Desmoplastic fibroma of the jaws: A case series and review of literature

Desmoplastic fibroma (DF) is a benign, locally aggressive neoplasm that rarely occurs in the facial skeleton. It usually presents during the first three decades of life. Due to its aggressiveness and high recurrence rate, early diagnosis is imperative, and complete surgical removal of the lesion is the treatment of choice. Herein, we present three cases of DF namely a 2 year-old girl with a mandibular DF, a 9 year-old boy with a maxillary lesion and a 1.5-year old boy with a mandibular DF. Complete clinicopathological information, treatment plan and long-term follow-up of patients are discussed. Histopathologic features of 3 cases revealed non-capsulated spindle cell tumor with fascicular or swirling patterns in incisional biopsy. Immunohistochemical staining was performed to make a definitive diagnosis. Strongly positive nuclear immunoreactivity for β-catenin confirmed the diagnosis of desmoplastic fibroma in 3 cases. Segmental mandibulectomy, partial maxillectomy and hemimandibulectomy were done for the cases. There was no recurrence in our reported cases after 8 and 11 months and 3 years follow up, respectively. It is noteworthy that despite the aggressive nature of DF, young patients often respond well to wide resection treatment. © 2020, Iranian Society of Pathology. All rights reserved

Experimental evaluation of tensile performance of aluminate cement composite reinforced with weft knitted fabrics as a function of curing temperature

Cement composites (CC) are among the composites most widely used in the construction industry, such as a durable waterproof and fire-resistant concrete layer, slope protection, and application in retaining wall structures. The use of 3D fabric embedded in the cement media can improve the mechanical properties of the composites. The use of calcium aluminate cement (CAC) can accelerate the production process of the CC and further contribute to improving the mechanical properties of the cement media. The purpose of this study is to promote the use of these cementitious composites by deepening the knowledge of their tensile properties and investigating the factors that may affect them. Therefore, 270 specimens (three types of stitch structure, two directions of the fabric, three water temperature values, five curing ages, with three repetitions) were made, and the tensile properties, absorbed energy, and the inversion effects were evaluated. The results showed that the curing conditions of the reinforced cementitious composite in water with temperature values of 7, 23, and 50 °C affect the tensile behavior. The tensile strength of the CCs cured in water with a temperature of 23 °C had the highest tensile strength, while 7 and 50 °C produced a lower tensile strength. The inversion effect has been observed in CC at 23 °C between 7 and 28 days, while this effect has not occurred in other curing temperature values. By examining three commercial types of stitches in fabrics and the performance of the reinforced cementitious composites in the warp direction, it was found that the structure of the “Tuck Stitch” has higher tensile strength and absorbed energy compared to “Knit stitch” and “Miss Stitch”. The tensile strength and fracture energy of the CC reinforced with “Tuck Stitch” fabric in the warp direction, by curing in 23 °C water for 7 days, were found to be 2.81 MPa and 1.65 × 103 KJ/m3, respectively. These results may be helpful in selecting the design and curing parameters for the purposes of maximizing the tensile properties of textile CAC composites

Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators

With a goal of achieving net-zero emissions by developing Smart Cities (SCs) and industrial decarbonization, there is a growing desire to decarbonize the renewable energy sector by accelerating green buildings (GBs) construction, electric vehicles (EVs), and ensuring long-term stability, with the expectation that emissions will need to be reduced by at least two thirds by 2035 and by at least 90% by 2050. Implementing GBs in urban areas and encouraging the use of EVs are cornerstones of transition towards SCs, and practical actions that governments can consider to help with improving the environment and develop SCs. This paper investigates different aspects of smart cities development and introduces new feasible indicators related to GBs and EVs in designing SCs, presenting existing barriers to smart cities development, and solutions to overcome them. The results demonstrate that feasible and achievable policies such as the development of the zero-energy, attention to design parameters, implementation of effective indicators for GBs and EVs, implementing strategies to reduce the cost of production of EVs whilst maintaining good quality standards, load management, and integrating EVs successfully into the electricity system, are important in smart cities development. Therefore, strategies to governments should consider the full dynamics and potential of socio-economic and climate change by implementing new energy policies on increasing investment in EVs, and GBs development by considering energy, energy, techno-economic, and environmental benefits

An improved dandelion optimizer algorithm for spam detection next-generation email filtering system

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), Chimp Optimization Algorithm (ChOA), Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO al-gorithm, which represents a promising approach for solving high-dimensional optimization prob-lems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning

