This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approach is the usage of burs of free configuration space ($\mathcal{C}$-space) as adaptive motion primitives within the graph search algorithm. Due to their feature to adaptively expand in free $\mathcal{C}$-space, burs enable more efficient exploration of the configuration space compared to fixed-sized motion primitives, significantly reducing the time to find a valid path and the number of required expansions. The algorithm is implemented within the existing SMPL (Search-Based Motion Planning Library) library and evaluated through a series of different scenarios involving manipulators with varying number of degrees-of-freedom (DoF) and environment complexity. Results demonstrate that the bur-based approach outperforms fixed-primitive planning in complex scenarios, particularly for high DoF manipulators, while achieving comparable performance in simpler scenarios.

U radu su razmatrana trenutna i konsolidacijska slijeganja kvadratnog temelja za dvije različite geomehaničke sredine. Prvi slučaj je pjeskovito tlo ispod kojeg se nalazi sloj gline, a druga geomehanička sredina je pjeskovito tlo u mješavini sa šljunkom te ojačano cementnim prahom (30%) i staklenim vlaknima (1%) ispod kojeg se nalazi glina. Dobiveni rezultati slijeganja pokazuju dobru stabilizaciju tla i povoljne geomehaničke karakteristike ojačanog tla koji se ogledaju u manjoj vrijednosti ukupnog slijeganja temelja koja zadovoljava maksimalnu dopuštenu vrijednost definiranu Eurocode-om. U radu su prikazana slijeganja kroz vremensko razdoblje od 120 dana, prikazano je konačno, dugoročno slijeganje ispod temelja kao i diferencijalni omjer (δ/L) slijeganja temelja. Proračun trenutnog slijeganja vršen je prema teoriji elastičnosti odnosno konsolidacijsko slijeganje prema Terzaghi-ju.

Abstract Consumers of agricultural products represent a sensitive category of society, therefore communication with them is particularly important. Communication is influenced by a large number of factors, and in this regard, making the right decision about the way to communicate is an extremely complex job. In previous research, the emphasis was placed mostly on the entire supply chain, much more than on the method of choosing communication with end consumers of agricultural products. Therefore, this research represents an attempt to make an additional contribution to rational decision-making in this part of the agricultural supply chain. In this paper, the selection of criteria used in the process of communicating with consumers in a country was carried out by applying expert fuzzy multi-criteria decision-making. The innovative fuzzy method SiWeC (Simple Weight Calculation) was applied to evaluate ten given criteria with the aim of their selection. The subject of the research were several agricultural companies and the experts were six experts from the marketing department of the companies in question. The results show that the method of communication itself is the best evaluated criterion, as well as the delivery time, i.e. the impact of delivery speed on the demand for a particular agricultural product. Uncertainty in decision-making is reduced by applying fuzzy decision-making logic, and the results provide the basis for future research that should be directed at selecting the most favorable methods of communication when products of this type are in question, as well as the development of the existing methodology, which in this case has proven to be adequate.

Purpose Diabetic kidney disease (DKD) significantly affects health and healthcare costs due to chronic kidney disease complications. Given asprosin’s potential as a biomarker for disease progression, we conducted the first systematic review and meta-analysis on its relationship with DKD in adults with type 2 diabetes mellitus (T2DM). Methods PubMed, Embase, Cochrane, and Web of Science were systematically searched. Standard mean differences (SMD) with 95% confidence intervals (CI) and Fisher’s Z transformation were used to examine the relationship between asprosin and DKD. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) and its version for cross-sectional studies. Heterogeneity (I² > 50%) was analyzed with a random-effects model. Results Six studies (n = 1340) were included. Meta-analysis results indicated that T2DM patients with DKD (micro/macroalbuminuria) had significantly higher circulating asprosin levels than normoalbuminuric T2DM patients (SMD: 1.5, 95% CI: 0.69–2.32, p = 0.0003). Meta-analysis of correlation revealed a positive association of asprosin with urinary albumin excretion ratio (UACR) (Fisher’s Z = 0.4; 95% CI: 0.240–0.554, p < 0.001) and body mass index (BMI) (Fisher’s Z = 0.17; 95% CI: 0.036–0.301, p = 0.013), and a negative association with estimated glomerular filtration rate (eGFR) (Fisher’s Z = −0.35; 95% CI: −0.471 to −0.239, p < 0.001). Conclusion Asprosin is elevated in T2DM patients with pre-DKD (early stage DKD) and DKD and correlates with key markers of disease severity. Additional research is required to better understand the pathophysiological mechanisms of asprosin and its role in DKD.

