Search Results (773)

Background: The current food system is unsustainable, making it essential to address the issue globally through adequate policies and sustainable development goals. The European Union aims to dedicate 25% of farmland to organic farming by 2030 to promote sustainable practices. Warsaw is the first Polish city working on an urban sustainable food policy; however, there is limited data on the sustainable food system (SFS) and organic sector available. Objectives: This research examines whether consumers in Warsaw who prefer organic food also display other sustainable characteristics and awareness, reflected in their food choices, dietary habits, and other food-related behaviors. Methods: A household survey (HHS) was conducted as part of the SysOrg project, focusing on evaluating the sustainability of food systems in Warsaw in the areas of diet and organic food. The clusters of respondents, grouped by the self-declared proportion of organic foods in their diets, were analyzed and compared, and in addition, correlation analyses of the share of organic food in diets and other sustainability parameters were performed. Results: The study of 449 respondents indicates that Warsaw is at an early stage of the organic transformation, with the largest group of respondents declaring a 1–10% share of organic products in their diet. There were significant differences in dietary choices, sustainability awareness, and food selection habits and motivations among various consumer groups depending on their organic food share. Conclusions: Overall, this study’s findings highlight a link between organic food consumption and certain sustainable behaviors, suggesting potential for organic consumers’ contribution to a sustainable transformation. The study offers valuable insights into the existing knowledge gap regarding the behaviors of organic and sustainable consumers in Warsaw. Furthermore, despite the non-random nature of the sample limiting the generalizability of findings, it serves as a preliminary resource for other European cities that are formulating food policies and incorporating Green Public Procurement (GPP) into their procurement processes, especially for municipalities within the Visegrad Group. Full article

Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available. Full article

Wood–textile composites (WTCs), consisting of polypropylene and woven willow wood, have potential for both interior and exterior applications. However, their basic materials are not inherently resistant to outdoor weathering. This study examines the impact of various climatic conditions on the material behavior of WTCs. The composite and its components were aged under different scenarios, including kiln-drying, frost, standard and tropical climate, and artificial weathering and water storage, and analyzed for dimensional stability, chemical changes (FTIR), mechanical damage (µ-CT), and mechanical performance. While kiln-drying, frost, and tropical climates had only minor effects, water storage caused swelling-related damage, resulting in a 45% decrease in Young’s modulus but increased elongation at break (+88%) and impact strength (+75%). Artificial weathering led to significant degradation: tensile strength declined by 28%, Young’s modulus by 49%, and impact strength by 26%. In the medium term, this degradation compromises the integrity of the composite. The results highlight the need for effective stabilization measures—such as polymer modification or structural protection—to ensure the long-term durability of WTCs in outdoor use. Full article

Background: To cope with their horizontal swimming phases, water polo players use different swimming techniques, such as specific variants of the crawl swimming style. Thus, this study aimed to investigate the swimming skills of young water polo players. Methods: An all-out 25-m sprint swimming test in crawl style was completed by 273 international youth water polo players (age = 14.0 ± 0.8 yrs) in two modalities: basic crawl with the head in the water (25C_HeadIN), and a crawl performed while dribbling the ball (25C_Ball). Results: We registered an average time of 14.79 and 15.64 s for 25C_HeadIN and 25C_Ball, respectively, in which the ball dribbling increased to 5% of the swimming time. A swimming skill index (25C_SIC) was calculated to account for differences in ball dribbling speeds, which, considering our international sample and in the absence of previous data, we could speculate as the first international standard value for 14-year-old male water polo players competing at international level. The averaged values for 25C_SI and 25C_SIC were 0.94 ± 0.04 (a.u.) and 1.52 ± 0.15 (a.u.), respectively. Factor analysis indicated that swimming with and without the ball are structurally distinct technical skills, highlighting the specificity of these water polo players’ abilities. Moreover, the study shows significant differences (p < 0.05) between players from different countries and despite some limitations, its results provide valuable insights for the assessment and development of sprint swimming skills in youth water polo players. Conclusions: In summary, the findings of this research provide practical implications for training, player selection, player development and the optimization of youth water polo player performance. Full article

