Cyber threats have become increasingly prevalent and sophisticated. Prior work has extracted actionable cyber threat intelligence (CTI), such as indicators of compromise, tactics, techniques, and procedures (TTPs), or threat feeds from various sources: open source data (e.g., social networks), internal intelligence (e.g., log data), and “first-hand” communications from cybercriminals (e.g., underground forums, chats, darknet websites). However, “first-hand” data sources remain underutilized because it is difficult to access or scrape their data. In this work, we analyze (i) 6.6 million posts, (ii) 3.4 million messages, and (iii) 120,000 darknet websites. We combine NLP tools to address several challenges in analyzing such data. First, even on dedicated platforms, only some content is CTI-relevant, requiring effective filtering. Second, “first-hand” data can be CTI-relevant from a technical or strategic viewpoint. We demonstrate how to organize content along this distinction. Third, we describe the topics discussed and how “first-hand” data sources differ from each other. According to our filtering, 20% of our sample is CTI-relevant. Most of the CTI-relevant data focuses on strategic rather than technical discussions. Credit card-related crime is the most prevalent topic on darknet websites. On underground forums and chat channels, account and subscription selling is discussed most. Topic diversity is higher on underground forums and chat channels than on darknet websites. Our analyses suggest that different platforms may be used for activities with varying complexity and risks for criminals.

Ethnic villages are examples of tourism products based on historical representations of the region. Within these villages, tourists participate in various customs and traditions, gaining insights into the heritage of local communities. As heritage should be the basis for improving rural tourism, this research was conducted to investigate the extent to which ethnic villages safeguard their heritage. The examination of cultural heritage was carried out by experts who evaluated the importance of the criteria for assessing heritage and the application of cultural heritage in these ethnic villages. A fuzzy approach was used to assess the criteria and ethnic villages using fuzzy Logarithm Methodology of Additive Weights (LMWA) and fuzzy Additive Ratio Assessment (ARAS). The sampling process included an initial pool of 28 ethnic villages identified through various associations and agencies. The villages included in this research were chosen by randomly selecting eight villages using a random number generator. Through collaboration with experts and thorough literature research, 12 criteria were established for evaluating heritage use degree in these villages. Results highlighted tourist participation as the most significant criterion, with the Lubac Valley ethnic village demonstrating superior performance. As this research has shown, applying heritage in tourism provides a unique experience, which is the base for developing rural tourism, and the Lubac Valley could serve as an example to other ethnic villages on building a tourist offer based on heritage. In addition, this research contributes to understanding the current landscape and strengthening the promotion of heritage in ethnic villages by developing a sustainable tourist offer.

The widespread deployment of AI systems has led to overlapping concerns around technological impact and governance, often resulting in conceptual ambiguities and policy confusion. We propose a structured and context-sensitive framework for addressing the ethical implications of artificial intelligence. We argue that ethical frameworks must distinguish between the intended domain of AI deployment and the scale of its societal effects.To resolve these tensions, we introduce a two-dimensional matrix based on (1) the extent of AI’s impact and (2) the scope of its governance, which together form four distinct ethical contexts. Within each quadrant, we explore specific risks, values, and regulatory considerations. This matrix not only clarifies the conceptual terrain of AI ethics but also offers a practical roadmap for anticipating ethical risks, developing normative guidance, and informing domain-specific governance strategies.Our goal is not to prescribe a single ethical doctrine but to provide decision-makers with a structured lens through which AI systems can be evaluated in context. This approach promotes adaptive and anticipatory governance while remaining responsive to local, institutional, and cultural variations.

Background and aim Public health and social measures (PHSM) are critical aspects of limiting the spread of infections in pandemics. Compliance with PHSM depends on a wide range of factors, including behavioral determinants such as emotional response, trust in institutions or risk perceptions. This study examines self-reported compliance with PHSM during the COVID-19 pandemic in the Federation of Bosnia and Herzegovina (FBIH). Materials and methods We analyze the association between compliance and behavioral determinants, using data from five cross-sectional surveys that were conducted between June 2020 and August 2021 in FBIH. Quota-based sampling ensured that the 1000 people per wave were population representative regarding age, sex, and education level based on the data from the latest census in Bosnia and Herzegovina. One-way analysis of variance (ANOVA) was used to identify significant changes between studies on determinants and PHSM measures. Regression was used to find relations between behavioral determinants and PHSM. Results Participants reported strong emotional responses to the rapid spread of the virus and its proximity to them. Risk perception was spiking in December 2020 when rates of infection and death were particularly high. Trends in policy acceptance were divergent; participants did not rate PHSM as exaggerated, but perceived fairness was low. Trust in institutions was low across all waves and declined for specific institutions such as the health ministry. In five wave-specific regression analyses, emotional response (βmin/max = .11*/.21*), risk perception (βmin/max = .06/.18*), policy acceptance (βmin/max = .09/.20*), and trust in institutions (βmin/max = .06/.21*) emerged as significant predictors of PHSM. Conclusions This study contributes to the body of research on factors influencing compliance with PHSM. It emphasizes the importance of behavioral monitoring through repeated surveys to understand and improve compliance. The study also affirms the impact of public trust on compliance, the risk of eroding compliance over time, and the need for health literacy support to help reinforce protective behaviors.

