The principal challenge addressed in this paper is modifying the standard particle swarm optimization algorithm to achieve improved multilevel image thresholding performance. In this paper, a multilevel image thresholding method that relies on Kapur's entropy and the improved particle swarm optimization algorithm is presented. The improved particle swarm optimization algorithm employs a particular nonlinearly decreasing inertia weight strategy and Gaussian mutation. The performance of the considered multilevel image thresholding method is assessed on five test images. The experimental results demonstrate the successful utilization of the improved particle swarm optimization algorithm for determining image thresholds across different images. This algorithm is shown to enhance the multilevel image thresholding performance over the standard particle swarm optimization algorithm.

We propose a new variant of the Adam optimizer called MicroAdam that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this by compressing the gradient information before it is fed into the optimizer state, thereby reducing its memory footprint significantly. We control the resulting compression error via a novel instance of the classical \emph{error feedback} mechanism from distributed optimization in which *the error correction information is itself compressed* to allow for practical memory gains. We prove that the resulting approach maintains theoretical convergence guarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MicroAdam can be implemented efficiently on GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MicroAdam provides practical convergence competitive to that of the uncompressed Adam baseline, with lower memory usage and similar running time. Our code is available at https://github.com/IST-DASLab/MicroAdam.

Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a “component disorder“ of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities.

The expansion of the open-pit exploitation of mineral raw materials, and especially the energy resources of fossil fuels, makes open-pit coal mines spatially dominant objects of large mining basins. Exploitation activities are accompanied by negative ecological impacts on the environment, which requires the integral planning, revitalization, reclamation, and rehabilitation of the disturbed area for human use in the post-exploitation period. The post-exploitation remediation and rehabilitation of open-pit mining areas and disposal sites, i.e., space disturbed by mining activities and accompanying facilities, are complex synthetic multidisciplinary multiphase engineering project tasks. In this paper, a hybrid fuzzy MCDM model (Multiple-Criteria Decision-Making) was developed for the selection of a reclamation solution for the Tamnava-West Field open-pit mine. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define the weights of 12 criteria of different structures used in the evaluation of reclamation solutions. The Fuzzy ROV (Range of Value) method was applied to select the reclamation solution from a total of 11 solutions previously obtained using a process approach. The results of the hybrid IMF SWARA—Fuzzy ROV model show that forestry is the best solution for the Tamnava-West Field open-pit mine. After the results had been obtained, verification analyses of the proposed model were performed and the best stable proposed reclamation solution was determined.

BACKGROUND: Left atrial stiffness index (LASI), defined as the ratio of early diastolic transmitral flow velocity/lateral mitral annulus myocardial velocity (E/e’) to peak atrial strain, reflects reduced left atrial (LA) compliance and represents an emerging marker that can be used for noninvasive measurement of fibrosis of LA in patients with mitral regurgitation (MR). OBJECTIVE: To investigate the impact of chronic MR in children and adolescents on the remodeling and function of the LA, quantified through strain parameters and diastolic function. METHODS: The study included fifty patients ( n = 50) diagnosed with primary and secondary chronic MR lasting at least 5 years. The echocardiographic recordings were performed by a third party, two cardiologists actively engaged in echocardiography on a daily basis. RESULTS: Older participants had higher values of the LASI ( r = 0.467, p = 0.001). Participants with higher LASI values had a smaller LA reservoir ( r = 0.784, p = 0.0001) and smaller LA conduit values ( r = - 0.374, p = 0.00). Participants with higher LASI values had a larger LA diameter ( r = 0.444, p -value= 0.001) and higher average E/e’ ratio ( r = 0.718, p = 0.0001). There was a significant difference ( p = 0.04) in the LASI among participants based on the MR jet area ( < 20.85% ⩾ 20.85%), LASI was higher in participants with an area greater than 20.85%. Differences in other parameters such as LA reservoir, LA conduit, LA contractile were not statistically significant. CONCLUSION: Increased LA stiffness is associated with diminished atrial compliance and reservoir capacity, and LASI has a potential to as an early marker for assessing disease severity and progression in pediatric MR.

Background: The primary goal of this study was to investigate the relationship between body composition and motor coordination performance, and the secondary goal was to determine sex differences in body composition and motor coordination of preschool children. Methods: Forty-eight children (23 boys and 25 girls) underwent assessments for body composition and motor coordination using the Köperkoordinationstest für Kinder (KTK). Results: Linear regression analysis revealed significant associations between body composition and motor coordination in boys (p < 0.05) but not in girls. In boys, Body height (p = 0.01), Total muscle mass (p = 0.03), Total fat (p = 0.03), and Total water (p = 0.02) show statistically significant influence on single-leg jumps. Similar results were obtained for lateral jumps where there was a statistically significant influence of Body height (p = 0.01), Total muscle mass (p = 0.03), and Total water (p = 0.02). Interestingly, predictive variables showed no statistically significant influence on KTK overall score in boys (p = 0.42) nor in girls (p = 0.90). Conclusions: The predictive system of morphological variables demonstrated significance only among boys in this age group and sample. Girls outperformed boys due to early maturation, resulting in better average KTK scores.