The conducted research aimed to determine the dietary habits of elderly people. The research was conducted on a sample of 237 respondents differentiated by gender, 133 males and 104 females and by age 65 -89 years. Data processing was done in the SPSS program and descriptive and comparative statistics were used to obtain data. Descriptive parameters were created for the analysis of the factual state of the respondents. The collected data were analyzed using the Microsoft Excel 2007 tool, and are presented in tables. It is evident that the health status of the respondents worsens with age, and dietary habits change in a positive sense with age. Given that the world's population is aging, it is necessary to emphasize attention to certain needs and challenges faced by many older people. Nutrition is an important element of health in the elderly population and affects the aging process. Although it is a study on a relatively small number of subjects, in comparison with a larger European study, it becomes relevant for proving the present problem of malnutrition among elderly patients. It is necessary to take all measures to increase the awareness of health workers about this problem in order to successfully prevent it or stop it in time.Looking at the entire sample of respondents globally, we can conclude that overall, a significant number of respondents do not pay enough attention to the structure of foods in their diet and water consumption.In line with previous research, the results of this study highlight the importance of dietary habits in creating a healthy lifestyle and preventing chronic non-communicable diseases. Key words:eating habits, age, respondents, differences, lifestyle

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that affects the entire personality and is most often diagnosed during school age. The Tomatis Method involves neurosensory brain stimulation through unique listening programs. The aim of this study was to examine the impact of the Tomatis Method on the attention of a child with ADHD. The listening program was tailored to the child's specific needs and was administered by a certified Tomatis practitioner. Each treatment cycle lasted 14 days. The assessment was conducted at four time points: before and after the first cycle, and before and after the second cycle of the Tomatis treatment. A software-based assessment tool specifically designed to evaluate components of executive functions was used to measure attention. The collected data were processed using IBM SPSS Statistics 21 software for statistical analysis. Basic statistical parameters were calculated. The results showed improvements in attention-related abilities, including increased response accuracy and faster reaction times. Key words:Tomatis Method, ADHD, attention.

Menopause represents an inevitable transition in a woman’s life, presenting with vasomotor symptoms, mood disorders, sleep difficulties, and prolonged risks such as osteoporosis and cardiovascular diseases. Hormone replacement therapy emerged as the cornerstone of menopausal management, particularly for alleviating symptoms and preventing postmenopausal osteoporosis. However, findings from the Women’s Health Initiative (WHI) study in 2002 highlighted increased risks of breast cancer, cardiovascular disease, and stroke associated with hormonal replacement treatment, leading to a significant global decline in its usage. Consequently, numerous women were deprived of essential therapy, endangering their health and quality of life. This review presents the findings of the WHI study, discusses its methodological errors, and evaluates its benefits and harms. We explore landmark studies that have reestablished the benefits and risks of hormone replacement therapy over the past two decades. Guidelines supported by these findings are presented in this review. Despite advancements, public perception of hormone replacement treatment remains influenced by outdated findings, limiting its utilization in many regions, especially in developing countries. Our objective is to provide evidence that misconceptions about hormone replacement therapy significantly impact women’s general health and quality of life, as well as to clarify the short-term and long-term impacts of hormone replacement therapy. We conclude that hormonal replacement treatment is effective and safe when administered according to established guidelines. Access to information, coupled with knowledgeable physicians who consistently interact with women, is as vital as the contributions of menopause healthcare specialists. Conflicting information from outdated professionals can likely lead to treatment failure in patients. Keywords: menopause, women’s health, estrogens, progestins, quality of life