Next-to-Leading order approximation of polarized valon and parton distributions

Polarized parton distributions and structure functions of the nucleon are analyzed in the improved valon model. The valon representation provides a model to represent hadrons in terms of quarks, providing a unified description of bound state and scattering properties of hadrons. Polarized valon distributions are seen to play an important role in describing the spin dependence of parton distributions in the leading order (LO) and next-to-leading order (NLO) approximations. In the polarized case, a convolution integral is derived in the framework of the valon model. The Polarized valon distribution in a proton and the polarized parton distributions inside the valon are necessary to obtain the polarized parton distributions in a proton. Bernstein polynomial averages are used to extract the unknown parameters of the polarized valon distributions by fitting to the available experimental data. The predictions for the NLO calculations of the polarized parton distributions and proton structure functions are compared with the LO approximation. It is shown that the results of the calculations for the proton structure function, $xg_1^p$, and its first moment, $\Gamma_{1}^p$, are in good agreement with the experimental data for a range of values of $Q^{2}$. Finally the spin contribution of the valons to the proton is calculated.Comment: 22 pages, 7 figures. Published in Journal of High Energy Physics (JHEP

Specialized Plant Metabolism Characteristics and Impact on Target Molecule Biotechnological Production.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkPlant secondary metabolism evolved in the context of highly organized and differentiated cells and tissues, featuring massive chemical complexity operating under tight environmental, developmental and genetic control. Biotechnological demand for natural products has been continuously increasing because of their significant value and new applications, mainly as pharmaceuticals. Aseptic production systems of plant secondary metabolites have improved considerably, constituting an attractive tool for increased, stable and large-scale supply of valuable molecules. Surprisingly, to date, only a few examples including taxol, shikonin, berberine and artemisinin have emerged as success cases of commercial production using this strategy. The present review focuses on the main characteristics of plant specialized metabolism and their implications for current strategies used to produce secondary compounds in axenic cultivation systems. The search for consonance between plant secondary metabolism unique features and various in vitro culture systems, including cell, tissue, organ, and engineered cultures, as well as heterologous expression in microbial platforms, is discussed. Data to date strongly suggest that attaining full potential of these biotechnology production strategies requires being able to take advantage of plant specialized metabolism singularities for improved target molecule yields and for bypassing inherent difficulties in its rational manipulation

Pion mass dependence of the Kl3K_{l3} semileptonic scalar form factor within finite volume

We calculate the scalar semileptonic kaon decay in finite volume at the momentum transfer $t_{m} = (m_{K} - m_{\pi})^2$, using chiral perturbation theory. At first we obtain the hadronic matrix element to be calculated in finite volume. We then evaluate the finite size effects for two volumes with $L = 1.83 fm$ and $L= 2.73 fm$ and find that the difference between the finite volume corrections of the two volumes are larger than the difference as quoted in \cite{Boyle2007a}. It appears then that the pion masses used for the scalar form factor in ChPT are large which result in large finite volume corrections. If appropriate values for pion mass are used, we believe that the finite size effects estimated in this paper can be useful for Lattice data to extrapolate at large lattice size.Comment: 19 pages, 5 figures, accepted for publication in EPJ

Typical m. triceps surae morphology and architecture measurement from 0 to 18 years: A narrative review.

The aim of this review was to report on the imaging modalities used to assess morphological and architectural properties of the m. triceps surae muscle in typically developing children, and the available reliability analyses. Scopus and MEDLINE (Pubmed) were searched systematically for all original articles published up to September 2020 measuring morphological and architectural properties of the m. triceps surae in typically developing children (18 years or under). Thirty eligible studies were included in this analysis, measuring fibre bundle length (FBL) (n = 11), pennation angle (PA) (n = 10), muscle volume (MV) (n = 16) and physiological cross-sectional area (PCSA) (n = 4). Three primary imaging modalities were utilised to assess these architectural parameters in vivo: two-dimensional ultrasound (2DUS; n = 12), three-dimensional ultrasound (3DUS; n = 9) and magnetic resonance imaging (MRI; n = 6). The mean age of participants ranged from 1.4 years to 18 years old. There was an apparent increase in m. gastrocnemius medialis MV and pCSA with age; however, no trend was evident with FBL or PA. Analysis of correlations of muscle variables with age was limited by a lack of longitudinal data and methodological variations between studies affecting outcomes. Only five studies evaluated the reliability of the methods. Imaging methodologies such as MRI and US may provide valuable insight into the development of skeletal muscle from childhood to adulthood; however, variations in methodological approaches can significantly influence outcomes. Researchers wishing to develop a model of typical muscle development should carry out longitudinal architectural assessment of all muscles comprising the m. triceps surae utilising a consistent approach that minimises confounding errors

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%