Physical chemical milk is an emulsion of milk fat in an aqueous solution of proteins, milk sugar and mineral salts. The high molar conductivity of goat milk samples compared to cow's milk indicates a high content of mineral substances. That goat milk is rich in total proteins is also indicated by the protein content in the samples, which are higher than the cow's milk samples. However, higher fat content was recorded in cow's milk samples, which also results in higher surface tension of cow's milk. The freezing point and refractive index of goat milk are higher compared to literature data and cow milk samples. The acidity of goat's milk comes from the acidic properties of casein, citrate, phosphate, etc. it is lower than cow's milk and is in accordance with literature data. The viscosity of pasteurized goat's milk at all temperatures is also higher than that of cow's milk.

This study aims to investigate the influence of muscle mass on jump height based on the stage of biological maturation. The total sample consisted of 71 male athletes with three years of minimum training experience. The athletes were divided into three groups based on biological maturation: PrePHV, MidPHV, and PostPHV. Vertical jump height was assessed using three tests: the countermovement jump (CMJ), the countermovement jump with arm swing (CMJwas), and the squat jump (SJ). The results of the interaction between muscle mass percentage (MM) and peak height velocity (PHV) indicate that the effect of MM on vertical jump variables is greater in the PrePHV and MidPHV groups compared to the PostPHV group. For the PrePHV and MidPHV groups, there was a significant increase in CMJ [b=.83, t(22)=3.77, p=.001 and b=.92, t(14)=3.70, p=.002, respectively] and SJ [b=1.11, t(22)=4.45, p< .001 and b=1.06, t(14)=3.51, p=.003, respectively] when muscle mass percentage increased by one unit, while no significant increments were apparent for the PostPHV group [b=0.71, t=1.98, p=.058 and b=0.48, t(28)=1.65, p=.111, respectively]. Additionally, when muscle mass percentage increased by one unit, the CMJwas performance significantly increased in the PrePHV [b=1.48, t(22)=4.68, p<.001], MidPHV [b=1.15, t(14)=4.59, p<.001], and PostPHV [b=.97, t(28)=2.52, p=.018] groups. This study substantiates muscle mass as an important predictor of explosive power, demonstrating a more pronounced impact in the PrePHV and MidPHV relative to the PostPHV group. The study points out the importance of considering biological maturation when understanding the relationship between muscle mass and explosive power performance in young athletes.

To bring robots into human everyday life, their capacity for social interaction must increase. One way for robots to acquire social skills is by assigning them the concept of identity. This research focuses on the concept of \textit{Explanation Identity} within the broader context of robots' roles in society, particularly their ability to interact socially and explain decisions. Explanation Identity refers to the combination of characteristics and approaches robots use to justify their actions to humans. Drawing from different technical and social disciplines, we introduce Explanation Identity as a multidisciplinary concept and discuss its importance in Human-Robot Interaction. Our theoretical framework highlights the necessity for robots to adapt their explanations to the user's context, demonstrating empathy and ethical integrity. This research emphasizes the dynamic nature of robot identity and guides the integration of explanation capabilities in social robots, aiming to improve user engagement and acceptance.

This study explored body composition differences among elite Serbian judokas across three weight categories: lightweight, middleweight, and heavyweight. Thirty-two male judokas from Serbia’s national team participated, with body composition metrics assessed using the Segmental Body Composition Analyzer InBody 720. Parameters measured included lean body mass percentage, skeletal muscle mass percentage, body fat percentage, waist-to-hip ratio, visceral fat area, bone mineral content, lean body mass index, and fat mass index. Heavyweights exhibited significantly lower lean body and skeletal muscle mass percentages than lightweights and middleweights (p < 0.001). Additionally, heavyweights had significantly higher body fat percentage, waist-to-hip ratio, and visceral fat area (p < 0.001). Bone mineral content was significantly higher in heavyweights compared to lightweights and middleweights (p < 0.001) and also differed significantly between lightweights and middleweights (p < 0.01). Lean body and fat mass index were significantly higher in heavyweights than lighter categories (p < 0.001). These findings highlight the importance of tailored training and nutritional strategies to optimize body composition for enhanced performance and health outcomes in judo. Effective weight management, focusing on reducing fat while maintaining muscle mass, is critical, particularly for heavyweight athletes. The study's methodology and representativeness align with international standards, supporting the applicability of findings to broader populations. Future research should include larger, more diverse samples and longitudinal designs to understand body composition dynamics over time and incorporate performance metrics for a holistic view of success factors in judo. Implementing these strategies will enhance athletic performance and promote long-term health and well-being among judokas.