This study investigated the potential of high-voltage electrical discharge (HVED), as a green, non-thermal extraction technology, for recovering polyphenols from winter savory (Satureja montana L.). Key process parameters, including frequency (40, 70, 100 Hz) and extraction time (1, 5, 15, 30, 45 min), were optimized, using water as a solvent and maintaining a constant solid-to-liquid ratio of 1:100 g/mL. The extracts were characterized for total polyphenol content (TPC), total flavonoid content (TFC), and antioxidant activity (DPPH, ABTS, FRAP), while individual phenolics were quantified via HPLC-DAD. Multivariate chemometric analyses, including Pearson correlation, heatmap clustering, and principal component analysis (PCA), were employed to reveal relationships between extraction conditions, polyphenolic profiles, and antioxidant activities. The results showed strong correlations between TPC, TFC, and antioxidant activity, with compounds such as quercetin-3-D-galactoside, procyanidin A2, and rutin identified as key contributors. Among the tested conditions, extraction at 70 Hz for 45 min provided the highest polyphenol yield and bioactivity. The application of HVED demonstrated its potential as an efficient and environmentally friendly technique for obtaining phenolic-rich extracts. In addition, the use of chemometric tools provided useful insights for optimizing extraction conditions and understanding the contributions of specific compounds to bioactivity. These results support future applications in clean-label product development and contribute to broader efforts in sustainable ingredient production for the food, cosmetic, and nutraceutical sectors.

Procedural modeling methods are used to automatically generate virtual scenes. There is a large number of available top‐down methods for generating partial content for specific purposes. However, little research was done on enabling the generation of content in the presence of manually modeled elements, from the bottom‐up direction, or without significant assistance from the user. No existing approach provides a platform that can combine the results of different methods, which leaves them isolated. This paper presents an integration approach that generates complete virtual space organizations by combining the usage of top‐down and bottom‐up procedural generation of content, with support for the placement of manually modeled content. The integration is made possible by using shape conversion to match the input and output shape types of different methods. The evaluation of the proposed approach was performed on a 2D polygon dataset by using four different scenarios, validating that it works as intended. Additional testing was performed by using a case study of organizing 3D virtual space around the manually modeled element of virtual heritage Tašlihan to demonstrate all capabilities of the integration approach and the different outputs depending on the level of user interaction and the desired results.

The ideological underpinnings of the Great Replacement Theory, which frames Muslims as a threat to Europe, originated in Serbia and emboldened a wider narrative of anti-Muslim hate across Western milieus. The othering of Bosnian Muslims (Bosniaks), an autochthonous ethnic group in Southeastern Europe, has contributed to the normalization of the alienation of Muslims throughout Europe, engendering Educational Displacement—an internalized sense of invisibility and devaluation within targeted individuals, diminishing their participation and trust in the societal institutions. In this complex socio-political and historical context, Bosniaks have nonetheless chosen to principally champion interfaith coexistence, offering an instructive and community-based model of resilience to hate and violence. The study investigates the Bosniaks’ affinity for coexistence by examining the underexplored case of interfaith solidarity and entente between Muslims and Jews in Bosnia and Herzegovina from 1540 to the present.

Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender equality endorsement, collectivistic attitudes, personal history of pathogenic diseases, relationship quality, jealousy, personal involvement in sexual and/or emotional infidelity, relational mobility, mate preferences, and acceptance of sugar relationships. The resulting dataset provides a rich resource for investigating patterns within, and associations across, a broad range of variables relevant to romantic relationships, with extensive opportunities to analyze individual experiences worldwide.

