Search Results (31)

Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement. Full article

The Gini coefficient, while widely used to quantify inequality in biological size distributions, lacks the capacity to resolve directional asymmetry inherent in Lorenz curves, a critical limitation for understanding skewed resource allocation strategies. To address this, we extend our prior geometric framework of the rotated and right-shifted Lorenz curve (RRLC) by introducing two original asymmetry metrics: the positional shift ratio (P_L, defined as $x_c/\sqrt{2}$, where $x_c$ is the x-coordinate of the RRLC’s maximum value point) and the area ratio (P_A, defined as A_L/(A_L + A_R), where A_L and A_R denote the areas under the left and right segments of the RRLC). These indices uniquely dissect contributions of dominant versus small individuals to overall inequality, with P_L reflecting the peak position of the RRLC and P_A quantifying the area dominance of its left segment. Theoretically, P_L directly links to the classical Lorenz asymmetry coefficient S (defined as $S=x_c^{\prime }+y_c^{\prime }$, where $\left(x_c^{\prime },y_c^{\prime }\right)$ is the tangent point on the original Lorenz curve with a 45° slope) through S = 2 − 2P_L, bridging geometric transformation and parametric asymmetry analysis. Applied to 480 Shibataea chinensis Nakai shoots, our analysis revealed that over 99% exhibited pronounced left-skewed distributions, where abundant large leaves drove the majority of leaf area inequality, challenging assumptions of symmetry in plant canopy resource allocation. The framework’s robustness was further validated by the strong correlation between P_A and P_L. By transforming abstract Lorenz curves into interpretable bell-shaped performance curves, this work provides a novel toolkit for analyzing asymmetric size distributions in ecology. The proposed metrics can be applied to refine light-use models, monitor phenotypic plasticity under environmental stress, and scale trait variations across biological hierarchies, thereby advancing both theoretical and applied research in plant ecology. Full article

Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive Network), a symmetry-driven framework for ODD (oriented object detection), is proposed. GAANet incorporates a symmetry-preserving mechanism into three critical components—feature extraction, representation modeling, and metric optimization—facilitating systematic improvements from structural representation to learning objectives. A CAX-ViT (Contextual Additive Exchange Vision Transformer) is developed to enhance multi-scale structural modeling by combining spatial–channel symmetric interactions with convolution–attention fusion. A GBBox (Gaussian Bounding Box) representation is employed, which implicitly encodes directional information through the invariance of the covariance matrix, thereby alleviating angular periodicity problems. Additionally, a GPIoU (Gaussian Product Intersection over Union) loss function is introduced to ensure geometric consistency between training objectives and the SkewIoU evaluation metric. GAANet achieved a 90.58% mAP on HRSC2016, 89.95% on UCAS-AOD, and 77.86% on the large-scale DOTA v1.0 dataset, outperforming mainstream methods across various benchmarks. In particular, GAANet showed a +3.27% mAP improvement over ${\mathrm{R}}^3$Det and a +4.68% gain over Oriented R-CNN on HRSC2016, demonstrating superior performance over representative baselines. Overall, GAANet establishes a closed-loop detection paradigm that integrates feature interaction, probabilistic modeling, and metric optimization under symmetry priors, offering both theoretical rigor and practical efficacy. Full article

The seismic performance of industrial and civil buildings is severely challenged by natural or man-made actions over a long period of time in service. Local strengthening is often carried out to avoid extensive strength reduction. However, current research primarily focuses on enhancing the mechanical performance of individual strengthened members, with little attention to the impact of local strengthening on the overall structure. In this study, the effect of layout symmetry on the overall seismic performance of a six-story reinforced concrete (RC) frame when locally strengthened by the strengthening bonding method is investigated by means of finite element analysis. Four strengthening schemes are considered: single-corner asymmetric, single-end asymmetric, quadrilateral symmetric, and central symmetric strengthening. The modal analysis confirms the enhanced stiffness in the strengthened structure. Asymmetric schemes yield uneven stiffness distributions, leading to pronounced vertical vibrations in higher modes. Conversely, symmetrical strengthening minimizes stiffness disparities through an optimized layout, yielding superior stiffness enhancements. The pushover analysis reveals a 53.6% increase in the lateral load-bearing capacity relative to the original configuration. Increasing the strengthening layers in symmetrical schemes further improves the lateral stiffness and performance reserve. However, when the number of strengthening layers exceeds four, the benefits become limited, and asymmetric strengthening significantly increases the inter-story drift ratio compared to its symmetric counterpart. Additionally, asymmetric strengthening leads to substantial lateral displacement discrepancies, thereby diminishing the overall structural coordination. Therefore, practical applications should adopt a holistic approach by favoring symmetrical strengthening and selecting an optimal number of strengthening layers to maximize the benefits. Full article

