<p><strong>Introduction:</strong> Environmental factors may influence postoperative outcomes and quality of life following total hip replacement (THR). This study investigated the impact of the geographical location of the surgical site, as well as the patient&rsquo;s place of birth and residence, on treatment outcomes in individuals with artificial hips.</p> <p><strong>Methods:</strong> A prospective study was conducted involving 280 patients (both genderes; mean age 62 &plusmn; 8.8 years) who underwent THR due to primary or secondary hip osteoarthritis. Patients were divided into two groups: Group A (n = 64) included individuals who were not operated on in their place of birth and residence, while Group B (n = 216) consisted of those who were born, resided, and underwent surgery in the same geographical location. Outcomes were assessed using the EQ-5D questionnaire (covering mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), the Visual Analogue Scale (VAS) for pain, and a VAS-based treatment satisfaction scale, administered preoperatively and one year postoperatively. Statistical analysis was performed using Fisher&rsquo;s exact test (p &lt; 0.05).</p> <p><strong>Results:</strong> Only 22.9% of all patients underwent surgery in their place of birth and residence, mostly for primary hip osteoarthritis. Preoperatively, Group A reported significantly greater limitations in self-care (p &lt; 0.05). One year postoperatively, Group B showed significantly higher VAS scores for treatment satisfaction (p &lt; 0.05).</p> <p><strong>Conclusion:</strong> Patients who underwent total hip replacement in their place of birth and residence demonstrated better postoperative outcomes compared to those who had relocated.</p> <p>&nbsp;</p> <p><strong>Keywords</strong>: arthroplasty; immigrants; quality of life</p> <p>&nbsp;</p>

Flood extent maps (FEM) and flood hazard maps (FHM) serve as legal instruments for spatial planning, decision-making, strategic flood risk planning, and public awareness, supporting sustainable and safe land use along the river corridor. This study aims to improve existing FEM and create FHM for the Sanica river, prone to frequent flooding. The existing FEM were developed using a 1D HEC-RAS model under steady-flow conditions, applying a single uniform Manning roughness coefficient along the entire river reach. The study presents the first application of an unsteady 2D HEC-RAS model along Sanica river, integrating LiDAR-based topography and updated hydrological data to derive FEM and FEH for common return periods. The final 2D hydraulic model was selected through calibration of seven variants of the Manning roughness coefficient, three lumped and four distributed, with the optimal configuration identified based on three goodness-of-fit measures. The comparison of 1D and 2D FEM shows close agreement in morphologically confined canyon reaches, while significant differences occur in river sections with floodplain inundation and dominant 2D flow. These results indicate that model dimensionality can be selected based on reach-scale morphology, enabling the use of 1D models in canyon sections to reduce computational time, while applying 2D models only where complex flow dynamics are present within the Sanica river study area.

Objective: To describe patient-reported symptoms of hypothyroidism in a primary care cohort and perform an exploratory assessment of potential gender differences. Methods: This retrospective observational study included 1000 patients with confirmed hypothyroidism treated at the Public Institution Health Care Centre Visoko between January and October 2024. Data were extracted from electronic medical records. Collected variables included age, gender, comorbidities, family history, pregnancy status, and reported symptoms. Descriptive statistics were performed for the entire cohort. Exploratory logistic regression analyses were used to assess potential gender differences in reported symptoms. Results: The cohort comprised 921 (92.1%) female and 79 (7.9%) male patients, with a mean age of 57±14.6 years. The most commonly reported symptoms were hyperhidrosis (97%), fatigue (96.6%), and heart palpitations (83.5%). Male patients showed a higher likelihood of reporting heart palpitations (OR 2.42, p=0.03) and bowel problems (OR 2.03, p=0.01), and a lower likelihood of reporting neck tightness (OR 0.26, p<0.001) and tearfulness (OR 0.045, p<0.001). Conclusion: In this primary care cohort, the reported symptom burden was largely nonspecific and reflects real-world patient-reported complaints rather than classical manifestations of overt hypothyroidism. Exploratory analyses suggested potential gender-related differences; however, the low number of male patients limits firm conclusions. Larger prospective studies are needed to confirm these observations.

