The objective of this study was to provide decision-making assistance in selecting electric vehicles (EVs). The multi-criteria decision-making methods (MCDM), criteria importance through inter-criteria correlation (CRITIC) and evaluation by distance from ideal solution of alternatives (EDISA), along with the technical specifications of EVs, were employed to facilitate the decision on purchasing an EV. A total of 14 minivans were analysed based on 10 criteria. The findings from the CRITIC method indicated that the most significant criteria are battery charging and vehicle consumption. The EDISA method indicated that EV11 exhibited the best characteristics and represented a prudent purchase decision. Nevertheless, the ultimate decision must consider additional factors beyond just the technical specifications, as numerous elements affect the final choice, necessitating an examination of other attributes of the EV.

Climate change is one of the main challenges facing the population around the world. The consequences of these changes have been manifested for decades in various ways, creating disasters that threaten the lives of an increasing number of people. This paper analyses the consequences of the floods that hit the United Arab Emirates on April 16, 2024. After any natural disaster, this flood also calls into question the approach, preparedness and response to this challenge, questioning the success of managing critical infrastructure. This is an important question given that in the last two decades the United Arab Emirates has encountered several floods of a more serious scale. In line with the further development of the consequences of climate change and in response to the challenges they bring with them when it comes to the United Arab Emirates, the authors also propose specific guidelines for the relevant institutions as a way of improving critical infrastructure. A significant area would be to focus on the latest technology in creating superior public-private partnerships that would become modern, national-integrated emergency management systems. The authors used the Risk Management Theory, suggesting ways in which preventive responses can be made. The authors also used content analysis, as well as the observation method.

Introduction: The mechanisms of pulmonary embolism in patients with metabolic syndrome has not been fully investigated. The aim of the study was to determine the connection between metabolic syndrome and pulmonary embolism and to evaluate the values of the glucolipid profile in patients with pulmonary embolism in metabolic syndrome. Methods: A cohort study included 305 patients with pulmonary embolism, divided into two groups: the first group with metabolic syndrome (n=165) and the control group without metabolic syndrome (n=140). The data was collected from May 2019 to May 2023. Demographic, anthropometric and laboratory parameters (glucose, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides) were analyzed in all patients with confirmed pulmonary embolism. Results: Patients with pulmonary embolism in metabolic syndrome had statistically significantly higher values of glucose, total cholesterol, LDL cholesterol, triglycerides and lower values of HDL cholesterol compared to patients with pulmonary embolism without metabolic syndrome (p<0.05). Discussion: The results of our research indicate a strong need for a broad evaluation of metabolic abnormalities with special highlight on comorbidities and the need for a multidisciplinary approach to patients with metabolic syndrome. Assessing the risk of pulmonary embolism should become imperative in the treatment of patients with metabolic syndrome. Conclusion: Metabolic syndrome may play a key role in the pathogenesis of pulmonary embolism and may be a link between venous thrombosis, atherosclerosis and pulmonary embolism. Due to the complex relationship between hyperglycemia, dyslipidemia and pulmonary embolism, additional research is necessary to answer the question why people with metabolic syndrome have a higher risk of pulmonary embolism, as well as to determine appropriate preventive strategies.

The growing need to reduce the negative impact on the environment and human health, as well as to meet the growing demand for eco-friendly textiles, has led to the development of environmentally friendly printing techniques and the use of natural dyes in the textile industry. Grape pomace is important waste material in winemaking, and has been extensively studied for its potential as a source of compounds with biological properties, especially anthocyanins, pigments responsible for the red, purple, and blue colors in grapes. The aim of this paper was to examine the potential use of natural dye extracted from grape pomace of domestic cultivar crna Mirisavka (GPCM) in preparation of printing paste with alginate, citric acid, and tannic acid for printing on cotton fabric. Special focus was placed to achieve process color magenta, by adjusting the pH value of the extract obtained. The natural dye from GPCM was extracted using maceration with 80% methanol, followed by sonication to enhance the yield of bioactive compounds. GPCM extract demonstrated significant antioxidant activity measured by FRAP, DPPH, and ABTS+ tests, and moderate antibacterial activity against Staphylococcus aureus and Escherichia coli. The pH-dependent stability of anthocyanins in GPCM extract was examined, demonstrating a magenta tone at acidic pH (pH 2-4) and color degradation at higher pH values. Cotton fabric printed with two different concentrations of GPCM extract (100 mg/L and 50 mg/L) showed good antioxidant and antibacterial activity. Based on the obtained results, it can be concluded that GPCM extract can be an environmentally friendly solution for the development of antibacterial and antioxidant textiles, with potential application in the production of protective clothing in healthcare institutions, as well as for the production of decorative home textiles, for the production of children's toys and textiles for packaging.