Rapid urban growth and the spread of the concept of smart cities force an increasing need to understand how cities become “smart” and apply their experience where it will best take root. Understanding which experience will be most suitable is not a trivial task and requires labor-intensive analysis. This study aims to identify smart cities that are most similar to Almaty and Astana in terms of key indicators by applying quantitative methods. Using a sample of smart cities, this paper successively employs three methods—principal component analysis, hierarchical cluster analysis, and t-distributed stochastic neighbor embedding. The results showed that Denver and Ottawa are the closest to Almaty and Astana, followed by Ankara and Phoenix. The proposed methodology allowed us to assess the similarity of urban development conditions, with an assumption that similar development conditions determine approaches to the development of smart cities, and thus the relevance of experiences from other smart cities worldwide could be applied to Almaty and Astana. This approach is intended to contribute to the effectiveness of transferring advanced solutions of smart city development to the context of Kazakhstan. The obtained conclusions can be used to form recommendations for the development strategy of Almaty and Astana, as well as other cities facing similar challenges. Full article

Mammary tumors occur in female and male guinea pigs. However, published data on their histology and sex predispositions are limited. Histologically, we examined proliferative mammary lesions of 69 female and 48 male pet guinea pigs. Lobular hyperplasia was observed only in females (n = 50). Benign tumors included simple adenomas (n = 20), adenolipomas (n = 3) and intraductal papillary adenomas (n = 5). All except two intraductal papillary adenomas occurred in females. Most malignancies were tubulopapillary and solid carcinomas (n = 54), and intraductal papillary carcinomas (n = 13). These were diagnosed more frequently in male (n = 41) than in female (n = 26) guinea pigs. The carcinomas of males had higher mitotic counts than those of females (p = 0.05). Three carcinosarcomas developed in adenolipoma, and one arose in adenoma. Results show that the mammary tumor classification of dogs and cats can be applied to guinea pigs. However, some tumors (adenolipoma, metaplastic carcinoma) are unique to guinea pigs and shared with laboratory rodents and humans, respectively. Benign tumors may undergo malignant progression. Male guinea pigs appear predisposed to ductal-associated and malignant tumors. Data suggest that male guinea pigs represent an animal model for human male breast cancer. Full article

This paper applies a semi-quantitative approach to review the diversity, environmental controls, detection methods, human health risks, and mitigation of cyanotoxins in drinking water systems (DWSs). It discusses the environmental factors controlling the occurrence of cyanotoxins, presents the merits and limitations of emerging methods of their detection (qPCR, liquid chromatography–mass spectrometry, and electrochemical biosensors), and outlines the human exposure pathways and health outcomes with identification of high-risk groups and settings. High-risk groups include (1) communities relying on untreated drinking water from unsafe, polluted water sources and (2) low-income countries where cyanotoxins are not routinely monitored in DWSs. The fate and behavior processes are discussed, including removing cyanotoxins in DWSs based on conventional and advanced treatment processes. The available methods for cyanotoxin removal presented in this paper include (1) polymer-based adsorbents, (2) coagulation/flocculation, (3) advanced oxidation processes, (4) ultra- and nanofiltration, and (5) multi-soil layer systems. Future research should address (1) detection and fate in storage and conveyance facilities and at the point of consumption, (2) degradation pathways and toxicity of by-products or metabolites, (3) interactive health effects of cyanotoxins with legacy and emerging contaminants, (4) removal by low-cost treatment techniques (e.g., solar disinfection, boiling, bio-sand filtration, and chlorination), (5) quantitative health risk profiling of high-risk groups, and (6) epidemiological studies to link the prevalence of human health outcomes (e.g., cancer) to cyanotoxins in DWSs. Full article

