Search Results (52)

Outlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to their inherent lack of data preprocessing capabilities, consequently demonstrating degraded performance when applied to novel or heterogeneous datasets. Moreover, the computation of the outlier factor for each sample in these algorithms results in considerably higher computational cost, especially in the case of large datasets. This paper introduces a local outlier detection method named FOLOF (FCM Objective Function-based LOF) through an examination of existing algorithms. The approach starts by applying the elbow rule to determine the optimal number of clusters in the dataset. Subsequently, the FCM objective function is employed to prune the dataset to extract a candidate set of outliers. Finally, a weighted local outlier factor detection algorithm computes the degree of anomaly for each sample in the candidate set. For the analysis, the Golden Section method was used to classify the outliers. The underlying causes of these outliers can be revealed by exploring the anomalous properties of each outlier data point through the outlier factors of each dimension property. This approach has been validated on artificial datasets, the UCI dataset, and an NBA player dataset to demonstrate its effectiveness. 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

Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we investigated the relationship between antibiotic permeation through the OmpF porin of Escherichia coli and antimicrobial efficacy. We measured the relative permeability coefficients (RPCs) through the bacterial porin by liposome swelling assays, including non-antibacterial molecules, and the minimum inhibitory concentrations (MICs) against E. coli. We developed a machine learning (ML) approach by combining classification and regression models to correlate these data sets. Our strategy allowed us to quantify the negative correlation between RPC and MIC values, clearly indicating that increased permeability through OmpF generally leads to improved antimicrobial activity. Moreover, the correlation was remarkable only for compounds with significant permeability coefficients. Conversely, when permeation ability is low, other factors play the most significant role in antimicrobial potency. Importantly, the proposed ML-based approach was set by exploiting the available seminal information from previous investigations in order to keep the number of molecular descriptors to the minimum for greater interpretability. This provided valuable insights into the complex interplay between different molecular properties in defining the overall outer membrane permeation and, consequently, the antimicrobial efficacy. From a practical perspective, the presented approach does not aim at identifying the “golden rule” for boosting antibiotic potency. The automated protocol presented here could be used to inspect, in silico, many alternatives of a given molecular structure, with the output being the list of the best candidates to be then synthesized and tested. This could be a valuable in silico tool for researchers in both academia and industry to rapidly evaluate novel potential compounds and reduce costs and time during the early drug discovery stage. Full article

To address the issues of fragmented species information, low knowledge extraction efficiency, and insufficient utilization in the aquaculture domain, the main objective of this study is to construct the first knowledge graph for the Golden Pomfret aquaculture field and optimize the named entity recognition (NER) methods used in the construction process. The dataset contains challenges such as long text processing, strong local context dependencies, and entity sample imbalance, which result in low information extraction efficiency, recognition errors or omissions, and weak model generalization. This paper proposes a novel named entity recognition model, LA-CANER (Local Attention-Category Awareness NER), which combines local attention mechanisms with category awareness to improve both the accuracy and speed of NER. The constructed knowledge graph provides significant scientific knowledge support to Golden Pomfret aquaculture workers. First, by integrating and standardizing multi-source information, the knowledge graph offers comprehensive and accurate data, supporting decision-making for aquaculture management. The graph enables precise reasoning based on disease symptoms, environmental factors, and historical production data, helping workers identify potential risks early and take preventive actions. Furthermore, the knowledge graph can be integrated with large models like GPT-4 and DeepSeek-R1. By providing structured knowledge and rules, the graph enhances the reasoning and decision-making capabilities of these models. This promotes the application of smart aquaculture technologies and enables precision farming, ultimately increasing overall industry efficiency. Full article

The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk (henceforth “golden strategy”) has only been studied if all the involved derivatives have the same underlying asset. This paper also considers multi-asset derivatives, gives practical methods to build multi-asset golden strategies for both the expected shortfall and the expectile risk measure, and shows that the use of multi-asset options makes the performance of the obtained golden strategy more efficient. Practical rules are given under the Black–Scholes–Merton multi-dimensional pricing model. Full article

We have collected theoretical arguments supporting the functional role of nano-metallic coatings of solar cells, which enhance solar cell efficiency via by plasmon-strengthening the absorption of sun-light photons and reducing the binding energy of photoexcitons. The quantum character of the plasmonic effect related to the absorption of photons (called the optical plasmonic effect) is described in terms of the Fermi golden rule for the quantum transitions of semiconductor-band electrons induced by plasmons from a nano-metallic coating. The plasmonic effect related to the lowering of the exciton binding energy (called the electrical plasmonic effect) is of particular significance for metalized perovskite solar cells and is also characterized in quantum mechanics terms. The coupling between plasmons in nanoparticles from a coating with band electrons in a semiconductor substrate significantly modifies material properties (dielectric functions) both of the particles and the semiconductor, beyond the ability of the classical electrodynamics to describe. Full article

