This paper presents a multilevel thresholding method based on the multi-swarm particle swarm optimization with dynamic learning strategy and chaotic random inertia weight. This multilevel thresholding method is implemented using Kapur’s entropy. The performance of the presented method is validated on a set of standard test images. For each image and each considered number of threshold levels, the mean and standard deviation of Kapur’s entropy values are determined based on 30 independent applications of the thresholding method. The reported experimental results show that the presented method can be successfully applied across different images.

This paper presents a comparative analysis of two different natural exponent inertia weight strategies for particle swarm optimization in multilevel image thresholding. The considered multilevel image thresholding methods are based on Otsu’s between class variance. The multilevel thresholding methods are evaluated on different test images and for varying numbers of thresholds. The experimental results have demonstrated that the particle swarm optimization algorithm with the natural exponent inertia weight can be successfully employed to obtain threshold levels for different test images.

Abstract Objective  This study investigated biomechanical behavior of custom post core made of six different materials on the tooth with and without the ferrule under different occlusal load. Materials and Methods  Three-dimensional models of mandibular first premolar, with and without ferrule, reconstructed from micro-computed tomography image are restored with different custom post core and zirconia crowns. By using the finite element analysis, von Mises stress shown in MPa was measured under simulated axial and oblique load of 200 [N]. To compare the stress distribution, six different custom post core materials were chosen: zirconia, Ni-Cr alloy, gold alloy, glass fiber-polyether ether ketone, polyether ether ketone, and carbon fiber-polyether ether ketone. Results  Custom post cores with a higher modulus of elasticity showed higher measured stress in the posts, but less stress in dentin. Measured stress in custom post core under oblique loading was approximately three times higher compared with axial loading. Stress in custom post core and in dentin under both types of loads was slightly higher in teeth without ferrule effect. Conclusion  The use of custom cast post cores made of different alloys is recommended in restoration of endodontically treated teeth, with extensive loss of tooth structure especially in teeth without ferrule effect.

Quantum algorithms, represented as quantum circuits, can be used as benchmarks for assessing the performance of quantum systems. Existing datasets, widely utilized in the field, suffer from limitations in size and versatility, leading researchers to employ randomly generated circuits. Random circuits are, however, not representative benchmarks as they lack the inherent properties of real quantum algorithms for which the quantum systems are manufactured. This shortage of `useful' quantum benchmarks poses a challenge to advancing the development and comparison of quantum compilers and hardware. This research aims to enhance the existing quantum circuit datasets by generating what we refer to as `realistic-looking' circuits by employing the Transformer machine learning architecture. For this purpose, we introduce KetGPT, a tool that generates synthetic circuits in OpenQASM language, whose structure is based on quantum circuits derived from existing quantum algorithms and follows the typical patterns of human-written algorithm-based code (e.g., order of gates and qubits). Our three-fold verification process, involving manual inspection and Qiskit framework execution, transformer-based classification, and structural analysis, demonstrates the efficacy of KetGPT in producing large amounts of additional circuits that closely align with algorithm-based structures. Beyond benchmarking, we envision KetGPT contributing substantially to AI-driven quantum compilers and systems.

As landmines and other unexploded ordnances (UXOs) present a great risk to civilians and infrastructure, humanitarian demining is an essential component of any post-conflict reconstruction. This paper introduces the Minefield Observatory, a novel web-based datastore service that semantically integrates diverse data in humanitarian demining to comprehensively and formally describe suspected minefields. Because of the high heterogeneity and isolation of the available minefield datasets, extracting relevant information to determine the optimal course of demining efforts is time-consuming, labor-intensive and requires highly specialized knowledge. Data consolidation and artificial intelligence techniques are used to convert unstructured data sources and store them in an ontology-based knowledge database that can be efficiently accessed through a Semantic Web application serving as the Minefield Observatory user interface. The MINEONT+ ontology was developed to integrate diverse mine scene information obtained through non-technical surveys and remote sensing, such as aerial and hyperspectral satellite imagery, indicators of mine presence and absence, contextual data, terrain analysis information, and battlefield reports. The Minefield Observatory uses the Microdata API to embed this dataset into dynamic HTML5 content, allowing seamless usage in a user-centric web tool. A use-case example was provided demonstrating the viability of the proposed approach.

