Search Results (418)

Background: Temporomandibular disorders are a generic term referred to clinical conditions involving the jaw muscles and temporomandibular joint with multifactorial pattern and genetic background. The aim of this observational study was to investigate the correlation between craniomandibular disorders and the presence of occlusal alterations. A clinical evaluation of the occlusal and articular status of the patients was carried out, integrating the latter with the electromyographic recording the activity of the masseter and temporalis muscles. Methods: A clinical observational study on 20 adults assessed temporomandibular disorders using DC/TMD criteria, anamnesis, clinical exams, occlusal and electromyographic analyses. Occlusion was evaluated morphologically and functionally. Electromyography tested static/dynamic muscle activity. Data were statistically analyzed using t-tests and Pearson correlation (p < 0.05). Results: Electromyographic analysis revealed significant differences between subjects with and without visual correction, suggesting that visual input influences masticatory muscle activity. Correlations emerged between occlusal asymmetries and neuromuscular parameters. These findings highlight clinical implications for mandibular function, muscle symmetry, and the potential for therapeutic rebalancing through targeted interventions. Conclusions: The study demonstrates a significant correlation between visual–motor integration and masticatory muscle efficiency. It emphasizes lateralized neuromuscular activation’s influence on occlusal contact distribution. Moreover, it identifies mandibular torsion–endfeel inverse correlation as a potential diagnostic marker for craniomandibular dysfunctions via surface electromyography. Full article

A real-space renormalization group (RG) framework is formulated for classical SO(n) spin models defined on d-dimensional crystal lattices composed of corner-sharing hyper-tetrahedra, a class of geometrically frustrated crystal structures. This includes, as specific instances, the classical Heisenberg model on the kagome and pyrochlore crystals. The approach involves computing the partition function and corresponding order parameters for spin clusters embedded in the crystal, to leading order in symmetry-breaking fields generated by surrounding spins. The crystal geometry plays a central role in determining the scaling relations and the associated critical behavior. To illustrate the efficacy of the method, a reduced manifold of symmetry-allowed ordered states for isotropic nearest-neighbor interactions is analyzed. The RG flow systematically excludes the emergence of a $\overrightarrow{q}=\mathbf{0}$ ordered phase within the antiferromagnetic sector, independently of both the spatial dimensionality of the crystal and the number of spin components. Extensions to incorporate more elaborate crystal-symmetry-induced ordering patterns and fluctuation-driven phenomena—such as order-by-disorder—are also discussed. Full article

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

This study proposes an application framework based on Large Language Models (LLMs) to analyze multimodal heterogeneous data in the power sector and introduces the CLB-BER model for classifying user electricity consumption behavior. We first employ the Euclidean–Cosine Dynamic Windowing (ECDW) method to optimize the adjustment phase of the CLUBS clustering algorithm, improving the classification accuracy of electricity consumption patterns and establishing a mapping between unlabeled behavioral features and user types. To overcome the limitations of traditional clustering algorithms in recognizing emerging consumption patterns, we fine-tune a pre-trained DistilBERT model and integrate it with a Softmax layer to enhance classification performance. The experimental results on real-world power grid data demonstrate that the CLB-BER model significantly outperforms conventional algorithms in terms of classification efficiency and accuracy, achieving 94.21% accuracy and an F1 score of 94.34%, compared to 92.13% accuracy for Transformer and lower accuracy for baselines like KNN (81.45%) and SVM (86.73%); additionally, the Improved-C clustering achieves a silhouette index of 0.63, surpassing CLUBS (0.62) and K-means (0.55), underscoring its potential for power grid analysis and user behavior understanding. Our framework inherently preserves temporal symmetry in consumption patterns through dynamic sequence alignment, enhancing its robustness for real-world applications. Full article

Recently, a new class of magnetic material, termed altermagnets, has caught the attention of the magnetism and spintronics community. The magnetic phenomenon arising from these materials differs from traditional ferromagnetism and antiferromagnetism. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties, such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism and prospects for exploiting its unique properties in next-generation devices. Full article

