There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the ...process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu’s method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement.
Due to the speedy development of mobile communication devices, the traditional cloudlet computing networks struggle to manipulate the huge collected data from these devices. Mobile computing has been ...developed to overcome the access delay (AD) and the workload balance (WB) in the traditional cloudlet networks by transferring the computing process from the remote servers in the cloudlet to the edge servers (ESs) allocated closer to the mobile users. The searching for the optimal place to allocate the ESs in mobile edge computing is the main challenge and it considers an NP-hard problem. In this work, we propose a new objective function with threefold objectives, the AD between base stations and ESs, the WB among various controlled ESs, and the energy consumption (EC) of ESs. Therefore, we formulate the edge server allocation problem as a multi-objective optimization problem that requires an efficient optimizer algorithm for solving it. To minimize the proposed objective function, we present an effective fitness-dependent optimizer (EFDO) algorithm and test it on Shanghai Telecom’s BS dataset. To investigate the efficiency of the proposed EFDO algorithm, we compare it against seven algorithms taken from literature (e.g., K-means, Random, TopFirst, PSO, CSA, Jaya, and JS). The numerical results verified the superiority of the proposed algorithm in terms fitness function reached up to 14.84%, 18.56%, 17.28%, and 13.48% when compared with PSO, CSA, Jaya, and JS, respectively with various number of base stations and edge servers. Although k-means has been achieved the least average AD among compared algorithms which considered only the distance between the BS and ES, the proposed algorithm has achieved the superior results in WB ranged from 12 to 72% when compared against other algorithms. In addition, it has gained the least average EC among the compared algorithms.
Purpose
Gallic acid (GA) is a polyphenolic compound with proven efficacy against hepatic fibrosis in experimental animals. However, it suffers from poor bioavailability and rapid clearance that ...hinders its clinical investigation. Accordingly, we designed and optimized reverse micelle-loaded lipid nanocapsules (RMLNC) using Box-Behnken design that can deliver GA directly into activated-hepatic stellate cells (aHSCs) aiming to suppress hepatic fibrosis progression.
Methods
GA-RMLNC was prepared using soft energy, solvent free phase inversion temperature method. Effects of formulation variables on particle size, zeta potential, entrapment efficiency (EE%) and GA release were studied.
In-vivo
biodistribution of GA-RMLNC in rats and
in-vitro
activities on aHSCs were also explored.
Results
Nano-sized GA-RMLNCs (30.35 ± 2.34 nm) were formulated with high GA-EE% (63.95 ± 2.98% w/w) and physical stability (9 months). The formulated system showed burst GA release in the first 2 h followed by sustained release profile.
In-vivo
biodistribution imaging revealed that RMLNC-loaded with rhodamine-B accumulated mainly in rats’ livers. Relative to GA; GA-RMLNC displayed higher anti-proliferative activities, effective internalization into aHSCs, marked down-regulation in pro-fibrogenic biomarkers’ expressions and elevated HSCs’ apoptosis.
Conclusions
These findings emphasize the promising application of RMLNC as a delivery system in hepatic fibrosis treatment, where successful delivery of GA into aHSCs was ensured via increased cellular uptake and antifibrotic activities.
COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from ...50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients.
In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process ...divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.
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•Zr(IV), Hg(II) and U(VI) complexes of albendazole.•Spectrophotometry determination of Zr(IV), Hg(II) and U(VI) as albendazole complexes in many samples.•The microbial and molecular ...docking studies.
Spectrophotometry was used to determine trace amounts of Zirconium(IV), Mercury(II) and Uranium(VI) in environmental, biological, pharmaceutical and industrial samples. The determination depend on the complexation reactions between albendazole reagent and metal ions Zr(IV), Hg(II) and U(VI) at 555 nm, 485 nm and 510 nm, respectively. The experimental conditions were explored to reach the optimum conditions for albendazole-metal ions interaction, including detection of a suitable wavelength, medium (pH), reagent concentration, surfactants effect, reaction time and temperature. Under optimum conditions, the complexes displayed apparent molar absorptivities of 0.8350 × 104, 0.6210 × 104 and 0.7012 × 104 L mol−1 cm−1; Sandell’s sensitivity of 0.01092, 0.03230 and 0.03394 µg cm−2 and with linearity ranges of 1.0–120.0, 3.0–200.0 and 1.0–150.0 µg mL−1 for the developed methods, respectively. Furthermore, Elemental analysis, thermal analysis (TGA, DTG), IR, 1HNMR, spectroscopies, electrical molar conductivity and magnetic moment measurements were used to determine the structures and characteristics of the complexes. A careful examination of the IR spectra revealed that the ligand interacted with all of the metal ions described as a bidentate via the oxygen of the carbonyl of the ester moiety and the nitrogen atom of the heterocyclic CN group. An octahedral geometry for Zr(IV), Hg(II) and U(VI) complexes has been postulated based on magnetic and electronic spectrum data. The band gap values indicated that these complexes were semi-conductors and belong to the same class of extremely effective solar materials. The albendazole ligand and its complexes have been biologically tested against a variety of bacterial and fungal strains, and molecular docking studies have been conducted to evaluate the optimal binding site and its inhibitory action.
Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information ...filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-
N
recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.
Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization ...algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal.
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