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) have emerged as key technologies for next-generation (6G) wireless networks, attracting significant attention from researchers. As an advanced extension of RISs, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) offer superior geometric and functional symmetry due to their capability to simultaneously reflect and transmit signals, thereby achieving full 360° spatial coverage. This symmetry not only ensures balanced energy distribution between the Transmission (T) and Reflection (R) regions but also facilitates interference cancellation through phase alignment. Furthermore, in NOMA networks, the symmetric allocation of power coefficients and the tunable transmission and reflection coefficients of STAR-RIS elements aligns with the principle of resource fairness in multi-user systems, which is crucial for maintaining fairness under asymmetric channel conditions. In this study, key factors, such as interference sources and distance effects, are considered in order to conduct a detailed analysis of the performance of STAR-RIS-assisted NOMA wireless education networks under multiple interference devices. Specifically, a comprehensive analysis of the Signal-to-Interference-plus-Noise Ratio (SINR) for both near-end and far-end devices is conducted, considering various scenarios, such as whether or not the direct communication link exists between the base station and the near-end device, and whether or not the near-end device is affected by interference. Based on these analyses, closed-form approximate expressions for the outage probabilities of the near-end and far-end devices, as well as the closed-form approximation for the system’s Spectral Efficiency (SE), are derived. Notably, the Gamma distribution is used to approximate the square of the composite channel amplitude between the base station and the near-end device, effectively reducing computational complexity. Finally, simulation results validate the accuracy of our analytical results. Both numerical and simulation results show that adjusting the base station’s power allocation, and the transmission and reflection coefficients of the STAR-RIS, can effectively mitigate the impact of interference devices on the near-end device and enhance the communication performance of receiving devices. Additionally, increasing the number of STAR-RIS elements can effectively improve the overall performance of the near-end device, far-end device, and the entire system. Full article

To boost the sustainable development of energy and the environment, a new power system with clean energy sources has been proposed by the Chinese government and traditional coal-fired power units are being transformed into regulation service providers for this new energy power system. Then, in this study, complementary power generation cooperation between traditional coal-fired power and new energy power producers is analyzed and discussed, and the energy quota agents, power sellers, are also included. Based on the cooperation game idea, different decision-making models of the tripartite power entities are elaborately constructed. Then, according to the price linkage mechanism between new energy and traditional thermal power, the profit of all power subjects is calculated and the profit allocation process is also analyzed. The conclusions show that the similarity of the two wholesale power price coefficients verifies the symmetry of the cooperative status of power producers. For BPC and SPC quota patterns, for example, BPC is bundled with new energy power and green certificates, whereas SPC is separate. Under the SPC pattern, there is a critical value for effective cooperation between the two power producers in the price range of traditional thermal power or new energy, which can achieve a win–win situation of increasing economic benefits and the consumption scale. Under the BPC pattern, the dynamic benefit compensation mechanism, which is the corrected Shapley value based on the RPS quota ratio, can solve the compressed profit of traditional coal-fired power producers. In contrast, the overall effect of profit allocation using the nucleolar method is not ideal. This study aims to give full play to the elastic induction effect of RPS to promote the sustainable transformation of traditional thermal power energy, especially combining the market mechanism to encourage traditional coal-fired power units to improve green technology to advance the construction of the green power market in China. Full article