Abstract Polygenic risk scores (PRS) combine the effects of multiple genetic variants to predict an individual’s genetic predisposition to a disease. PRS typically rely on linear models, which assume that all genetic variants act independently. They often fall short in predictive accuracy and are not able to explain the genetic variability of a trait to the full extent. There is growing interest in applying deep learning neural networks to model PRS given their ability to model non-linear relationships and strong performance in other domains. We conducted a survey of the literature to investigate how neural networks model PRS. We categorize deep learning-based approaches by their underlying architecture, highlighting their modeling assumptions, likely strengths and potential weaknesses of the architectures. Several categories of neural network architectures exhibited promising signs for the improvement of PRS’ predictive power, namely sequence-based architectures, graph neural networks and those that incorporated biological knowledge. Additionally, the use of latent representations in autoencoders has improved predictive performance across diverse ancestries. However, a lack of existing model benchmarks on consistent datasets and phenotypes makes it challenging to understand the extent to which different architectures improve performance. Interpretability of deep learning-based PRS is also challenging with great care required when inferring causation. To address these challenges, we suggest the establishment and adherence to reporting standards and benchmarks to aid the development of deep learning-based PRS to find quantifiable trends in neural network architectures.

Background Acute cholecystitis (AC) is a frequent surgical emergency associated with significant variability in clinical outcomes and hospital length of stay (LOS). Early identification of patients at risk for prolonged hospitalization can improve triage and resource planning. Inflammatory markers such as C-reactive protein (CRP), white blood cell count (WBC), and total bilirubin (TBil), along with biliary complications like choledocholithiasis and Mirizzi syndrome, may have prognostic value. Materials and methods This retrospective study included 150 patients who underwent cholecystectomy for AC at the Department of General and Abdominal Surgery, University Clinical Centre Tuzla, Tuzla, Bosnia and Herzegovina, between January 1, 2024, and January 31, 2025. Demographic, laboratory, and intraoperative data were collected. Receiver operating characteristic (ROC) analysis identified optimal cut-offs for inflammatory markers predicting prolonged LOS (≥7 days). Multivariate linear regression was used to assess independent predictors, including CRP, WBC, TBil, and intraoperative findings. Results We found that CRP was significantly higher in patients with prolonged LOS and demonstrated the highest predictive accuracy, with an area under the curve (AUC) of 0.733 (95% CI: 0.630-0.835), followed by TBil and WBC. In multivariate analysis, only CRP ≥110.5 mg/L (p<0.001), the presence of choledocholithiasis in 26 patients (17.3%; p=0.010), and Mirizzi syndrome in seven patients (4.7%; p=0.017) remained significant predictors. WBC and TBil lost significance after adjustment. Conclusion CRP is the most reliable independent laboratory predictor of prolonged LOS in AC. The presence of choledocholithiasis and Mirizzi syndrome further contributes to extended hospitalization. These factors should be considered in early clinical risk assessment.

Background/Objectives: This research reports the synthesis and evaluation of novel charged thienobenzo-triazoles as non-selective cholinesterase inhibitors (AChEs and BChEs), their anti-inflammatory properties, and a computational study. Methods: Fifteen derivatives were created through photochemical cyclization and quaternization of the triazole core. The compounds were tested for AChE and BChE inhibition. They showed greater potency and selectivity toward BChE. Results: The most potent compound, derivative 14, inhibited BChE with an IC50 of 98 nM, while derivative 9 also displayed significant anti-inflammatory activity by inhibiting LPS-induced TNF-α production (IC50 = 0.66 µM). Molecular docking revealed that triazolinium salts form key π-π and electrostatic interactions within enzyme active sites. In silico predictions indicated favorable ADME-Tox properties for compounds 9 and 11, including low mutagenicity and moderate CNS permeability. Conclusions: These findings highlight the potential of new charged triazolinium salts as peripherally selective cholinesterase inhibitors with additional anti-inflammatory potential.