This study investigated the effect of altitude on the microbiological quality of raw sheep milk and Vlašić cheese produced at three locations (Galica, Gostilj, and Paklarevo) on Mount Vlašić, Bosnia and Herzegovina. A total of 30 samples were analysed for the presence of Salmonella spp. and Listeria monocytogenes, and for the quantification of coagulase-positive staphylococci, aerobic mesophilic bacteria, Escherichia coli, Enterobacteriaceae, yeasts, and moulds. None of the samples contained Salmonella spp. or Listeria monocytogenes. In milk samples, significant differences were found among localities for moulds, with regression analysis indicating a significant negative correlation between altitude and mould count (p < 0.001, R² = 0.486). In Vlašić cheese samples, aerobic mesophilic bacteria were abundant at all sites, while regression analysis showed that E. coli (p < 0.001, R² = 0.472) and Enterobacteriaceae (p < 0.001, R² = 0.767) counts varied significantly with altitude. No significant correlations with altitude were observed for other microorganisms. The results suggest that differences among localities and altitude-related trends reflect the influence of variations in cheese-making practices on microbial dynamics in artisanal dairy systems. Improving hygiene control, as well as expanding altitude-based studies across different regions, could enhance both product safety and the preservation of traditional cheesemaking practices.

Background Previous studies have shown that items that another individual looks at are better remembered than items that are not looked at, whereas the same effect has rarely been observed for non-social cues such as arrows. This pattern of results has been taken as an indication that joint attention improves memory. However, these previous studies have differed in the type of memory being tested and the type of content that is to be remembered: while effects of joint attention on long-term memory were tested with verbal items and non-verbal items, effects on working memory have only been tested with non-verbal items such as colour. Thus, the aim of the current study was to extend these previous findings and investigate whether joint attention influences working memory for verbal items. Methods In Experiment 1, participants were first presented with an image of a face with eyes that gazed either to the left or to the right. A grid of 4 letters (2x2) was then shown either on the side cued by gaze or on the opposite side. After a retention interval (1000 ms), participants were shown a letter in the centre of the screen and judged whether this letter was part of the grid shown before. In Experiment 2, we followed the same general procedure but one group was presented with gaze cues, and the other group was presented with arrow cues. Results Across two experiments, our results revealed that participants had better recall of letters that had been cued than letters that had not been cued, regardless of cue type. In contrast, participants’ reaction times were not influenced by whether the letter had been cued. Conclusions Our findings suggest that both social and non-social cues can modulate recall of verbal items such as letters in a working memory task.

We studied the expression of connexin 43 (Cx43) and pannexin 1 (PANX1) in different cellular populations of the kidneys of diabetic mice and diabetic and non-diabetic patients, to evaluate their role as potential therapeutic targets in diabetic kidney disease (DKD). A combination of a low dose of streptozotocin and a high-fat diet (HFD) was used to induce a type 2 diabetes model (DM2) in mice. Kidney tissues from diabetic (n = 9) and control patients (n = 11) who underwent nephrectomy were collected. Tissues from mice and humans were processed for double immunofluorescence, using antibodies against Cx43, phosphorylated Cx43 (pCx43) or PANX1 and markers for specific cell populations: endothelium (CD31/PECAM1); pericytes/mesangium (PDGFRB); podocytes (nephrin/synaptopodin); proximal tubules and collecting ducts (aquaporin 2). The results showed a significant decrease in the expression of pCx43 in PDGFRB-immunoreactive mesangium in diabetic patients compared to the control group (p < 0.0001). This contrasted with an increase in pCx43 in pericytes of diabetic mice (p = 0.1). However, we found a general decrease in Cx43 protein expression in diabetic mouse kidneys (p < 0.05). We also found a decrease in the expression of PANX1 in endothelial cells of diabetic patients (p < 0.05) and a significant increase in PANX1 expression in cells expressing PDGFRB (p < 0.05). Expression of PANX1 in endothelium (r = -0.50; p < 0.05) and pCx43 in the mesangium (r = -0.65; p < 0.01) correlated negatively with the percentage of sclerotic glomeruli. The expression and activation of Cx43 and the expression of PANX1 are altered in distinct populations of renal cells during long-term type 2 diabetes mellitus, especially cells of the vascular wall. This may indicate their role in the pathophysiological processes of DKD. Therefore, connexin and pannexin channels could be considered as possible therapeutic targets in the prevention and treatment of diabetic kidney disease.

