Search Results (194)

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

The rapid densification of wireless networks demands efficient evaluation of special functions underpinning system-level performance metrics. To facilitate research, we introduce a computational framework tailored for the zero-balanced Gauss hypergeometric function $\Psi (x,y)\triangleq {}_{2}F_1(1,x;1+x;-y)$, a fundamental mathematical kernel emerging in Signal-to-Interference-plus-Noise Ratio (SINR) coverage analysis of non-uniform cellular deployments. Specifically, we propose a novel Reciprocal-Argument Transformation Algorithm (RTA), derived rigorously from a Mellin–Barnes reciprocal-argument identity, achieving geometric convergence with $O\left(1/y\right)$. By integrating RTA with a Pfaff-series solver into a hybrid algorithm guided by a golden-ratio switching criterion, our approach ensures optimal efficiency and numerical stability. Comprehensive validation demonstrates that the hybrid algorithm reliably attains machine-precision accuracy ($\sim {10}^{-16}$) within 1 μs per evaluation, dramatically accelerating calculations in realistic scenarios from hours to fractions of a second. Consequently, our method significantly enhances the feasibility of tractable optimization in ultra-dense non-uniform cellular networks, bridging the computational gap in large-scale wireless performance modeling. Full article

Topological optimization uses two main optimization conditions aimed at achieving the maximum stiffness at minimum weight of the loaded object, while not exceeding the allowable stress. This process naturally creates complex structures with varying degrees of density. There is a certain regularity between the density of the structure and stiffness, with the optimal density being related to the golden ratio. This study contributes to materials modeling and their characterization by introducing a mathematical theory related to the virial theorem as a predictive framework for understanding stiffness–density relationships in additively manufactured structures. The definition of virial stability and the methodology for deriving this stability from the kinetic and potential components of a random signal are introduced. The proposed virial-based model offers a generalizable tool for materials characterization, applicable not only to topological optimization but also to broader areas of materials science and advanced manufacturing. Full article

This paper presents a novel approach to determine dynamic safety and comfort zones in human–robot interactions (HRIs), with a focus on service robots operating in dynamic environments with people. The proposed proxemic model leverages the golden ratio-based comfort zone distribution and ISO safety standards to define adaptive proxemic boundaries for robots around humans. Unlike traditional fixed-threshold approaches, this novel method proposes a gradual and context-sensitive modulation of robot behaviour based on human position, orientation, and relative velocity. The system was implemented on an NVIDIA Jetson Xavier NX platform using a ZED 2i stereo depth camera Stereolabs, New York, USA and tested on two mobile robotic platforms: Go1 Unitree, Hangzhou, China (quadruped) and Scout Mini Agilex, Dongguan, China (wheeled). The initial verification of proposed proxemic model through experimental comfort validation was conducted using two simple interaction scenarios, and subjective feedback was collected from participants using a modified Godspeed Questionnaire Series. The results show that the participants felt comfortable during the experiments with robots. This acceptance of the proposed methodology plays an initial role in supporting further research of the methodology. The proposed solution also facilitates integration into existing navigation frameworks and opens pathways towards socially aware robotic systems. Full article

Background: Androgenetic alopecia (AGA) is a prevalent condition characterized by progressive follicular miniaturization. Minoxidil topical treatment and finasteride oral treatment are the golden standard, but they are limited by local and systemic adverse effects. Combination therapies targeting both follicular stimulation and nutritional support may enhance clinical outcomes. Objective: To evaluate the efficacy of a combined topical and oral therapy compared to topical monotherapy in patients with AGA using trichoscopic and clinical parameters. Methods: In this open-label, prospective trial, 48 patients were assigned to receive either a topical spray alone (Group A) or in combination with oral capsules (Group B) for 3 months. Trichoscopic parameters were assessed at baseline and post-treatment. Paired and independent t-tests, along with Cohen’s d effect sizes, were used to evaluate intra- and inter-group changes. Results: Both groups demonstrated improvements in hair density, thickness, and anagen/telogen ratio. Group B exhibited significantly greater increases in total hair count and anagen conversion (p < 0.05). The effect sizes ranged from small to large, with the most pronounced changes observed in anagen/telogen ratio (Cohen’s d = 0.841) in males. Conclusions: The combination of topical and oral treatment led to greater trichologic improvements than topical therapy alone. While extrapolated projections at 6 and 12 months suggest continued benefit, future studies with longer duration and placebo controls are required to validate these findings. 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