Short-wave infrared radiation allows for efficient heating of a body (target) with minimal thermal interaction with the gaseous medium through which it passes. The mutual geometric relationship between the infrared heating source and the target impacts the spatial resistances of radiation heat transfer. Therefore, a significant portion of the short-wave infrared radiation emitted by the heat source does not reach the target, thereby reducing its efficiency. To maximize the use of thermal radiation, this study analyzes a profiled nanofluid collector on which a heated target is placed. Nanofluid with Al2O3 nanoparticles flows through internal arrays of round nozzles and a profiled housing, being heated by the inner surface of the collector. The paper establishes a methodology based on the thermal irreversibility of the heat source, collector and nanofluid. The established methodology allows for minimizing thermal entropy in order to optimize the geometric and process parameters of the described system. The results of the conducted analysis are based on the cross-influence of the Reynolds number of the nanofluid, target and collector emissivity, short-wave heating time and nanoparticle volumetric ratio. The results obtained indicate that as the target emissivity, heater temperature and heating time increase, the thermal entropy of the mutual interaction between the heater and the target also increases significantly. Similarly, the thermal entropy of the mutual interaction between the collector and the nanofluid is greatly influenced by factors such as the Reynolds number (from 2000 to 4000), volumetric ratio (3% and 5%) and type of nanoparticles used (Al2O3, TiO2, and CuO). In this way, the specially designed collector allows for the utilization of captured heat, while the established methodology offers the opportunity to optimize the process-geometric parameters of the heating system being analyzed.

Sewage sludge is a by-product of wastewater treatment. Sewage sludge is a desirable material for application in agriculture production as a fertilizer or soil conditioner, as it is characterized by a high content of organic matter and nutrients such as nitrogen, phosphorus and potassium. However, sewage sludge tends to concentrate trace metals, organic pollutants and pathogenic organisms due to the initial pollutant load of the treated wastewater and the treatment processes. Although various chemical methods can be used to remove trace metals from sewage sludge, the use of microbes is considered a more cost-effective and environmentally friendly approach compared to chemical treatments. This review examines the risks of trace metals in the application of sewage sludge and demonstrates the efficiency of their removal using physical, chemical and biological methods.

Deformation analysis is a complex procedure where, based on several periodic geodetic measurements, displacements of points in the geodetic network are detected and determined. On this basis, movements and deformations of the built and natural environment are detected. The article discusses the Squared Msplit estimation, an extension of the maximum likelihood method, which is one of the procedures used in deformation analysis. The equations of the Squared Msplit estimation are derived and the method is presented on 2D geodetic network case study. The effectiveness of the presented method is compared with the results of other deformation analysis approaches performed with the same numerical example. The results obtained using the Squared Msplit estimation slightly differ from the simulated values, with the maximum discrepancy being 11.5 mm at unstable points and 10.4 mm at stable points, which are satisfactory results. The findings indicate that the Squared Msplit estimation provides results comparable to other methods. Therefore, it is considered suitable for deformation analysis and can be regarded as one of the applicable procedures in this field.

Background/Aim. Rheumatoid arthritis (RA) is a systemic autoimmune disease that can cause destructive joint disease and progressive disability. The diagnosis of RA is based on laboratory and clinical evidence, which includes the analysis of inflammatory markers, hematological, and biochemical parameters. Methods. Fifty patients diagnosed with RA without methotrexate (MTX) therapy and 50 patients with therapy (MTX, 7.5 mg/week; after three months prednisolone 10 mg/day) were included in this study. After six months of therapy, inflammatory biomarkers, hematological, and biochemical parameters were analyzed. Results. Inflammatory biomarkers: sedimentation rate (SE), C-reactive protein (CRP), and anti-cyclic citrullinated peptide (anti-CCP) are significantly lower in the group of patients on therapy compared to patients without MTX therapy. Significant differences were not found for the rheumatoid factor (RF). Significant differences were not found for hematological parameters between the compared groups. Analysis of serum biochemical parameters showed significant differences for aspartate aminotransferase (AST) and iron values. In patients without MTX therapy, the incidence of anemia was recorded in 68%, which is significantly higher than the incidence of 32% in patients with therapy. Conclusion. Prescribed therapy has shown effectiveness in the treatment of RA and reduction of the inflammatory process. The success of the treatment depends on the timely diagnosis of RA. Postponement of therapy and late-detected disease prolongs therapy treatment and often requires a combination of several drugs.

The critical role of high-voltage circuit breakers in the power grid underscores the need for reliable and efficient methods to assess their condition and operational parameters. To support the integration of smart grid concepts and enable condition assessment during circuit breaker exploitation, non-invasive approaches are essential. Among these, methods leveraging vibration fingerprints generated during the opening or closing of circuit breakers have shown significant promise. This paper presents a comprehensive survey of state-of-the-art research in this area, systematically analyzing over 100 influential works from the past two decades. The survey categorizes these methods based on their domain-specific approaches and highlights key challenges related to signal analysis, data acquisition, feature extraction, interpretation, and reasoning. By offering a structured analysis, this survey serves as a valuable resource for researchers and practitioners, providing insights and direction for future advancements in this niche field.