Purpose: This study aimed to examine the relationships between stoppage time (ST), rally time (RT), and selected performance variables in elite men’s volleyball competitions across three leagues with and without ST restrictions. Methods: A total of 1616 rallies were analysed, including 450 from the 2022 Men’s Volleyball Nations League (VNL), 500 from the 2022 Turkish Efeler League (TL) playoff finals, and 666 from the 2022 Italian Serie A League (IL) playoff finals series. Match videos were analysed using Data Volley software, and technical performance variables, RT, and ST were recorded. Spearman correlation and Kruskal–Wallis H tests were applied to examine relationships and differences among leagues (p < .05). Ethical approval was not required because only publicly available match data were analysed. Results: Significant positive correlations were found between ST and RT in all leagues (ρ = .28–.33, p < .001). Weak to moderate negative correlations were observed between ST and reception percentage in the IL and VNL (ρ = −.08 to −.13, p < .05), while a weak positive correlation was found in the TL (ρ = .10, p = .029). Across all leagues, ST was negatively correlated with first-attack success (ρ = −.15 to −.21, p < .001). Conclusion: ST was significantly shorter in the VNL compared with the TL and IL. Differences were also observed in reception and attack performance variables across leagues. These findings indicate that temporal characteristics of the game are associated with technical performance in elite men’s volleyball.

China’s export-oriented economic expansion has substantially influenced transport-sector CO2 emissions, raising critical concerns about the environmental impacts of sustained industrial growth and global trade integration. Understanding the interplay between macroeconomic dynamics, trade composition, and industrial structure is essential for aligning economic development with climate mitigation objectives. This study examines transport-related CO2 emissions in China over the period 1990–2023, employing a hybrid methodological framework that combines econometric modeling—including Autoregressive Distributed Lag (ARDL) bounds testing, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS)—with machine-learning techniques using Extreme Gradient Boosting (XGBoost) interpreted through SHapley Additive exPlanations (SHAP). The analysis confirms a long-run cointegration relationship between transport emissions and the selected macroeconomic variables. Short-run dynamics indicate a strong sensitivity of emissions to GDP growth, while long-run estimates reveal that higher export-to-GDP ratios and industrial value added contribute to reducing transport emissions, reflecting the efficiency gains from industrial upgrading and cleaner trade practices. By contrast, the expansion of medium- and high-technology exports increases emissions due to the energy- and logistics-intensive nature of high-value goods. The XGBoost model achieves high predictive performance, with an out-of-sample R2 of 0.9975 and a Root Mean Square Error (RMSE) of 87.16, confirming the dominant contribution of medium- and high-technology exports to transport-sector emissions. The results underscore the critical role of aligning trade structure, industrial productivity, and low-carbon logistics within China’s policy agenda. Implementing strategies that enhance industrial energy efficiency and develop sustainable transport infrastructure can substantially reduce the environmental impacts associated with export-driven economic expansion.

The expansion of industrialization and household use of synthetic compounds has generated significant wastewater containing toxic heavy metals. In developing countries, this wastewater is often discharged untreated due to the high cost of advanced treatment technologies. This study used sodium hydroxide as a low-cost, readily available precipitation agent to remove selected metal ions from mono- and binary-component solutions. Unlike most studies focusing on pH and initial ion concentration, this work investigated operational parameters such as stirring speed (0–800 rpm) and time (0–30 min) while keeping pH and concentration constant. Results showed that higher stirring speeds and longer stirring times enhanced metal ion removal, with Pb(II) efficiency increasing from 86.64% at 100 rpm to 94.33% at 800 rpm. In binary mixtures, similar improvements were observed. These findings highlight that simple, low-cost operational adjustments can significantly improve metal removal efficiency, which is particularly relevant for water treatment in resource-limited settings. The two-way ANOVA without replication showed that the type of metal or mixture had a significant effect on removal efficiency, while stirring speed and time within the investigated ranges did not have a statistically significant effect. These results indicate that differences in removal efficiency are primarily due to the metals’ chemical properties rather than the operational parameters.

This study explored the relationships between geological substrate and the structural and compositional attributes of mixed beech ( Fagus sylvatica L.), fir ( Abies alba Mill.), and spruce (Picea abies [L.] Karst.) forests on Mt. Konjuh in northeastern Bosnia and Herzegovina. Research was conducted on 81 experimental plots established across three dominant substrates: limestone, peridotite, and chert. Stand structure, diversity, and spatial organization were assessed using the Shannon diversity index, Pretzsch’s species profile index, Gini coefficient, and the Clark–Evans and Füldner indices. The analyses revealed consistent differences among substrates, suggesting that geological conditions influence forest structure and diversity. Higher diversity and vertical heterogeneity were generally associated with limestone, while stands on peridotite and chert exhibited simpler but more balanced structures. All forest types displayed a reverse J-shaped diameter distribution, indicating uneven-aged composition and ongoing natural regeneration. Spatial patterns showed a tendency toward clustering of beech and spruce and higher species mingling on limestone. Overall, mixed beech–fir–spruce forests on Mt. Konjuh appear to be stable ecosystems whose structure and diversity are shaped by an interplay of geological, edaphic, and ecological factors. The results highlight the relevance of site-specific and adaptive silvicultural approaches that account for local variability in substrate and stand conditions.