This article situates itself in the theoretical space between world-systems theory and postcolonial theory, exploring how the state of peripherality and concomitant dependency is reproduced in Bosnia and Herzegovina during the Covid-19 pandemic. The dependent position of the Bosnian protectorate in the world-system, its heritage of colonial rule and peripherality, as well as post-colonial influences of Pax-Americana on state constitution and state capture, have all contributed to the inability of the divided state to adequately respond to the pandemic. This article reveals a multifaceted dependence of Bosnia and Herzegovina on the Western core economies in relation to aid, equipment and vaccines as well as its gradual move towards China as a new opportunity. The pandemic also becomes the stage for competition between the Eastern and Western companies for mining concessions needed to secure the green transition in the respective economies, as a new wave of primitive accumulation ravages the European periphery. As a result of this new scramble for the Balkans, and amidst the global shift towards multipolarity, we see a stable reproduction of peripherality in Bosnia and Herzegovina and the Western Balkans, and re-emergence of ethnic conflict in previously disputed areas, where ethnic groups identify with the interests of their respective hegemons.

This study compares two titrimetric methods for quantifying acetylsalicylic acid (ASA) in aspirin tablets stored under different environmental conditions. ASA stability can be influenced by factors such as temperature, humidity, and light exposure. The two titrimetric methods used are acid-base titration with hydrochloric acid (HCl) and sodium hydroxide (NaOH). Aspirin tablets were stored for 30 days under controlled conditions simulating varying environmental factors, and both methods were evaluated for accuracy, precision, and reliability. The results show a strong  correlation between the two methods, with a Pearson correlation coefficient of 0.937 and a high Intraclass Correlation Coefficient (ICC), indicating consistency and reliability. However, the paired t-test revealed a statistically significant difference (r = 0.937, p = 0.001) between the  methods, suggesting small but meaningful discrepancies in their results. The Bland-Altman   analysis demonstrated that Method I consistently provided higher values than Method II, while the linear regression analysis indicated that Method II slightly underestimates values compared to Method I. Overall, both methods were found to be highly reliable and interchangeable within certain limits, but the small systematic differences between them should be considered when interpreting results. This study provides valuable insights into the performance of titrimetric   methods for ASA quantification, contributing to the optimization of pharmaceutical analysis techniques.

BACKGROUND AND AIMS Adults with congenital heart disease (ACHD) knowledge regarding their heart condition is crucial for optimal long-term outcome. Previous studies from North-Western Europe showed that important gaps in ACHD knowledge still exist. This study evaluates ACHD patients' knowledge in Central and South-eastern Europe (CESEE) and aims to identify opportunities for improving life-long ACHD care and outcomes in this region. METHODS A structured survey regarding the baseline heart condition knowledge was prospectively distributed to stable ACHD patients over a one-year period (2021-2022). Patients' responses were verified by their ACHD physicians to ensure accurate background information. RESULTS Among 1650 patients (age 34.5 ±14) across 14 CESEE countries the majority 1023(62.0%) had simple congenital heart disease with at least one previous heart procedure performed 1201(72.8%); 1060(64.2%) were asymptomatic and 875(53.8%) had secondary school education. Overall, 576(34.9%) did not have basic knowledge regarding their congenital heart disease and 146(12.2%) did not have basic understanding regarding their previous heart procedure/s. Patients considered their life expectancy similar to the general population (p=0.039). Encouragingly, 962(59.5%) expressed a desire to learn more, and 929(58.1%) favoured technological integration in their care. CONCLUSIONS Significant knowledge gaps exist amongst CESEE ACHD patients regarding their heart condition. Better ACHD patient education on current health and prospects is urgently needed. The results of this study should serve for developing congenital heart disease structured transitional and educational programmes in CESEE incorporating technology for their ACHD care and education to enhance patients' health knowledge and healthy life-behaviours to positively influence their life-long prospects.

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.

