Review Highlights • A novel methodology for selecting distribution centers, incorporating the LMAW and RAWEC methods, is established.• The combination of specific steps in the MCDM method is proposed to enhance and streamline the decision-making process.

This research investigated energy-efficient (EE) retrofit strategies for a historically and culturally significant residential building complex, located in Sarajevo, Bosnia and Herzegovina. The objective was to evaluate existing building performance and propose delicate, EE retrofit strategies while preserving the original design character. The overarching objective was to demonstrate a framework through which historically and culturally significant buildings can be investigated for EE retrofitting. Using original construction drawings and current photographs, a 3D BIM model of a typical residential building was developed for analysis and energy simulations. Next, using Revit and Insight 360 simulations, the building's response to environmental conditions was evaluated. Thermal behavior and moisture resistance performance of typical facade systems were evaluated using WINDOW, THERM, and WUFI simulations. Lastly, a full-building energy model was developed in IES-VE software to simulate full-building performance. Results showed that while the conceptualization of this neighborhood paid careful attention to social and environmental factors and had implemented some of the most advanced passive and active technologies of that time, a typical residential building generally underperformed in all evaluated criteria. The proposed retrofit strategies, focusing on improving the building enclosure and implementation of EE mechanical systems, achieved 53% energy-use reduction and elimination of fossil-fuel energy sources.

This study delves into the intersection of music and machine learning, examining the performance of five algorithms—Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and K-Nearest Neighbours—in sentiment analysis for music. The goal is to systematically evaluate their effectiveness in decoding and classifying the emotional content of musical compositions. The selected algorithms represent diverse computational approaches, contributing to the overarching objective of understanding the intricate emotional landscape of music. A crucial aspect of this comparative analysis involves assessing the accuracy of these machine learning models, both before and after applying feature selection techniques. This step proves critical in enhancing the predictive capabilities of the models. The observed accuracy levels exhibit a dynamic range from 57% to 67%, unveiling subtle yet noteworthy performance variations among the chosen algorithms.

This paper concentrates on the analysis of spam messages as well as processing them by using machine learning models. The result of this research allows the reader to learn about the most important characteristics of spam messages in the form of the most common pattern used, which may assist in their detection as well as prevention of any kind of loss that may occur.

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.

With a focus on Culturally and Linguistically Diverse (CaLD) women, this article will discuss the underlying gender inequalities and stereotypes these women experience in Australian tertiary institutions through reflections of translingual discrimination. Translingual discrimination refers to the ideologies and practices that produce unequal linguistic power relationships between CaLD communities and dominant communities of the host society, focusing on the central role that language plays in the enduring relevance of discrimination disparity. Because of CaLD women's translingual identities, these groups experience such aspects of translingual discrimination as accentism, naming practices, linguistic subordination, deskilling, and stereotyping, which eventually affect their well‐being and economic security. CaLD women need a linguistically and culturally “safe space” where they will be supported and appreciated based on their capabilities and skills and not subjected to objectification, femininity evaluations, and derogatory actions. Opportunities for women should persist because, unfortunately, in men‐dominated fields, these opportunities are still necessary to support and include women.

Objective: The aim of the study was to determine the possible impact of the total daily amount of skim milk on the level of bilirubin and liver enzymes through regression analysis. Materials and Methods: The study included 63 Holstein-Friesian cows. They were formed in 3 groups, based on the amount of daily milk production. Peripheral blood was punctured, through which the activities of total bilirubin were analyzed (μmol/L), as well as liver enzymes: alanine aminotransferase – ALT (U/L), aspartate aminotransferase – AST (U/L), lactate dehydrogenase – LDH (U/L) and alkaline phosphatase – ALP (U/L). Results: The lowest concentration of total bilirubin in blood plasma was recorded in the group of cows that have the lowest daily milk production (1.295 ± 0.255 µmol/L), and highest concentration is in cows that produce the most milk (1.855 ± 0.159 µmol/L), but intergroup differences are not significant. Regression analysis found a statistically significant relationship between the amount of produced daily milk and the concentration of total bilirubin (R2=0.132, p=0.0050.05). Conclusion: The activities of bilirubin and liver enzymes in the examined cows were in physiological balance. This indicates that the cows on the farm are raised in modern and good zootechnical and feeding conditions. In such conditions, dairy cows are able to maintain blood composition and homeostatic integrity within physiological limits and adequate reproductive and productive capacity.

The wide range of supported services in modern telecommunication networks has increased the demand for highly secure means of communication. Common security frameworks based on the computational security model are expected to become insecure due to significant advances in quantum computing. Quantum key distribution (QKD), a new secret key agreement primitive, enables long-anticipated practical information-theoretical security (ITS). Over the past two decades, academic and industrial communities have devoted their time and resources to developing QKD-based networks that can distribute and serve ITS keys to remote parties. However, because the availability of QKD network testbeds to the larger research community is limited and the deployment of such systems is costly and difficult, progress in this area is noticeably slow. To address this problem and spur future development and education, we provide a valuable, unique tool for simulating a QKD network. The tool is essential to testing novel network management methodologies applied to large-scale QKD networks. The simulator model contained in the tool was validated by simulating a network with six nodes and three pairs of users. The results indicate that the designed functional elements operate correctly.

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.

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.

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.