Norway spruce ( Picea abies (L.) Karsten) is one of the most economically important conifer species in Europe. Efficient utilisation and processing of its wood require detailed knowledge of its technical properties, as well as the most common wood defects that substantially affect both properties and utilisation. Given the crucial role of wood defects in the roundwood classification system, the primary objective of this study was to identify defects in Norway spruce and to analyse the influence of forest assortment characteristics (diameter and position along the stem) and tree attributes (diameter at breast height and position within the stand) on the size of wood defects. The research was conducted in Bosnia and Herzegovina, within a forest compartment of an uneven-aged, mixed beech and silver fir stand with spruce. Trees were felled and processed into assortments using a chainsaw, predominantly applying the cut-to-length method. After measuring the assortment dimensions, the occurrence of defects was assessed, and their sizes were determined. The analysis showed that, following knots, the most common wood defect was rot, followed by pith eccentricity, compression wood, scars, mechanical damage, and resin pockets. Statistically significant differences were found in the size of knots, ellipticity, and taper among different diameter classes of assortments (p<0.05), as well as assortment positions along the stem (p=0.0000). Also, a statistically significant difference was observed in the size of the knots and ellipticity in relation to both diameter at the breast height and tree position within the stand (p<0.05). Overall, the findings align with previous studies, confirming the higher quality of the lower stem section, as reflected in smaller defect sizes critical for roundwood quality classification, such as knots, rot, ellipticity, and taper.

Purpose: This study examines the psychological implications of integrating artificial intelligence (AI) into judicial decision-making in criminal justice, including algorithmically supported risk assessment and sentencing decisions. It analyzes how AI-based decision-support systems influence perceptions of fairness, trust in judicial decisions, and decision confidence, as well as the emotional responses of judges, jurors, defendants, and victims. Methodology: The study employs a theory-driven and interdisciplinary conceptual framework grounded in psychological theories of decision-making, procedural justice, and affective processes. Through a critical integrative synthesis of legal, psychological, and ethical scholarship on algorithmic decision-making, predictive modeling, and risk assessment systems in criminal justice, the study examines their implications for human judgment, responsibility attribution, and judicial experience. Findings: The analysis demonstrates that AI-assisted decision-making can substantially shape psychological perceptions of justice and the legitimacy of judicial processes. Although algorithmic tools are often perceived as consistent and objective, their reliance on historical data may reproduce existing biases, thereby negatively affecting perceived fairness, trust in judicial outcomes, and decision confidence among legal professionals and trial participants. These findings indicate that the psychological impact of artificial intelligence extends beyond technical accuracy and plays a significant role in shaping perceptions of the legitimacy of judicial processes. Unique Contribution to Theory, Practice, and Policy: This study contributes to psychological theory by offering a systematic examination of the cognitive, affective, and evaluative processes associated with algorithmically supported judicial decision-making in criminal justice. In the context of judicial practice, the analysis demonstrates how uncritical reliance on AI systems may diminish judicial autonomy and obscure responsibility attribution in decision-making processes. From a public policy perspective, the findings contribute to the conceptualization of regulatory approaches oriented toward transparency, fairness, and trust in the use of AI in judicial decision-making.

This study explores how the choice of database influences performance, modularity, and extensibility in monolithic and microservices software architectures. These software architectures are tested in combination with relational database MySQL and document-based database MongoDB. Apache JMeter is used for automated load testing. Relational databases consistently deliver better performance for structured transactions, while document-based solutions offer greater flexibility and extensibility in distributed systems. In addition, the analysis shows that the choice of database may have more effect on the total performance of a microservices architecture than the inherent overhead of the architecture itself. The findings highlight the critical trade-offs between performance and flexibility, emphasizing the importance of strategic database selection.

This research presents a comparative study of rule-based and machine learning-based approaches for detecting anomalous authentication activities. Rule-based detectors are evaluated against an unsupervised anomaly detector trained on normal user behavior, using the LANL dataset expanded with realistic synthetic attacks. Thresholds used by all detectors are calibrated on an evaluation set to meet fixed false-positive budgets. Results are reported using eventlevel and burst-level metrics. The results show that rule-based approaches perform strongly on high-rate attacks, while machine learning approaches are effective for low-rate, stealthy activity.