Upper extremity amputations significantly impact an individual’s physical capabilities, psychosocial well-being, and overall quality of life. The level at which an amputation is performed influences residual limb function, prosthetic compatibility, and long-term patient satisfaction. While surgical guidelines traditionally emphasize maximal limb preservation, prosthetists often advocate for amputation sites that optimize prosthetic fit and function, highlighting the need for a collaborative approach. This review examines the discrepancies between surgical and prosthetic recommendations for optimal amputation levels, from digit amputations to shoulder disarticulations, and explores their implications for prosthetic design, functionality, and patient outcomes. Various prosthetic options, including passive functional, body-powered, myoelectric, and hybrid devices, offer distinct advantages and limitations based on the level of amputation. Prosthetists emphasize the importance of residual limb length, not only for mechanical efficiency but also for achieving symmetry with the contralateral limb, minimizing discomfort, and enhancing control. Additionally, emerging technologies such as targeted muscle reinnervation (TMR) and advanced myoelectric prostheses are reshaping rehabilitation strategies, further underscoring the need for precise amputation planning. By integrating insights from both surgical and prosthetic perspectives, this review highlights the necessity of a multidisciplinary approach involving surgeons, prosthetists, rehabilitation specialists, and patients in the decision-making process. A greater emphasis on preoperative planning and interprofessional collaboration can improve prosthetic outcomes, reduce device rejection rates, and ultimately enhance the functional independence and well-being of individuals with upper extremity amputations. Full article

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in subjective model selection. This study introduces a Monte Carlo-based evaluation framework that integrates data-driven simulation with geotechnical principles, embedding the concept of symmetry across both modeling and assessment stages. Equivalent permeability coefficients (EPCs) are used to normalize soil consolidation behavior, enabling the generation of a large, statistically robust dataset. Four empirical settlement prediction models—Hyperbolic, Exponential, Asaoka, and Hoshino—are systematically analyzed for sensitivity to temporal features and resistance to stochastic noise. A symmetry-aware comprehensive evaluation index (CEI), constructed via a robust entropy weight method (REWM), balances multiple performance metrics to ensure objective comparison. Results reveal that while settlement behavior evolves asymmetrically with respect to EPCs over time, a symmetrical structure emerges in model suitability across distinct EPC intervals: the Asaoka method performs best under low-permeability conditions (EPC ≤ 0.03 m/d), Hoshino excels in intermediate ranges (0.03 < EPC ≤ 0.7 m/d), and the Exponential model dominates in highly permeable soils (EPC > 0.7 m/d). This framework not only quantifies model robustness under complex data conditions but also formalizes the notion of symmetrical applicability, offering a structured path toward intelligent, adaptive settlement prediction in HSR subgrade engineering. Full article

(1) Background: Human walking involves adapting to diverse terrains, influencing gait biomechanics. This study examined how seven outdoor surfaces—flat–even, banked-right/-left, cobblestone, grass, sloped-down, and sloped-up—affect nonlinear gait dynamics in 30 healthy adults (14 females and 15 males). (2) Methods: Trunk and shank accelerations were analyzed for movement predictability (sample entropy, SE), smoothness (log dimensionless jerk, LDLJ), symmetry (step/stride regularity), and stability (short-/long-term Lyapunov exponents, LyE_s, LyE_l). (3) Results: Surface type significantly influenced all gait metrics, regardless of sex. Banked-right and sloped-down walking reduced SE, indicating less predictable movements. All surfaces except flat–even increased LDLJ, suggesting reduced smoothness. Cobblestone and sloped-down surfaces impaired step symmetry, while banked surfaces enhanced stride symmetry. LyE_s decreased on cobblestones (lower variability), while sloped-up increased it. LyE_l rose on all surfaces except cobblestones, indicating a more chaotic gait. No significant sex differences were found, though males showed a non-significant trend toward lower LyE_s. Notably, sex–surface interactions emerged for SE and stride symmetry on banked-right surfaces, with females showing decreased SE and increased symmetry. (4) Conclusions: These findings underscore the importance of terrain and sex in gait dynamics research. Full article