The primary aim of this study was to examine the body composition of young female rhythmic gymnasts and draw comparative insights from the collected data. Additionally, the results were compared with other studies that shared identical or analogous research objectives. 36 girls divided into three age groups participated in the research: Group 1 (age: 8.57 ± 0.66), group 2 (age: 10.32 ± 0.48) and group 3 (age: 12.90 ± 0.74). Body composition was determined using an octopolar bioelectrical impedance analysis device InBody 370. Among the various variables assessed, the groups exhibited statistically significant differences across most parameters, with exception of body fat (%). Our results, consistent with prior research studies, revealed that our participants also exhibited the characteristic body composition commonly observed in rhythmic gymnastics. These findings suggest that rhythmic gymnastics primarily affects body weight and the percentage of body fat tissue, while also contributing to the development of muscle mass over years of training and sports experience.

Practitioners and decision-makers often face difficulties in selecting and prioritizing effective strategies to address challenges to sustainable urban transportation development. Although there has been considerable research conducted on the subject, the Tanzanian context, which is greatly affected by social and environmental problems, has received inadequate attention. Therefore, this study intends to bridge this gap by pinpointing the obstacles to sustainable urban transportation and proposing the most appropriate strategies to tackle them. The study proposes seven strategies and determines five criteria to prioritize them. To accomplish this, the study proposes a novel Fermatean fuzzy-based intelligent decision support model to assess the criteria weights and prioritizes strategies based on the weighted criteria. The study validates the proposed methodology by conducting a sensitivity analysis, which indicates that restricting car use (A5), improving sector coordination (A1), and conducting extensive research on transportation issues (A7) are the top three strategies for promoting sustainable urban transportation. The study’s findings hold significant value in providing urban transportation planners with helpful guidance to develop optimization techniques that can improve transportation systems.

Given the growing shares of renewable energy sources in the grids, the interest in energy storage systems has increased. The role of pumped hydro energy storage systems as flexible solutions for managing peak and off-peak prices from nuclear and fossil power plants in previous systems is now revitalized in the liberalized systems, with a volatile generation of wind and solar energy. Thus, understanding of the patterns behind the economics of energy storage is crucial for the further integration of energy storage in the grids. In this paper, the factors that impact the economic viability of energy storage in electricity markets are analyzed. The method of approach used in this study considers the electricity market price distribution, full load hours, the total costs of energy storage, and linear regression analysis. Using revenues from arbitraging a 10-megawatt (MW) pumped hydro storage system in the Western Balkans, resulting from the electricity market price distribution and the analysis of the total costs of storage, an econometric model is created. This model shows the impacting factors of energy storage development in the context of the rising renewables sector. Research shows that the previous hypothesis about the integration of energy storage systems in proportion to the increase in shares of renewables in the grids is incorrect. There is a significant correlation between energy storage revenues, the dependent variable, and the independent variables of hydro, wind, and solar generation. The conducted analysis indicates the future arbitraging opportunities of pumped hydro energy storage systems and provides useful insights for energy storage investors and policymakers. During the transitional period, until the deployment of renewables changes the effects of fossil power plants, energy storage price arbitrage is profitable and desirable for 500, 1000, and 2000 full load hours in the Western Balkan region. Despite the need for flexibility, with more renewables in the grids, large-scale energy storage systems will not be economically viable in the long run because of “revenue cannibalization”.

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.