Fatty fish, such as mackerel (Scomber scombrus), are recommended as part of a healthy diet, providing essential fatty acids (FA). Fried fish is appreciated for its attributes, including a crispy texture, golden crust, and pleasant taste. However, frying increases the fat content and the caloric value of food. This study evaluated the use of pre-frying edible coatings on mackerel fillets aiming to: (i) reduce oil absorption, (ii) minimize water loss, preserving fish succulence, and (iii) prevent fat oxidation. For this purpose, alginate- and carrageenan-based coatings were supplemented with extracts of Pelvetia canaliculata (Pc), a seaweed with high potential as a source of bioactive compounds. The fried fillets were analysed for colour, texture, moisture, ash, lipid content, and FA profile. No significant differences were observed for colour and textural parameters. Fillets coated with Pc-supplemented carrageenan showed the highest moisture (an increase of 3%) and the lowest fat content (a decrease of 7,5%) compared to the control (fried uncoated fillets). Coated fillets also exhibited reduced saturated FA and increased monounsaturated FA. In general, linoleic acid (C18:2) decreased markedly, while the values for docosahexaenoic acid (C22:6, n-3) remained stable (11–12% of total FA). Moreover, the n3/n6 ratio and atherogenic indices (AI) were improved in the coated fillets. Full article

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an inner wheel steering angle of 70 degrees. The mechanisms synthesized include the Ackermann steering mechanism and two variants (Type I and Type II) of the Watt-I six-bar steering mechanisms, also known as central-lever steering mechanisms. To ensure accurate steering and minimize tire wear during cornering, adherence to the Ackermann steering condition is enforced. The objective function combines the mean squared structural error at selected steering positions with a penalty term for violations of the Grashoff inequality constraint. Each optimization run involved 100 or 200 iterations, with numerical experiments repeated 100 times to ensure robustness. Kinematic simulations were conducted in ADAMS v2015 to visualize and validate the synthesized mechanisms. Performance was evaluated based on maximum structural error (steering accuracy) and mechanical advantage (transmission efficiency). The results indicate that the optimized Watt-I six-bar steering mechanisms outperform the Ackermann mechanism in terms of steering accuracy. Among the Watt-I variants, the Type II designs demonstrated superior performance and convergence precision compared to the Type I designs, as well as improved results compared to prior studies. Additionally, the optimal Type I-2 and Type II-2 mechanisms consist of two symmetric Grashof mechanisms, can be classified as non-Ackermann-like steering mechanisms. Both optimization methods proved easy to implement and showed reliable, efficient convergence. The DE-gr algorithm exhibited slightly superior overall performance, achieving optimal solutions in seven cases compared to four for the IPSO method. Full article

Golden mulberry (Morus macroura Miq.) is favored for its rich bioactive components and unique flavor, but fruit quality depends on harvest time. In the present study, golden mulberry fruits were collected at 18 (T1), 21 (T2), 24 (T3), and 27 (T4) days after flowering to investigate the impact of the harvesting stage on its physicochemical properties, antioxidant capacity, and aroma profile. Physicochemical parameters such as total phenols, total soluble solids, titratable acidity, and sensory evaluation revealed that the hardness gradually decreased with fruit maturity, whereas the weight of single fruit, total soluble solids, and solid–acid ratio increased, and soluble sugars, titratable acidity, total polyphenols and sugar–acid ratio initially increased and then decreased. Antioxidant capacity, measured by ABTS, FRAP, and DPPH assays, decreased with ripening, but stabilized at T3. In addition, the aroma components of golden mulberry fruit were analyzed by GC-MS, and it was found that aldehyde, alcohol, and ester were the main aroma components of the golden mulberry fruit. Combining the physicochemical indices, sensory evaluation, and aroma profiles, T3 period considered the optimal harvesting time. These findings offer practical guidance for the optimal harvesting and utilization of golden mulberry fruits. Full article

Chrysanthemum morifolium, ‘Huangju’, is a golden chrysanthemum used for making tea. Limited by land resources, the continuous cropping of Chrysanthemum morifolium ‘Huangju’ has led to serious soil issues, which affects its yield and quality. In this study, different ratios of traditional Chinese medicine compound fertilizers were used to regulate the soil environment in order to achieve the green prevention and control of continuous cropping obstacles of the golden chrysanthemum. Five treatments were set up in the experiment: the control (CK) and different proportions of the Chinese herbal compound fertilizer T1, T2, T3, and T4. After the application of the traditional Chinese medicine compound fertilizer, the physical and chemical soil properties of the golden chrysanthemum were changed to varying degrees, resulting in an increased yield of golden silk chrysanthemum and an improved tea quality. This preliminary study on the application of the traditional Chinese medicine compound fertilizer T2 and T3—that is, Sophora flavescens–Stemona sessilifolia–Mentha haplocalyx–Perilla frutescens–Artemisia annua at ratios of 2:1:2:1:1.5 and 3:1:3:1:2—treatments provided the best results and can be further developed to alleviate the continuous cropping obstacles of fertilizers. Full article

