limb differences were analysed for leading or trailing roles using paired samples t-tests or non-parametric equivalents; waveforms were visually inspected. Results: Distinct neuromuscular and kinematic asymmetries were observed when affected and contralateral limbs were compared within each role (leading/trailing). During step-up, the affected leading limb demonstrated higher quadriceps activation at initial stance (VM: p = 0.035; VL: p = 0.027) and reduced trailing-limb activation at step contact (VM: p = 0.015; VL: p = 0.018), with sagittal-plane ankle differences (p = 0.004). During step-down, when the affected limb initiated ascent, trailing limb activation was higher at descent completion (VL: p < 0.001; VM: p = 0.003; BF: p = 0.009), with coronal-plane hip deviations (p < 0.001). When the contralateral limb-initiated ascent, trailing-limb muscles activation differences (VM: p < 0.001; VL: p = 0.015; BF: p = 0.007) and ankle/coronal-plane asymmetries (p ≤ 0.049) persisted. Conclusions: The StUD-F task elicits altered strategies in KOA, including elevated quadriceps–hamstring co-activation and altered sagittal/coronal alignment, and habitual limb choice across ascent and descent. These adaptations may enhance stability and joint protection but could increase medial compartment loading. The findings support rehabilitation focused on dynamic control, alignment, and shock absorption. Full article

Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments. Full article

Background/Objectives: Chrononutrition—the temporal organization of food intake relative to circadian rhythms—has emerged as an important factor in cardiometabolic health. While meal timing is typically analyzed as an isolated variable, limited research has examined integrated meal timing patterns, and no study has systematically compared clustering approaches for their identification. This cross-sectional study compared four clustering techniques—traditional (K-means, Hierarchical) and non-traditional (Gaussian Mixture Models (GMM), Spectral)—to identify meal timing patterns from habitual breakfast, lunch, and dinner times. Methods: The sample included 388 Mexican university students (72.8% female). Patterns were characterized using sociodemographic, anthropometric, food intake quality, and chronotype data. Clustering method concordance was assessed via Adjusted Rand Index (ARI). Results: We identified five patterns (Early, Early–Intermediate, Late–Intermediate, Late, and Late with early breakfast). No differences were observed in BMI, waist circumference, or age among clusters. Chronotype aligned with patterns (morning types overrepresented in early clusters). Food intake quality differed significantly, with more early eaters showing healthy intake than late eaters. Concordance across clustering methods was moderate (mean ARI = 0.376), with the highest agreement between the traditional and non-traditional techniques (Hierarchical–Spectral = 0.485 and K-means-GMM = 0.408). Conclusions: These findings suggest that, while traditional and non-traditional clustering techniques did not identify identical patterns, they identified similar core structures, supporting complementary pattern detection across algorithmic families. These results highlight the importance of comparing multiple methods and transparently reporting clustering approaches in chrononutrition research. Future studies should generate meal timing patterns in university students from other contexts and investigate whether these patterns are associated with eating patterns and cardiometabolic outcomes. Full article

Freezing of gait (FoG) is a common motor symptom in advanced Parkinson’s disease, leading to falls, disability, and reduced quality of life. Although cueing systems using visual or auditory stimuli can help patients resume walking, existing solutions are often expensive, uncomfortable, and conspicuous. ParCuR (Parkinson Cueing and Rehabilitation) is a compact, ankle-worn wearable integrating an inertial sensor, haptic stimulator, and AI-based software. It was developed to detect FoG episodes in real time and provides automatic sensory cues to assist patients with Parkinson’s Disease (PwP). A classifier was trained for FoG detection using the DAPHNet dataset, comparing patient-specific and patient-independent models. While a small-scale trial with PwP assessed usability and reliability. ParCuR is watch-sized (35 × 41 mm), discreet, and comfortable for daily use. The online detection algorithm triggers stimulation within 0.7 s of episode onset and achieves 94.9% sensitivity and 91.3% specificity using only 14 frequency-based features. Preliminary trials confirmed device feasibility and guided design refinements. This low-cost, wearable solution supports personalized, real-time FoG detection and responsive cueing, improving patient mobility while minimizing discomfort and continuous stimulation habituation. Full article

Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player in the non-invasive assessment of coronary artery disease (CAD). Advancements in CT technology, including iterative and deep-learning-based reconstruction, wide-area detectors, and dual-source systems, have helped mitigate early limitations, such as high radiation doses, motion artifacts, high iodine load, and non-diagnostic image quality. However, the adjustments between ionizing radiation and iodinated contrast material (CM) volumes remain a critical concern, especially due to the increasing use of CCTA in various indications. This review explores the balance between radiation and CM volumes, emphasizing patient-specific protocol optimization to improve diagnostic accuracy while minimizing risks. Radiation dose reduction strategies, such as low tube voltage protocols, prospective ECG-gating, and modern reconstruction algorithms, have significantly decreased radiation exposure, with some studies achieving sub-millisievert doses. Similarly, CM volume optimization, including adjustments in strategies for calculating CM volume, iodine concentration, and flow protocols, plays a role in managing risks such as contrast-associated acute kidney injury, particularly in patients with renal impairment. Emerging technologies, such as photon-counting CT and deep-learning reconstruction, promise further improvements in dose efficiency and image quality. This review summarizes current evidence, highlights the benefits and limitations of dose control approaches, and provides practical recommendations for practitioners. By tailoring protocols to patient characteristics, such as age, renal function, and body habitus, clinicians can achieve an optimal trade-off between diagnostic accuracy and patient safety, ensuring optimal operation of CT systems in clinical practice. Full article