Background: Continuous vital sign monitoring via wearable technology, combined with algorithm-based notifications, has been utilized for early detection of patient deterioration. In this retrospective observational study, we summarize a large-scale implementation of a continuous monitoring system in medical–surgical units of two hospitals over the course of fifteen (15) months. Methods: An FDA-cleared wireless monitoring device (BioButton^®, BioIntelliSense Inc., Golden, CO, USA), was placed on each patient upon admission. The wearable device measures heart rate and respiratory rate at rest, skin temperature, and patient activity levels. High-frequency data (up to 1440 measurements per day) are transmitted to display in exception management software (BioDashboard™, version 2.9, BioIntelliSense Inc.). Algorithmic and rules-based notifications are triggered based on clinical and statistical trending criteria. We present (i) agreement of device readings with bedside charted measurements, (ii) the frequency of notifications, (iii) the occurrence of notifications prior to clinical deterioration events, and (iv) impact on clinical management, including early data on length of stay (LOS). Results: In total, 11,977 patient encounters were monitored at two sites. Bias ±95% limits of agreement were 1.8 ± 12.5 for HR and 0.4 ± 8.0 for RR. The rates of notifications were 0.97 and 0.65 per patient-day at Sites 1 and 2, respectively. Among clinical deteriorations, 73% (66%) had at least one notification within 24 h prior at Site 1 (Site 2). At Site 1, there were 114 cases for which a notification led to a new or changed physician’s order. LOS in the first unit monitored by the system exhibited a decreasing trend from 3.07 days to 2.75 days over 12 months. Conclusions: Wearable continuous vital sign monitoring with the BioIntelliSense BioButton^® system enables early detection of clinical deterioration. Full article

In this work, we study a new line-search rule for solving the pseudomonotone variational inequality problem with non-Lipschitz mapping in real Hilbert spaces as well as provide a strong convergence analysis of the sequence generated by our suggested algorithm with double inertial extrapolation steps. In order to speed up the convergence of projection and contraction methods with inertial steps for solving variational inequalities, we propose a new approach that combines double inertial extrapolation steps, the modified Mann-type projection and contraction method, and the line-search rule, which is based on the golden ratio $(\sqrt{5}+1)/2$. We demonstrate the efficiency, robustness, and stability of the suggested algorithm with numerical examples. Full article

Alzheimer’s disease, characterized by a decline in cognitive functions, is frequently associated with decreased levels of acetylcholine due to the overactivity of acetylcholinesterase (AChE). Inhibiting AChE has been a key therapeutic strategy in treating Alzheimer’s disease, yet the search for effective inhibitors, particularly from natural sources, continues due to their potential for fewer side effects. In this context, three new alkaloids—oleracone L, portulacatone B, and portulacatal—extracted from Portulaca oleracea L., have recently shown promising anticholinesterase activity in vitro. However, no experimental or computational studies have yet explored their binding potential. This study represents the first comprehensive in silico analysis of these compounds, employing ADME prediction, molecular docking, molecular dynamics simulations, and MM-PBSA calculations to assess their therapeutic potential. The drug-likeness was evaluated based on Lipinski, Pfizer, Golden Triangle, and GSK rules, with all three alkaloids meeting these criteria. The ADME profiles suggested that these alkaloids can effectively cross the blood–brain barrier, a critical requirement for Alzheimer’s treatment. Molecular docking studies revealed that oleracone L had the highest binding affinity (−10.75 kcal/mol) towards AChE, followed by portulacatal and portulacatone B, demonstrating significant interactions with crucial enzyme residues. Molecular dynamics simulations over 200 ns confirmed the stability of these interactions, with RMSD values below 2 Å for all complexes, indicating stable binding throughout the simulation period. RMSF and the radius of gyration analyses further corroborated the minimal impact of these alkaloids on the enzyme’s overall flexibility and compactness. Moreover, MM-PBSA calculations provided additional support for the binding efficacy, showing that oleracone L, with the most favorable binding energy, could be a superior inhibitor, potentially due to its stronger and more consistent hydrogen bonding and favorable electrostatic interactions compared to the other studied alkaloids. These computational findings highlight the binding efficiency and potential therapeutic viability of these alkaloids as AChE inhibitors, suggesting they could be promising candidates for Alzheimer’s disease treatment. The study underscores the importance of further validation through in vitro and in vivo experiments to confirm these predictions. Full article