Defect engineering has recently emerged as a cutting-edge discipline for precise modulation of electronic structures in nanomaterials, shifting the paradigm in nanoscience from passive ‘inherent defect tolerance’ to proactive ‘defect-controlled design’. The deliberate introduction of defect—including vacancies, dopants, and interfaces—breaks the rigid symmetry of crystalline lattices, enabling new pathways for optimizing catalysis performance. This review systematically summarizes the mechanisms underlying defect-mediated electronic structure at active sites regulation, including (1) reconstruction of the electronic density of states, (2) tuning of coordination microenvironments, (3) charge transfer and localization effects, (4) spin-state and magnetic coupling modulation, and (5) dynamic defect and interface engineering. These mechanisms elucidate how defect-induced electronic restructuring governs catalytic activity and selectivity. We further assess advanced characterization techniques and computational methodologies for probing defects-induced electronic states, offering deeper mechanistic insights at atomic scales. Finally, we highlight recent breakthroughs in defect-engineered nanomaterials for catalytic applications, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER) and beyond, while discussing existing challenges in scalability, defect stability, and structure–property causality. This review aims to provide actionable principles for the rational design of defects to tailor electronic structures toward next-generation energy technologies. Full article

The eye is one of our five sense organs, where optical and neural structures are integrated. It works in synchrony with the brain, enabling the formation of meaningful images. However, lack of function, complete absence or structural abnormalities of cone cells in the cone cells in the retina causes the emergence of types of Color Vision Deficiency (CVD). This deficiency is characterized by the lack of clear vision in the use of colors in the same region of the spectrum, and greatly affects the quality of life of the patient. Therefore, it is important to develop filters that enable colors to be combined successfully. In this study, an original filter design was improved, built on a five-stage systematic structure that complements and supports itself. But optimization regarding performance value needs to be tested with objective methods independent of human decision. Therefore, in order to provide performance analyses based on objective evaluation criteria, original and enhanced images simulated by patients with seven different Color Vision Deficiency (CVD) types were classified with the MobileNet transfer learning model. The classification results show that the developed final filter greatly improves the differences in color perception levels in both eyes. Thus, color stimulation between the two eyes is more balanced, and perceptual symmetry is created. With perceptual symmetry, environmental colors are perceived more consistently and distinguishably, and the visual difficulties encountered by color blind individuals in daily life are reduced. Full article

LK-99, with chemical formula Pb_10−xCu_x(PO₄)₆O, was recently reported to be a room-temperature superconductor. While this claim has met with little support in a flurry of ensuing work, a variety of calculations (mostly based on density-functional theory) have demonstrated that the system possesses some unusual characteristics in the electronic structure, in particular flat bands. We have established previously that within DFT, the system is insulating with many characteristics resembling the classic cuprates, provided the structure is not constrained to the P3(143) symmetry nominally assigned to it. Here we describe the basic electronic structure of LK-99 within self-consistent many-body perturbative approach, quasiparticle self-consistent GW (QSGW) approximation and their diagrammatic extensions. QSGW predicts that pristine LK-99 is indeed a Mott/charge transfer insulator, with a bandgap gap in excess of 3 eV, whether or not constrained to the P3(143) symmetry. When Pb₉Cu(PO₄)₆O is hole-doped, the valence bands modify only slightly, and a hole pocket appears. However, two solutions emerge: a high-moment solution with the Cu local moment aligned parallel to neighbors, and a low-moment solution with Cu aligned antiparallel to its environment. In the electron-doped case the conduction band structure changes significantly: states of mostly Pb character merge with the formerly dispersionless Cu d state, and high-spin and low spin solutions once again appear. Thus we conclude that with suitable doping, the ground state of the system is not adequately described by a band picture, and that strong correlations are likely. Irrespective of whether this system class hosts superconductivity or not, the transition of Pb₁₀(PO₄)₆O from being a band insulator to Pb₉Cu(PO₄)₆O, a Mott insulator, and multi-determinantal nature of doped Mott physics make this an extremely interesting case-study for strongly correlated many-body physics. Full article

We review recent developments in structural stability as applied to key topics in general relativity. For a nonlinear dynamical system arising from the Einstein equations by a symmetry reduction, bifurcation theory fully characterizes the set of all stable perturbations of the system, known as the ‘versal unfolding’. This construction yields a comprehensive classification of qualitatively distinct solutions and their metamorphoses into new topological forms, parametrized by the codimension of the bifurcation in each case. We illustrate these ideas through bifurcations in the simplest Friedmann models, the Oppenheimer-Snyder black hole, the evolution of causal geodesic congruences in cosmology and black hole spacetimes, crease flow on event horizons, and the Friedmann–Lemaître equations. Finally, we list open problems and briefly discuss emerging aspects such as partial differential equation stability of versal families, the general relativity landscape, and potential connections between gravitational versal unfoldings and those of the Maxwell, Dirac, and Schrödinger equations. Full article