Search Results (186)

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

Background: Anterior cruciate ligament reconstruction (ACLr) often leads to asymmetries between limbs, with variable return-to-performance rates in athletes. The single-leg countermovement jump (SLCMJ) is commonly used to assess postoperative knee function. However, limited research has explored deficits specifically during the unweighting phase of the jump. Methods: This study assessed 53 recreational athletes (11 females, 42 males) between 6 and 9 months post-ACLr using a dual force plate system (1000 Hz). Each participant performed three maximal-effort SLCMJs per limb. Outcome measures included jump height, negative peak velocity, minimum force, and center of mass (COM) displacement. Paired t-tests and Wilcoxon tests were used to compare the ACLr limb with the contralateral limb. Results: Compared to the healthy limb, the ACLr limb showed significantly lower negative peak velocity (−0.80 ± 0.40 m/s vs. −0.94 ± 0.40 m/s, p < 0.001), higher minimum force (36.75 ± 17.88 kg vs. 32.05 ± 17.25 kg, p < 0.001), and reduced COM displacement (−17.62 ± 6.25 cm vs. −19.73 ± 5.34 cm, p = 0.014). Eccentric phase duration did not differ significantly. Conclusions: Athletes post-ACLr demonstrate altered neuromuscular control during the early SLCMJ phase. These findings highlight the importance of rehabilitation strategies targeting eccentric strength and symmetry restoration. Full article

Background and Clinical Significance: Juvenile ossifying fibroma (JOF) is a rare, benign, but locally aggressive fibro-osseous neoplasm that primarily affects the craniofacial skeleton of children and adolescents. Early surgical intervention is often required due to the lesion’s rapid growth and potential for significant facial deformity. Long-term functional and esthetic rehabilitation following maxillary resection in early childhood remains a clinical challenge. Case Presentation: This case reports a unique long-term follow-up of a 22-year-old female patient who underwent partial maxillary resection at the age of five due to JOF. Initial reconstructive efforts failed, necessitating a removable prosthesis to restore function and appearance. The patient experienced persistent self-consciousness and social withdrawal during adolescence, attributed to altered facial esthetics and repeated surgical disappointment. Nevertheless, prosthetic rehabilitation significantly improved mastication, phonetics, facial symmetry, and psychological well-being. Conclusions: The enduring psychosocial and functional impact of early maxillary resection for JOF and the pivotal role of prosthodontic management in long term rehabilitation are highlighted. A multidisciplinary approach that includes psychological support is suggested. This case report is among the few reports documenting long-term prosthetic outcomes for pediatric JOF patients extending into adulthood. Full article

There are numerous methods available for evaluating leg length discrepancy (LLD), ranging from classic clinical techniques to advanced systems based on sophisticated and expensive equipment, as well as rudimentary manual adjustment mechanisms for the prosthesis by specialists. However, unilateral amputee patients often face difficulties in accessing these solutions. They either lack the necessary equipment or do not have a medical specialist available to assist them in preventing postural imbalances. This study proposes the first smartphone-based computer vision system that evaluates and automatically compensates for leg length discrepancy in transtibial prostheses, offering a low-cost, accessible, and fully autonomous alternative to existing solutions. The method was tested using complex metrological systems. The application of the proposed method demonstrated its effectiveness in correcting simulated LLD for various values. Experimental validation demonstrated the system’s ability to restore symmetry in simulated LLD cases within the 1–10 mm range, achieving a relative compensation error of 2.44%. The proposed method for correcting LLD, based on computer vision and integrated into a smartphone, represents a significant advancement in restoring symmetry for unilaterally amputated patients. This technology could provide an accessible, efficient solution, thereby reducing the need for frequent prosthetist visits and enhancing user autonomy. Full article

We present novel proofs of the Riemann hypothesis by extending the standard complex Riemann zeta function into a quaternionic algebraic framework. Utilizing λ-regularization, we construct a symmetrized form that ensures analytic continuation and restores critical-line reflection symmetry, a key structural property of the Riemann ξ(s) function. This formulation reveals that all nontrivial zeros of the zeta function must lie along the critical line Re(s) = 1/2, offering a constructive and algebraic resolution to this fundamental conjecture. Our method is built on convexity and symmetrical principles that generalize naturally to higher-dimensional hypercomplex spaces. We also explore the broader implications of this framework in quantum statistical physics. In particular, the λ-regularized quaternionic zeta function governs thermodynamic properties and phase transitions in Bose–Einstein condensates. This quaternionic extension of the zeta function encodes oscillatory behavior and introduces critical hypersurfaces that serve as higher-dimensional analogues of the classical critical line. By linking the spectral features of the zeta function to measurable physical phenomena, our work uncovers a profound connection between analytic number theory, hypercomplex geometry, and quantum field theory, suggesting a unified structure underlying prime distributions and quantum coherence. Full article

Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired t-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (p > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (p < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. Full article

This study introduces a deep learning (DL)-based optimal condition monitoring and control system tailored to indoor smart greenhouses, with a novel focus on maintaining symmetry—defined as a dynamic equilibrium among temperature, humidity, and CO₂ levels—critical in plant growth. A hydroponic greenhouse prototype was developed to capture real-time climate data at high temporal resolution. A custom 1D convolutional neural network (1D-CNN) optimized via a genetic algorithm (GA) was employed to predict environmental fluctuations, achieving R² scores up to 0.99 and a standard error of prediction (SEP) as low as 0.35%. The system then actuated climate control mechanisms to restore and maintain symmetry. Experimental validation revealed that plants grown under the symmetry-aware control system exhibited significantly improved growth metrics. The results underscore the potential of integrating symmetry-aware DL strategies into precision agriculture in achieving sustainable and resilient plant production systems. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

Longstanding anomalies in the Cosmic Microwave Background (CMB), including the low quadrupole moment and hemispherical power asymmetry, have recently been linked to an underlying parity asymmetry. We show here how this parity asymmetry naturally arises within a quantum framework that explicitly incorporates the construction of a geometric quantum vacuum based on parity ($\mathcal{P}$) and time-reversal ($\mathcal{T}$) transformations. This framework restores unitarity in quantum field theory in curved spacetime (QFTCS). When applied to inflationary quantum fluctuations, this unitary QFTCS formalism predicts parity asymmetry as a natural consequence of cosmic expansion, which inherently breaks time-reversal symmetry. Observational data strongly favor this unitary QFTCS approach, with a Bayes factor, the ratio of marginal likelihoods associated with the model given the data $p\left(M|D\right)$, exceeding 650 times that of predictions from the standard inflationary framework. This Bayesian approach contrasts with the standard practice in the CMB community, which evaluates $p\left(D|M\right)$, the likelihood of the data under the model, which undermines the importance of low-ℓ physics. Our results, for the first time, provide compelling evidence for the quantum gravitational origins of CMB parity asymmetry on large scales. Full article

High-quality reconstruction of magnetic resonance imaging (MRI) data from undersampled k-space remains a significant challenge in medical imaging. While the integration of compressed sensing and deep learning has notably improved the performance of MRI reconstruction, existing convolutional neural network-based methods are limited by their small receptive fields, which hinders the exploration of global image features. Meanwhile, Swin-Transformer-based approaches struggle with inter-window information interaction and global feature extraction and perform poorly when dealing with complex repetitive structures and similar texture features under undersampling conditions, resulting in suboptimal reconstruction quality. To address these issues, we propose a Symmetry-based Cascaded Dual-Domain Hybrid Attention Network (SCDDHAN). Leveraging the inherent symmetry of medical images, the network combines channel and self-attention to improve global context modeling and local detail restoration. The overlapping window self-attention module is designed with symmetry in mind to improve cross-window information interaction by overlapping adjacent windows and directly linking neighboring regions. This facilitates more accurate detail recovery. The concept of symmetry is deeply embedded in the network design, guiding the model to better capture regular patterns and balanced structures within MRI images. Experimental results demonstrate that under 5× and 10× undersampling conditions, SCDDHAN outperforms existing methods in artifact suppression, achieving more natural edge transitions, clearer complex textures and superior overall performance. This study highlights the potential of integrating symmetry concepts into hybrid attention modules for accelerating MRI reconstruction and offers an efficient, innovative solution for future research in this area. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Energy-aware scheduling plays a critical role in modern computing and manufacturing systems, where energy consumption often increases with job execution order or resource usage intensity. This study investigates a scheduling problem in which a sequence of heterogeneous jobs—classified as either heavy or light—must be assigned to multiple identical machines with monotonically increasing slot costs. While the machines are structurally symmetric, the fixed job order and cost asymmetry introduce significant challenges for optimal job allocation. We formulate the problem as an integer linear program and simplify the objective by isolating the cumulative cost of heavy jobs, thereby reducing the search for optimality to a position-based assignment problem. To address this challenge, we propose a structured assignment model termed monotonic machine assignment, which enforces index-based job distribution rules and restores a form of functional symmetry across machines. We prove that any feasible assignment can be transformed into a monotonic one without increasing the total energy cost, ensuring that the global optimum lies within this reduced search space. Building on this framework, we first present a general dynamic programming algorithm with complexity $O\left(n^2{m}^2\right)$. More importantly, by introducing a structural correction scheme based on misaligned assignments, we design an iterative refinement algorithm that achieves global optimality in only $O\left(n{m}^2\right)$ time, offering significant scalability for large instances. Our results contribute both structural insight and practical methods for optimal, position-sensitive, energy-aware scheduling, with potential applications in embedded systems, pipelined computation, and real-time operations. Full article