As the global population continues to age, understanding and addressing the complex interplay between physical activity (PA) and quality of life (QoL) among old people is becoming increasingly imperative. The aim of this study was to determine whether the level of PA is related to the QoL in elderly men. Using a set of eight variables of PA and four for quality of life assessment, an evaluation of physical activity and quality of life was performed on a sample of 666 senior men (67.37±5.68 years). The level of PA was measured using the IPAQ questionnaire, while the QoL was evaluated by the World Health Organization Questionnaire (WHOQoL-BREF). A canonical correlation analysis was conducted to identify any relationships. Statistical significance was set at p< .01. The results showed that statistically significant relationships were found between moderate PA and the Environmental Health domain of QoL (Sig.= .000). Additionally, relationships were found between overall Walking activity, total PA, and Leisure Physical Activity and Physical and Psychological Health, as well as Social Relationships (Sig.= .003). This study confirmed that different domains of PA are related to the quality of life in elderly men.

Summary This study investigates earnings management and its determinants in the agricultural and manufacturing sectors, with the aim of promoting the quality of financial reporting. The sample includes 1,381 actively operating companies in AP Vojvodina, Republic of Serbia, in the period from 2019 to 2021. Earnings management activities are identified using discretionary accruals computed with modified Jones model. Panel data analysis reveals that profitability and company size exert a positive and statistically significant influence on earnings management practices. Conversely, sales growth demonstrates a negative and statistically significant impact on earnings management. Furthermore, the analysis across the studied years indicates statistically significant differences in the prevalence of earnings management practices. However, the study found no significant differences in earnings management practices between the agriculture and manufacturing sectors. The significance of this study lies in its potential to provide valuable insights for investors, regulators, and financial analysts, helping them in making informed decisions. Moreover, it contributes to refinement of financial reporting standards and enforcement mechanisms, and enables a more accurate assessment of the financial health and performance of companies in both industries. The research also endeavors to identify sector-specific factors influencing earnings management dynamics, with the aim of enhancing transparency and optimizing decision-making processes in the financial environment.

In this paper, we demonstrate and introduce a novel Situational Awareness with Event-driven Network Programming Edge Network Application (EdgeApp), designed to optimize network resource utilization during vessel teleoperation in congested port areas. The demonstration is conducted on an open real-life EdgeApp 5G Standalone (SA) and beyond testbed situated at the port of Antwerp-Bruges. Through this showcase, we demonstrate how 5G and beyond services, utilizing an open 5G SA testbed, can enhance vessel teleoperation. The proposed solution dynamically adjusts network configurations, allowing for lower-quality camera feeds during vessel autonomy and higher-quality feeds when in the teleoperation zone. The practical application and benefits are exemplified through visual representations within the testbed environment.

The evolving landscape of 5G Standalone (SA) and beyond networks is being increasingly focused on vertical industries. To unlock the full potential for verticals, it is important to tightly integrate Edge Network Applications (EdgeApps) tailored to vertical use cases, with the 5G SA network, while allowing them to interact with each other at the same time. Such interaction enables more transparency in expressing Quality of Service (QoS) demands from verticals in the form of intent while hiding the network complexity from them. In this paper, we propose two EdgeApps, which by interacting with both User Equipments (UEs) and 5G SA network, are becoming aware of network quality (quality-awareness) and context around UEs (situational-awareness). Such awareness is also enabled by initiatives such as GSMA Open Gateway and CAMARA, where network and IT functionality are exposed to application developers through standardized Application Programming Interfaces (APIs) that abstract the underlying complexity on the telco and IT systems. In this paper, we utilize the Nokia Network as Code (NaC) platform that exposes the capabilities of the Telenet 5G SA network through CAMARA APIs allowing our EdgeApps to dynamically and in real-time create events that trigger changes in QoS levels required by vertical applications. The paper showcases this concept through a case study within the Transport and Logistics (T&L) sector, which is focused on improving the safety and efficiency of remote vessel operation in busy port environments. The overall solution is deployed and tested on the Antwerp 5G SA testbed, which consists of the UEs (vehicle and vessel) and 5G network infrastructure in the Port of Antwerp-Bruges, as well as the 5G edge where EdgeApps are running. This research contributes to the broader objective of incorporating diverse industrial applications into the 5G and beyond ecosystem, showcasing tangible benefits for vertical industries.

Epilepsy represents a neurological disorder of the brain characterized by repeated seizures. These are sudden abnormality in the brain’s electrical activities that temporarily affect normal brain function. Electroencephalogram (EEG) is one of the main diagnostic tools for monitoring the brain activity of patients with epilepsy. Typically, the detection of epileptic activity is carried out by an expert by analyzing the EEG recordings, but this is a difficult, error prone and time-consuming task. In order to get timely and accurate automatic detection of seizure, various approaches based on both conventional and deep learning techniques were proposed in the literature. The aim of this paper is to present a framework for the automatic detection of epileptic seizure based on the functional connectivity matrix obtained from EEG signals and deep learning. Convolutional neural networks (CNN) were employed because of their capability to learn patterns of neural activities based on brain connectivity represented by connectivity matrix. Obtained results are very promising indicating a potential of this approach as an efficient tool for automated seizure detection based on EEG data.