Background and Objectives Progression independent of relapse activity (PIRA) is associated with worse outcomes in people with multiple sclerosis (pwMS). Although previous research has linked PIRA to accelerated brain and spinal cord atrophy and compartmentalized chronic inflammation, the role of white matter (WM) tract degeneration remains unclear. This study aimed to explore the relationship between PIRA and the integrity of major WM tracts using diffusion tensor imaging (DTI). Methods A cohort of 258 pwMS was stratified based on the presence or absence of PIRA over a 4-year follow-up period. At the end of follow-up, DTI metrics were compared between groups using propensity score–weighted linear regression models to account for potential confounders. Results PwMS with ≥1 PIRA event (n = 39) exhibited significant reductions in fractional anisotropy and increases in radial, axial, and mean diffusivity within the corpus callosum and motor tracts (false discovery rate–adjusted p ≤ 0.04) compared with those without PIRA, indicating more pronounced WM damage. Discussion Our findings highlight an association between PIRA and microstructural damage in key WM tracts. The observed DTI changes likely reflect processes such as Wallerian degeneration and contribute to the growing evidence linking PIRA to neurodegeneration.

To examine the impact of obesity on treatment outcomes in inflammatory bowel disease (IBD). Patients aged ≥ 16 years, with IBD, a documented baseline body mass index (BMI), and starting thiopurines and allopurinol, intravenous (iv) vedolizumab, subcutaneous (sc) vedolizumab, ustekinumab, ozanimod, filgotinib, or tofacitinib were selected from the Dutch Initiative on Crohn and Colitis (ICC) registry. Underweight patients (BMI < 18.5 mg/kg2) were excluded. The primary outcome was steroid-free clinical remission (i.e. Simple Clinical Colitis Activity Index (SCCAI) ≤ 2 for ulcerative colitis (UC) and IBD-unclassified (IBD-U), and Harvey Bradshaw Index (HBI) < 5 for Crohn’s disease (CD)) at week 24. Remission rates were compared between normal weight (BMI 18.5–25 kg/m2), and overweight (BMI 25–30 kg/m2), and obese (BMI ≥ 30 kg/m2) patients using binary logistic regression analyses. Multivariable regression analysis was used to correct for possible confounders. Among 1066 patients with IBD, 619 had normal weight, 303 were overweight, and 144 were obese. At week 24, obese patients achieved steroid-free clinical remission less frequently (35.3%, OR = 0.578, 95% CI: 0.380–0.879, p = 0.010), supported by multivariable analysis (OR = 0.537, 95% CI: 0.346–0.832, p = 0.005). Obesity was associated with lower steroid-free clinical remission at week 24. Obese patients with IBD should be encouraged to lose weight not only to improve their overall health, but also to optimize their treatment outcomes.

Inside a closed, thin-walled hollow cylinder, there is a solid state of phase change material (NePCM) that has been nano-enhanced. This NePCM is heated at its bottom, with nanoparticles (Al2O3) inserted and homogenized within the PCM (sodium acetate trihydrate, C2H3O2Na) to create the NePCM. The hollow cylinder is thermally insulated from the outside ambient temperature, while the heat supplied is sufficient to cause a phase change. Once the entire NePCM has converted from a solid to a liquid due to heating, it is then cooled, and the thermal insulation is removed. The cylindrical liquefied NePCM bar is cooled in this manner. Thermal entropy, entransy dissipation rate, and bar efficiency during the heating and cooling of the NePCM bar were analyzed by changing variables. The volume fraction ratio of nanoparticles, inlet heat flux, and liquefied bar height were the variables considered. The results indicate a significant impact on the NePCM bar during liquefaction and convective cooling when the values of these variables are altered. For instance, with an increase in the volume fraction ratio from 3% to 9%, at a constant heat flux of 104 Wm−2 and a liquefied bar height of 0.02 m, the NePCM bar efficiency decreases to 99%. The thermal entropy from heat conduction through the liquefied NePCM bar is significantly lower compared to the thermal entropy from convective air cooling on its surface. The thermal entropy of the liquefied NePCM bar increases on average by 110% without any cooling. With a volume fraction ratio of 6%, there is an 80% increase in heat flux as the bar height increases to 0.02 m.

Straw has been used as a building material since time immemorial and has been considered as a waste product from the agricultural sector, usually used for feed, bedding, or fertilization. Nowadays, the construction industry strives to reduce greenhouse gas emissions and is focusing on renewable materials; hence, straw seems to be an attractive, low-energy option. Straw bales or blown insulation are common uses, with limited detailed knowledge regarding the properties of different straw types. Straw is made up of the dry stems of crops. Straw’s chemical composition will differ with different crops and can have a great impact on its effectiveness. As a renewable material, straw also has the potential to be used in buildings, enhancing thermal insulation and reducing environmental impacts. This study considers four kinds of straw: barley, oats, oilseed rape, and triticale, regarding their possible usage in insulation materials. The thermal conductivity, bulk density, and dust generation of each type were tested in the laboratory. Among them, the best performance was shown by the barley straw treated with mechanical pulping using a knife mill at 4000 rpm for 60 s, which showed the lowest bulk density and thermal conductivity and generated the least dust. It is thus proven to be an environmental insulation material with significant implications for sustainable construction and energy-efficient building design, further helping in maintaining environmental sustainability in building construction.