Background : Abnormal uterine bleeding (AUB) is one of the most common health problems encountered in women of the peri-menopausal age group. The aim of this study was to investigate the characteristics of functional AUB in perimenopausal women, and to identify predictors of the occurrence of functional AUB in perimenopausal women. Methods : This was a cross-sectional study that included 45 women with complaints of AUB admitted to the gynecology ward in the University Hospital in Foča, and 45 women without AUB who underwent a gynecological examination in the Primary Health Center in Foča, Bosnia and Herzegovina. We included women with AUB who had a normal ultrasound finding without obvious genital or cervical lesions, and who were not on hormonal therapy. The existence of AUB was determined on the basis of a clinical examination by a gynecologist and existing medical documentation. A logistic regression model was used to assess the possible relationship between the occurrence of the functional AUB and the risk factors for its occurrence. Results : The mean patient age was 48.38 ± 2.40 years. There was no significant difference in age, frequency of physical activity, or previous use of oral contraceptives between the two groups of patients divided by the presence of the functional AUB in perimenopause. The logistic regression model showed that age ( p = 0.008), high values of body mass index (BMI) ( p = 0.001), consumption of alcohol and cigarettes on a daily basis ( p = 0.001), and hypertension ( p = 0.046) represented significant risk factors associated with functional AUB in perimenopausal women. Conclusions : During routine exams of women approaching menopause, gynecolo-gists should emphasize the harms of smoking and alcohol use, as well as the significance of non-pharmacological and pharmacological methods for the treatment of obesity and hypertension.

As an oxygen carrier and a strong oxidising agent, ammonium nitrate can create an explosive mixture when mixed with organic material. A typical example is the mixture of ammonium nitrate and fuel oil (ANFO), which is the most used explosive for civilian applications. In this work, we studied the detonability and detonation properties of mixtures of ammonium nitrate with recycled rubber and hay. The main goal of this study was to determine the optimal volume ratio of ammonium nitrate and organic materials in terms of achieving the best performance (working capacity). Using small experiments, it was determined that the maximum burst velocity for the ammonium nitrate/hay mixture is achieved at 8% hay by volume, while the maximum burst velocity for the ammonium nitrate/rubber mixture is achieved at 15% rubber by volume. A thermochemical calculation has shown that the maximum detonation heat is achieved at the zero oxygen balance at the volume ratios of 89.2/10.8 for AN/Rubber and 72.85/27.15 for AN/Hay.

We aimed to assess the extent of integration of non‐communicable disease (NCD) assessment and management in HIV clinics across Europe.

: This paper describes the possibility of applying heuristic methods for parameter optimization in the TCP protocol. The proposed concept provides for a TCP protocol adjusting its parameters for greater efficiency through testing the network state and adapting accordingly. This can be achieved through careful analysis of the network state both before and during data transfer connections. The proposed solution introduces an innovative approach, incorporating the possibility of self-learning and self-adjusting capabilities. This sophisticated algorithm should define the next parameter values in terms of finding optimal parameter settings. Each TCP connection plays a crucial role as iteration in the process of finding the optimal solution. The concept focuses on calculating TCP parameter values at the network ends in order to optimize network traffic and to maximize the use of network resources. The approach has been tested on a dedicated test platform, validating its potential for verifying the network protocols functionality and for optimizing their parameters. The proposed solution, here referred to as the modified TCP, showed better performance compared to other versions of the TCP protocol. Notably, even under heavy traffic loads on links, the results for the modified TCP consistently outperform the standard TCP, delivering results that are several times better.

This study explores the anatomical relationship between iliac blood vessels and the lower lumbar spine during supine and prone patient positions. The average height of participants was 174.02 cm ± 9.01, while the average weight was 80.38 kg ± 13.48. Body mass index (BMI) analyses showed differences (p = 0.002), with 34.7% classified as normal weight, 53.1% as overweight, and 12.2% as moderately obese. The study examined the distances between iliac arteries and veins in relation to intervertebral anterior and posterior disc contours. Patient positioning significantly affected these measurements at both L4/L5 and L5/S1 levels. The findings highlight the critical influence of body position on anatomical relationships in the context of lower lumbar spine surgery. The study underscores the importance of preoperative awareness of vascular anatomy to prevent iatrogenic lesions during spine surgery, contributing valuable insights for optimizing surgical approaches and minimizing complications in spine surgery, particularly microdiscectomy.