Background/Objective: The effects of concomitant meniscal tears and their associated treatment on strength, lower extremity balance, and functional status after anterior cruciate ligament reconstruction (ACLR) have not been widely investigated. This study aimed to compare the functional outcomes in patients who underwent ACLR with concomitant treatment of the medial meniscus repair versus meniscectomy when returning to unrestricted physical activity. Methods: A total of 85 patients who underwent primary ACLR with combined meniscal repair (MREP; n = 39) or meniscectomy (MRES; n = 46) were assessed. The dataset included the Functional Movement Screen^TM (FMS) outcomes and single-leg balance test (SLBT) with anterior–posterior, medial–lateral, and overall stability indexes. Isokinetic knee extension and flexion strengths were tested at velocities of 60 deg·s⁻¹ and 180 deg·s⁻¹. The peak torque-to-body weight ratio (PT/BW) and limb symmetry index (LSI) were calculated. Results: In the functional assessment, there was no significant inter-group difference in the composite score of the FMS (MREP: 15.08 pts vs. MRES: 15.13 pts; p > 0.05). The SLBT outcomes in inter-group and inter-extremity comparisons were irrelevant (p > 0.05), too. Significant differences emerged in the inter-group comparison of the knee extension strength in the non-operated extremity at both 60 deg·s⁻¹ and 180 deg·s⁻¹ (p = 0.02). Inter-extremity differences were significant in both the MREP and MRES groups for knee extension and flexion at both angular velocities (all p values < 0.05). For knee extension, the LSI values ranged from 82% to 87%, and for flexion, from 77% to 84%, with no significant inter-group differences. Conclusions: Patients undergoing ACLR with concomitant meniscal repair or resection did not exhibit differences in isokinetic muscle strength, lower extremity balance, and functional tests upon returning to activity. However, participants in both groups demonstrated significant differences between the operated and non-operated extremities as far as the knee joint extensor and flexor strengths are concerned. Therefore, rehabilitation protocols should prioritize equalizing inter-extremity strength differences after the ACLR with additional treatment procedures addressing the menisci. Full article

Shallow geothermal or ground source heat pump (GSHP) energy systems offer efficient space heating and cooling, reducing greenhouse gas emissions and electrical consumption. Incorporating ground heat exchangers (GHEs) within pile foundations, as part of these GSHP systems, has gained significant attention as it can reduce capital costs. The design and optimisation of GHEs connected in parallel within energy piles have been researched widely, considering symmetrical placement, while the potential misplacement due to construction errors and the optimal placement remain mostly unexplored. This study utilises 3D finite element numerical methods, analysing energy piles with diameters from 0.5 m to 1.4 m, equipped with parallelly connected U-tube and W-tube GHEs. The impact of GHE loop placement is analysed, considering the influence of the ground and concrete thermal conductivities, pile length, fluid flow rate, GHE pipe diameter, and pile spacing. Results indicate a marginal impact, less than 3%, on the overall heat transfer when loops deviate from symmetry and less than 5% on the total heat transfer shared by each loop, except for highly non-symmetric configurations. Symmetrical and evenly spaced loop placement generally maintains favourable thermal performance and ease of installation. This study underscores the flexibility in GHE design and construction with a low risk of thermal yield variations due to uncertainties, particularly with a separation-to-shank distance ratio between 0.5 and 1.5 in a symmetrical distribution. Full article

Whether to use face priors in the face super-resolution (FSR) methods is a symmetry problem.Various face priors are used to describe the overall and local face features, making the generation of super-resolution face images expensive and laborious. FSR methods that do not require any prior information tend to focus too much on the local features of the face, ignoring the modeling of global information. To solve this problem, we propose a dual-path facial image super-resolution network (SwinDPSR) fused with Swin Transformer. The network does not require additional face priors, and it learns global face shape and local face components through two independent branches. In addition, the channel attention ECA module is used to aggregate the global and local face information in the above dual-path sub-networks, which can generate corresponding high-quality face images. The results of face super-resolution reconstruction experiments on public face datasets and a real-scene face dataset show that SwinDPSR is superior to previous advanced methods both in terms of visual effects and objective indicators. The reconstruction results are evaluated with four evaluation metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and mean perceptual score (MPS). Full article