In Ying Xian Fogong Si Shijia Ta 應縣佛宮寺釋迦塔 (Sakyamuni Pagoda of Fogong Temple in Ying County), Shanxi, there are statues set on each floor, and 26 exist in total, with six murals painted on the first floor. The pagoda was designed as a vertically rising Buddhist temple, and the interior space of each floor was customized for the statues. Based on previous research and through surveying and mapping of the architecture and statuary (including the murals on the first floor) combined with geometric design analysis, this paper proposes the following: First, there exists a clear geometric proportion among the interior space and statues on each floor of the pagoda. Second, clear proportional relationships also exist among the statues on each floor, and each of the 26 statues has ‘classical’ proportion rules. Third, the height of the giant Buddha statue on the first floor is the module for not only the height of the statues on each floor but also the construction of the whole pagoda such that the height of the statue on the first floor is 1/6 of the total height of the pagoda (excluding the base). And the ratio of the pagoda’s total height to the first floor’s diameter, the ratio of the total height to the top-story height under the column capital, and all the other geometric proportions are closely related to the architectural modeling. And finally, in the construction of the pagoda, statues, and murals, the scale is deduced to be 1 chi 尺 (Chinese foot) = 29.5 cm. These values give clear scale logics not only to the construction but also to the details of the statues. Accordingly, the most frequently used proportions in the architectural space, statues, and murals of the Pagoda of Fogong Temple are $\sqrt{2}$, 3:2, 5:3 (or 8:5), and 9:5, which are imbued with cultural messages, like Zhou Bi Suan Jing 周髀算經 (The Mathematical classic of the Zhou shadow-gauging instrument), Ying Zao Fa Shi 營造法式 (Treatise on Architectural Methods or State Building Standards), the ancient Chinese world view—tian yuan di fang 天圜地方 (the dome-shaped heaven and the flat, square earth) reflected from “ yuan fang tu 圓方圖 (rounded-square map)” and “fang yuan tu 方圓圖 (squared-circle map)”, ancient Chinese ideas that ”san tian liang di er yi shu 參天兩地而倚數 (‘three’ is the number of the heaven and ‘two’ is the number of the earth, and all numbers are based on them)” and “jiu wu zhi zun 九五之尊 (nine and five are the numbers of the honorable central position)”, and most probably related to the “mandala” of Esoteric Buddhism and to the Western “Golden Ratio”, which all need further research in depth. Full article

We examine the optical and electronic properties of a GaAs spherical quantum dot with a hydrogenic impurity in its center. We study two different confining potentials: (1) a modified Gaussian potential and (2) a power-exponential potential. Using the finite difference method, we solve the radial Schrodinger equation for the $1s$ and $1p$ energy levels and their probability densities and subsequently compute the optical absorption coefficient (OAC) for each confining potential using Fermi’s golden rule. We discuss the role of different physical quantities influencing the behavior of the OAC, such as the structural parameters of each potential, the dipole matrix elements, and their energy separation. Our results show that modification of the structural physical parameters of each potential can enable new optoelectronic devices that can leverage inter-sub-band optical transitions. Full article

Knee osteoarthritis (OA) is one of the leading causes of disability around the globe. Osteoarthritis is mainly considered a disease affecting the elderly. However, more and more studies show that sports overuse, obesity, or congenital disorders can initiate a pathologic cascade that leads to OA changes in the younger population. Nevertheless, OA mostly affects the elderly, and with increasing life expectancy, the disease will develop in more and more individuals. To date, the golden standard in the treatment of the end-stage of the disease is total joint replacement (TJR), which restores painless knee motion and function. One of the weakest elements in TJR is its bonding with the bone, which can be achieved by bonding material, such as poly methyl-methacrylate (PMMA), or by cementless fixation supported by bone ingrowth onto the endoprosthesis surface. Each technique has its advantages; however, the most important factor is the revision rate and survivor time. In the past, numerous articles were published regarding TJR revision rate, but no consensus has been established yet. In this review, we focused on a comparison of cemented and cementless total knee replacement surgeries. We introduced PICO rules, including population, intervention, comparison and outcomes of TJR in a PubMed search. We identified 783 articles published between 2010 and 2023, out of which we included 14 in our review. Our review reveals that there is no universally prescribed approach to fixate knee prostheses. The determination of the most suitable method necessitates an individualized decision-making process involving the active participation and informed consent of each patient. Full article

When dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems. To realize the design with limited computational resources, problems with the features above need to be linearized and then solved using solution algorithms for linear programming. The adaptive linear programming (ALP) algorithm is an extension of the Sequential Linear Programming algorithm where a nonlinear compromise decision support problem (cDSP) is iteratively linearized, and the resulting linear programming problem is solved with satisficing solutions returned. The reduced move coefficient (RMC) is used to define how far away from the boundary the next linearization is to be performed, and currently, it is determined based on a heuristic. The choice of RMC significantly affects the efficacy of the linearization process and, hence, the rapidity of finding the solution. In this paper, we propose a rule-based parameter-learning procedure to vary the RMC at each iteration, thereby significantly increasing the speed of determining the ultimate solution. To demonstrate the efficacy of the ALP algorithm with parameter learning (ALPPL), we use an industry-inspired problem, namely, the integrated design of a hot-rolling process chain for the production of a steel rod. Using the proposed ALPPL, we can incorporate domain expertise to identify the most relevant criteria to evaluate the performance of the linearization algorithm, quantify the criteria as evaluation indices, and tune the RMC to return the solutions that fall into the most desired range of each evaluation index. Compared with the old ALP algorithm using the golden section search to update the RMC, the ALPPL improves the algorithm by identifying the RMC values with better linearization performance without adding computational complexity. The insensitive region of the RMC is better explored using the ALPPL—the ALP only explores the insensitive region twice, whereas the ALPPL explores four times throughout the iterations. With ALPPL, we have a more comprehensive definition of linearization performance—given multiple design scenarios, using evaluation indices (EIs) including the statistics of deviations, the numbers of binding (active) constraints and bounds, the numbers of accumulated linear constraints, and the number of iterations. The desired range of evaluation indices (DEI) is also learned during the iterations. The RMC value that brings the most EIs into the DEI is returned as the best RMC, which ensures a balance between the accuracy of the linearization and the robustness of the solutions. For our test problem, the hot-rolling process chain, the ALP returns the best RMC in twelve iterations considering only the deviation as the linearization performance index, whereas the ALPPL returns the best RMC in fourteen iterations considering multiple EIs. The complexity of both the ALP and the ALPPL is O(n²). The parameter-learning steps can be customized to improve the parameter determination of other algorithms. Full article

The golden rule in tissue engineering is the creation of a synthetic device that simulates the native tissue, thus leading to the proper restoration of its anatomical and functional integrity, avoiding the limitations related to approaches based on autografts and allografts. The emergence of synthetic biocompatible materials has led to the production of innovative scaffolds that, if combined with cells and/or bioactive molecules, can improve tissue regeneration. In the last decade, silk fibroin (SF) has gained attention as a promising biomaterial in regenerative medicine due to its enhanced bio/cytocompatibility, chemical stability, and mechanical properties. Moreover, the possibility to produce advanced medical tools such as films, fibers, hydrogels, 3D porous scaffolds, non-woven scaffolds, particles or composite materials from a raw aqueous solution emphasizes the versatility of SF. Such devices are capable of meeting the most diverse tissue needs; hence, they represent an innovative clinical solution for the treatment of bone/cartilage, the cardiovascular system, neural, skin, and pancreatic tissue regeneration, as well as for many other biomedical applications. The present narrative review encompasses topics such as (i) the most interesting features of SF-based biomaterials, bare SF’s biological nature and structural features, and comprehending the related chemo-physical properties and techniques used to produce the desired formulations of SF; (ii) the different applications of SF-based biomaterials and their related composite structures, discussing their biocompatibility and effectiveness in the medical field. Particularly, applications in regenerative medicine are also analyzed herein to highlight the different therapeutic strategies applied to various body sectors. Full article

Chinese privet (Ligustrum sinense) has escaped cultivation in 20 states in the USA and is classified as a noxious weed in Florida, where its use is prohibited. There is confusion among producers and consumers surrounding the invasive status of its cultivars. In Florida, cultivars of an invasive wildtype species are also invasive unless proven otherwise and exempted from an invasive ruling. This study evaluated the fruiting and landscape performance of two cultivars of Chinese privet, ‘Variegatum’ (variegated privet) and ‘Sunshine’ (sunshine privet), and an interspecific hybrid of another form of privet, ‘NCLX1’ (L. × vicaryi, Golden Ticket^®), planted in southwest, northcentral, and north Florida. ‘Sunshine’ and ‘Variegatum’ privet both performed well throughout the study with average quality ratings ranging from 3.2 to 4.9 (scale of 1–5) and a 100% survival rate during the nearly two-year study. ‘NCLX1’ privet also had a 100% survival rate at the southwest and northcentral sites, but 80% survival at the north Florida site. ‘Variegatum’ privet grew the largest in both height and width compared to ‘Sunshine’ and ‘NCLX1’ privet, that had similar heights. ‘Variegatum’ and ‘NCLX1’ privet flowered during the study, but ‘Sunshine’ privet did not. ‘Variegatum’ privet was the only cultivar evaluated to produce mature fruit and show clear signs of reversion back to its green wildtype form. The DNA content of all three cultivars suggests they are diploids, as is the wildtype form of Chinese privet. These results provide potential alternatives to Chinese privet for use in landscapes but recommend avoiding ‘Variegatum’ privet, due to its frequent reversion to the wildtype and its abundant fruit production. Full article