Background: Nasal reconstruction requires a balance between aesthetic and functional restoration. Recent advances in three-dimensional (3D) printing have introduced new approaches to this field, enabling precise, patient-specific interventions. This review explores the applications, benefits, and challenges of integrating 3D printing in nasal reconstruction. Methods: A literature search was conducted using PubMed, Scopus, and Web of Science to identify studies on 3D printing in nasal reconstruction. Peer-reviewed articles and clinical trials were analyzed to assess the impact of 3D-printed models, implants, and bioengineered scaffolds. Results: 3D printing facilitates the creation of anatomical models, surgical guides, and implants, enhancing surgical precision and patient outcomes. Techniques such as stereolithography (SLA) and selective laser sintering (SLS) enable high-resolution, biocompatible constructs using materials like polylactic acid, titanium, and hydroxyapatite. Computational fluid dynamics (CFD) tools improve surgical planning by optimizing nasal airflow. Studies show that 3D-printed guides reduce operative time and improve symmetry. Emerging bioprinting techniques integrating autologous cells offer promise for tissue regeneration. Challenges and Future Directions: Challenges include high costs, imaging limitations, regulatory hurdles, and limited vascularization in bioprinted constructs. Future research should focus on integrating bioactive materials, artificial intelligence-assisted design, and regulatory standardization. Conclusions: 3D printing offers specific advantages in nasal reconstruction, improving precision and outcomes in selected cases. Addressing current limitations through technological and regulatory advancements will further its clinical integration, potentially enhancing reconstructive surgery techniques. Full article

Background/Objectives: Total hip arthroplasty (THA) is a widely performed procedure to alleviate pain and improve function in patients with hip disorders. However, leg length discrepancy (LLD) remains a prevalent complication. LLD can cause gait disturbances, back pain, postural imbalance, and patient dissatisfaction, along with significant medico-legal implications. This review examines the evaluation, management, and medico-legal aspects of LLD. Methods: The review analyzed literature on the prevalence, evaluation methods, and management strategies for LLD in THA. Radiographic and clinical assessment tools were considered, alongside factors such as pelvic obliquity and pre-existing conditions. The importance of preoperative planning, intraoperative techniques (including computer-assisted methods), and comprehensive documentation was evaluated to address both clinical and legal challenges. Results: The review shows that leg length discrepancy (LLD) following total hip arthroplasty (THA) occurs in 3% to 30% of cases, with mean values ranging from 3 to 17 mm. LLD may result from anatomical or procedural factors, and effective evaluation requires both radiographic imaging and clinical assessment. Preoperative planning plays a critical role in accurately assessing anatomical parameters and selecting appropriate prosthetic components to preserve or restore limb length symmetry. Advanced intraoperative techniques, including computer-assisted surgery, help reduce LLD incidence. While some complications may be unavoidable, proper documentation and patient communication, particularly regarding informed consent, are essential to mitigate medico-legal risks Conclusions: LLD after THA requires a multidimensional approach incorporating clinical, radiological, biomechanical, and legal considerations. Effective preoperative and intraoperative strategies, combined with robust communication and documentation, are essential to minimize LLD and its associated risks. A focus on precision and patient-centered care can improve outcomes and reduce litigation. Full article

This case report details the successful rehabilitation of a 31-year-old male Asian elephant (Elephas maximus) presenting with an abnormal left forelimb gait following chronic traumatic injury. The elephant exhibited a distinctive circumduction gait with a semicircular arc movement, characterized by limited flexion at the elbow and carpus, along with compensatory proximal shrugging during the swing phase. Diagnostic evaluations revealed joint space narrowing and ligament fibrosis, while biomechanical gait analysis using inertial measurement units highlighted significant asymmetries between affected and unaffected limbs. An interprofessional team developed a comprehensive rehabilitation protocol that integrated peripheral magnetic stimulation, task-specific therapeutic walking with adjustable obstacles, and progressive strengthening exercises. At the eight-week follow-up, improvements were observed in cross-correlation coefficients of limb movement and imaging assessments, indicating enhanced symmetry and structural improvements with reduced fibrosis. However, persistent discrepancies in elbow functions suggest that further targeted rehabilitation may be warranted. This report underscores the potential of a coordinated interprofessional approach to restore functional gait patterns in elephants and offers valuable insights for future rehabilitative strategies in managing complex musculoskeletal injuries in large mammals. Full article