Flat panel displays for large applications (monitors and TVs) have structural weaknesses in improving the yield of mass-produced products due to large panels: the yield is defined by ratio of output quantity to input into panel fabrication process. From a panel manufacturing point of view, low-cost production should be achieved through improved yield of mass production (Samsung Display’s quantum dot display backplane panel). So, we set the target yield at an extreme value, over the golden yield (90%) at the beginning of new mass products. The main factors contributing to the yield loss were “lifted insulator and etched active pattern defects”. To reach the target yield, we focused on these two main defects. The root causes of these defects (delta coefficient of thermal expansion and galvanic corrosion) are explained, and a defect generation mechanism is proposed (the size of the separated large power line in relation to the defect rate). The power lines are defined based on an Electroluminescent Voltage at the Drain (ELVDD) and Electroluminescent Voltage at the Source (ELVSS). We developed a separated large power line design to reduce defect rates. This design plays a role in preventing these two defects during the mass production of medium to large display panels for use in TVs by ensuring that the large power line area is less than the optimum value (<0.44 cm²). Full article

In recent years, with the rapid development of the Internet and mobile devices, the high-resolution video industry has ushered in a booming golden era, making video content the primary driver of Internet traffic. This trend has spurred continuous innovation in efficient video coding technologies, such as Advanced Video Coding/H.264 (AVC), High Efficiency Video Coding/H.265 (HEVC), and Versatile Video Coding/H.266 (VVC), which significantly improves compression efficiency while maintaining high video quality. However, during the encoding process, compression artifacts and the loss of visual details remain unavoidable challenges, particularly in high-resolution video processing, where the massive amount of image data tends to introduce more artifacts and noise, ultimately affecting the user’s viewing experience. Therefore, effectively reducing artifacts, removing noise, and minimizing detail loss have become critical issues in enhancing video quality. To address these challenges, this paper proposes a post-processing method based on Convolutional Neural Network (CNN) that improves the quality of VVC-reconstructed frames through deep feature extraction and fusion. The proposed method is built upon a high-resolution dual-path residual gating system, which integrates deep features from different convolutional layers and introduces convolutional blocks equipped with gating mechanisms. By ingeniously combining gating operations with residual connections, the proposed approach ensures smooth gradient flow while enhancing feature selection capabilities. It selectively preserves critical information while effectively removing artifacts. Furthermore, the introduction of residual connections reinforces the retention of original details, achieving high-quality image restoration. Under the same bitrate conditions, the proposed method significantly improves the Peak Signal-to-Noise Ratio (PSNR) value, thereby optimizing video coding quality and providing users with a clearer and more detailed visual experience. Extensive experimental results demonstrate that the proposed method achieves outstanding performance across Random Access (RA), Low Delay B-frame (LDB), and All Intra (AI) configurations, achieving BD-Rate improvements of 6.1%, 7.36%, and 7.1% for the luma component, respectively, due to the remarkable PSNR enhancement. Full article

Feature selection has been a fundamental research area for both conventional and contemporary machine learning since the beginning of predictive analytics. From early statistical methods, such as principal component analysis, to more recent and data-driven approaches, such as deep unsupervised feature learning, selecting input features to achieve the best objective performance has been a critical component of any machine learning application. In this study, we propose a novel, easily replicable, and robust approach called probability-weighted feature selection (PWFS), which randomly selects a subset of features prior to each training–testing regimen and assigns probability weights to each feature based on an objective performance metric such as accuracy, mean-square error, or area under the curve for the receiver operating characteristic curve (AUC–ROC). Using the objective metric scores and weight assignment techniques based on the golden ratio led iteration method, the features that yield higher performance are incrementally more likely to be selected in subsequent train–test regimens, whereas the opposite is true for features that yield lower performance. This probability-based search method has demonstrated significantly faster convergence to a near-optimal set of features compared to a purely random search within the feature space. We compare our method with an extensive list of twelve popular feature selection algorithms and demonstrate equal or better performance on a range of benchmark datasets. The specific approach to assigning weights to the features also allows for expanded applications in which two correlated features can be included in separate clusters of near-optimal feature sets for ensemble learning scenarios. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

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