Seed shape descriptions of species of Vitis have traditionally been based on adjectives comparing overall shape with geometric figures, such as oval, elongated oval, and pear-shaped, corresponding to higher values of the Stummer index (lower aspect ratio) for oval, and lower values of the Stummer index for pear shape (or elongated seeds, with a higher aspect ratio). Analytical, quantitative descriptions of shape have recently been applied to diverse genera of Vitaceae and cultivated varieties of Vitis. Here, we present the application of three quantitative methods to the seed shape description of ten species of the genus Vitis and three species of related genera (Ampelopsis, Cissus and Parthenocissus). First, general seed shape was described through comparisons using geometric models. For this, the average silhouettes of representative seed populations were used as models for shape quantification. Two additional quantitative methods were based on the measurement of bilateral symmetry and curvature analysis in the apex. Quantitative methods for shape description based on similarity with the models give an accurate account of the relationships between Vitis species. The resulting dendrogram is like the dendrogram obtained from a combined analysis using the data from general measurements and curvature and symmetry analyses. The original methods presented here for seed morphology are useful for analyzing the phylogenetic relationships between species of Vitis. Full article

Background: Vestibular schwannoma (VS) surgery may cause facial nerve damage. However, a comprehensive evaluation of post-operative facial outcomes may be difficult to achieve. Surface electromyography (sEMG) is a promising non-invasive evaluation tool. However, its use in the follow-up after VS surgery has not been reported yet. The main objective was to develop and validate a new sEMG application specifically for the post-VS surgery setting. Secondary goals were to provide a systematic description of facial muscle activity after VS surgery and assess the association between sEMG parameters and Sunnybrook scale scores. Methods: Thirty-three patients with facial palsy following VS surgery were included. The clinical outcomes (Sunnybrook symmetry, movement, and synkinesis scores) and sEMG parameters (signal amplitude normalized by the maximal voluntary contraction (NEMG) and sEMG synkinesis score (ESS, number of synkinesis per movement sequence)) were evaluated at the end of the follow-up. Results: In all tested muscles, NEMG variance was significantly higher on the affected side than the contralateral (variance ratio test, p < 0.00001 for each muscle). In total, 30 out of 33 patients (90.9%) showed an ESS ≥ 1 (median: 2.5, IQR: 1.5–3.0). On the affected side, NEMG values positively correlated with both dynamic and overall Sunnybrook scores (Spearman’s model, p < 0.05 for each muscle, except orbicularis oculi). ESS significantly correlated with the Sunnybrook synkinesis score (Spearman’s rho: 0.8268, p < 0.0001). Conclusions: We described and preliminarily validated a novel multiparametric sEMG approach based on both signal amplitude and synkinesis evaluation specifically for oto-neurosurgery. Large-scale studies are mandatory to further characterize the semiological and prognostic value of facial sEMG. Full article

The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field. Full article

The physical presence of a street, called the “street view”, is a medium through which people perceive the urban form. A street’s spatial ratio is the main feature of the street view, and its measurement and quality are the core issues in the field of urban design. The traditional method of studying urban aspect ratios is manual on-site observation, which is inefficient, incomplete and inaccurate, making it difficult to reveal overall patterns and influencing factors. Street view images (SVI) provide large-scale urban data that, combined with deep learning algorithms, allow for studying street spatial ratios from a broader space-time perspective. This approach can reveal an urban forms’ aesthetics, spatial quality, and evolution process. However, current streetscape research mainly focuses on the creation and maintenance of spatial data infrastructure, street greening, street safety, urban vitality, etc. In this study, quantitative research of the Beijing street spatial ratio was carried out using street view images, a convolution neural network algorithm, and the classical street spatial ratio theory of urban morphology. Using the DenseNet model, the quantitative measurement of Beijing’s urban street location, street aspect ratio, and the street symmetry was realized. According to the model identification results, the law of the gradual transition of the street spatial ratio was depicted (from the open and balanced type to the canyon type and from the historical to the modern). Changes in the streets’ spatiotemporal characteristics in the central area of Beijing were revealed. Based on this, the clustering and distribution phenomena of four street aspect ratio types in Beijing are discussed and the relationship between the street aspect ratio type and symmetry is summarized, selecting a typical lot for empirical research. The classical theory of street spatial proportion has limitations under the conditions of high-density development in modern cities, and the traditional urban morphology theory, combined with new technical methods such as streetscape images and deep learning algorithms, can provide new ideas for the study of urban space morphology. Full article

When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder’s influence to eliminate the paradox. The ECIT uses relative risk difference P_d = max(0, (R − 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to P_d before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R − 1)/max(R, 1). Cc is like P_d but has normalizing property (between −1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that P_d and Cc are more reasonable than D; Cc is more useful than P_d. Full article

The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy. Full article