Background and Objectives: Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients’ perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery. Methods: A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients’ experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses. Results: Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs. Conclusions: We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision. Full article

Background/Objectives: Individual habitual conditions entail a risk during the interpretation of vital parameters. We developed algorithms for calculating, validating, and interpreting individual normal ranges of body temperature and oxygen saturation. Methods: In total, 70 healthy individuals aged 27 to 80 and 52 frail individuals aged 60 to 100 were included. Data on individual conditions comprised age, gender, physical ability, chronic disease, and medication. Ear temperature and oxygen saturation were measured for five mornings before the participants got out of bed and consumed medicine, food, or drink. Results: The range for body temperature was 34.3 °C to 37.7 °C, with a variation of 0.7 °C ± 0.4 °C. The variation in minimum and maximum temperatures was 2.4 °C vs. 2.7 °C and 2.9 °C vs. 2.3 °C in healthy and frail subjects, respectively. The range for oxygen saturation was 85% to 99% in healthy individuals and 75% to 100% in frail individuals. The variation between minimum and maximum oxygen saturation was 13% vs. 25% and 4% vs. 17% in healthy and frail subjects, respectively. Conclusions: To promote the implementation of precision medicine in clinical practice, it is necessary to interpret body temperature and oxygen saturation based on individual habitual conditions. Interpreting deviations from an individual’s normal ranges allows healthcare professionals to provide necessary treatment without delay, which can be decisive in preventing further deterioration. Full article

Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the individual usage of different electrical devices. To address issues in load disaggregation models such as long training times, feature interference caused by the activation of other loads, and accuracy deficiencies caused by behavioral interference from users’ electricity usage habits, this paper proposes a VMD–Nyströmformer–BiTCN network architecture. The variational mode decomposition (VMD) filters the raw power data, reducing errors caused by noise and enhancing the accuracy of decomposing the load. A deep learning network utilizes a modified attention model, Nyströmformer, to reduce feature entanglement and accuracy degradation caused by habitual behavior interference during load disaggregation, while ensuring precise accuracy and improving network operational speed. The training network uses a bidirectional temporal convolutional network (BiTCN) and incorporates a residual network to expand the receptive field, allowing it to receive longer load sequence data and acquire more effective load information, thereby improving the disaggregation effectiveness for target appliances. Full article

The changing notion of “companion animals” and their increasing global status as family members underscores the dynamic interaction between gut microbiota and host health. This review provides a comprehensive understanding of the intricate microbial ecology within companion animals required to maintain overall health and prevent disease. Exploration of specific diseases and syndromes linked to gut microbiome alterations (dysbiosis), such as inflammatory bowel disease, obesity, and neurological conditions like epilepsy, are highlighted. In addition, this review provides an analysis of the various factors that impact the abundance of the gut microbiome like age, breed, habitual diet, and microbe-targeted interventions, such as probiotics. Detection methods including PCR-based algorithms, fluorescence in situ hybridisation, and 16S rRNA gene sequencing are reviewed, along with their limitations and the need for future advancements. Prospects for longitudinal investigations, functional dynamics exploration, and accurate identification of microbial signatures associated with specific health problems offer promising directions for future research. In summary, it is an attempt to provide a deeper insight into the orchestration of multiple microbial species shaping the health of companion animals and possible species-specific differences. Full article

We explore in this article how Evangelical and Pentecostal/Charismatic Churches (EPCCs) view Artificial Intelligence (AI), and how they use it, either intentionally or indirectly. Considering first the digital habitus in which EPCCs are immersed, we have documented and analyzed three sample cases showing how EPCCs use advanced AI tools to improve the sanctification process for believers; how the Bible can be translated, distributed, and its reading can be fostered around the world, using machine intelligence; and how a spiritual revival among EPCCs can spread rapidly through AI-mediated algorithms. We discuss the implications of these developments and conclude finally with some ideas about how EPCCs should engage AI applications in the future. Full article

Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the meteorologist Edward Lorenz (1963), we consider the expansion of the mathematics of chaos in this article, paying attention to topology, qualitative geometry, and Catastrophe Theory, on the one hand, and addressing the possibilities derived from the new Computer Science as Quantum Algorithms and the advances in Artificial Intelligence, on the other. We especially highlight the section on computing chaos, which we consider to be new calculation and analysis instruments, such as machine learning and its algorithm called reservoir computing, through which we can know the dynamics of a chaotic system. With past data, with equations like Karamoto–Sivashinsky, one can improve predictions of the system eight times further ahead than in previous methods. Integrating the machine learning approach and traditional model-based prediction, one could obtain accurate predictions twelve Lyapunov times. As we know, in the framework of chaos theory, it is habitually accepted that the idea of long-term prediction seems impossible because we live under a veil of uncertainty. But with technological advances, the landscape begins to change, both in chaos theory and in its applications, especially in the field of economics, to which we devote particular attention, carrying out as an example the analysis of the evolution of the Madrid Stock Exchange in the 2006–2013 crisis. Above all this, a reflection of a general nature is necessary to enlighten us on the possibility of opening a new horizon. Full article

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries. Full article