Bovine anaplasmosis, caused by Anaplasma marginale, is a major tick-borne disease in tropical and subtropical regions of the world, leading to significant production losses. Prolonged convalescence periods are common and surviving animals often become subclinical carriers. This study aimed to detect and characterise A. marginale in bovines in smallholder dairy farms across diverse climatic zones of Pakistan using molecular methods. In total, 321 blood DNA samples from apparently healthy cattle (n = 174) and buffaloes (n = 147) from six districts in Pakistan were tested for A. marginale using a nested PCR assay, targeting part of the major surface protein B gene (msp1β) as a genetic marker, followed by agarose gel electrophoresis and selective sequencing of amplicons from test-positive samples. Of the 321 DNA samples tested, 135 (42.1%) were test-positive for A. marginale. Prevalence was significantly higher in cattle (64.4%; 112/174) than in buffaloes (15.6%; 23/147), and female bovines (43.5%; 108/248) were more frequently infected than males (37%; 27/73). Phylogenetic analysis of the msp1β sequence data (n = 42) revealed that A. marginale from Pakistan clustered with those from Brazil, Thailand, South Africa, and the USA. This study represents the first comprehensive investigation of A. marginale from bovines from diverse agroecological zones of Pakistan and will further stimulate population genetic studies of A. marginale and investigations into the economic impact of subclinical infections in bovines in smallholder farming systems. Full article

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. Full article

Maintenance measures are widespread in the industrial environment, and various approaches to maintenance using artificial intelligence are increasingly gaining ground. Predictive assessments of system conditions ensure greater reliability and cost reductions through longer service life. The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. The algorithms were selected based on extensive literature research. This paper deals with the question of whether the approach of condition-based or statistical predictive maintenance provides more benefit for a development test bench with highly varying tests. The chosen method of deep learning and machine learning can predict damage for a specific device under test with an accuracy of up to 99% using only one torque signal. In fact, the machine learning approach was found to be sensitive to abnormal behavior on the test bench as well, leaving no abnormality undetected. Although the deep learning model was more resistant to other damage, a pretrained model can be applied to any similar device under test and deliver almost identical results. The methods presented can be adapted to various industrial applications with some adjustments, even without access to big data. This enables predictive maintenance from the very first applications. Full article

High-speed friction stir welding (HSFSW) has emerged as a promising technique for improving the manufacturing efficiency of aluminum alloy structures by enabling faster welding while maintaining the quality of welded joints. This study investigates the mechanical properties and microstructural characteristics of AA 7020-T651 aluminum alloy joints welded using a novel multi-pin tool at high feed rates ranging from 2500 to 6000 mm/min under a constant rotational speed of 4000 rpm. Defect-free welds were successfully fabricated, as confirmed by metallographic analysis and micro-computed tomography (µ-CT). The multi-pin tool facilitated consistent material flow and heat distribution, which contributed to reliable joint formation across all feed rates. At the highest feed rate, the tensile strength reached 76% of the base material. A consistent softening in the nugget zone (NZ) was observed, and electron backscatter diffraction (EBSD) analysis showed a more than 70% grain size reduction in this zone, averaging ~3 µm, due to dynamic recrystallization. These findings underscore the suitability of HSFSW with multi-pin tools for high-speed industrial applications, offering enhanced productivity without compromising structural integrity. Full article

Mental health problems among university students have become a growing concern for higher education institutions. Students experiencing mental health issues, with depression being the most prevalent disorder, tend to take longer to graduate and have a higher risk of dropping out of university. This study examines the predictive values of depression severity (Patient Health Questionnaire Depression—PHQ-9), use of psychosocial counseling, and social integration on the deviation from the standard study period and dropout intentions. A total of 3300 students at the University of Kassel, Germany were surveyed at baseline in March 2022; 1744 students provided an email address and gave permission to contact them individually for the follow-up survey in March 2023. After eliminating dropouts and questionnaires with a lot of missing values, the final sample consisted of 500 students who participated at both time points. Longitudinal data were used for descriptive, correlational, and multiple linear regression analyses. Multiple linear regression analyses revealed a significant adverse predictive value of the PHQ-9 (β = −0.082; p < 0.05) on the deviation from the standard study period. The analyses found significant positive predictive values of the PHQ-9 (β = 0.190; p < 0.001) and examination grades (β = 0.108, p < 0.05) on dropout intentions. Furthermore, this study could not confirm significant predictive values of difficulties with interaction with fellow students and lecturers on dropout intentions. The results highlight the role of health promotion, psychosocial counseling, and social networks for students with depressive symptoms. Concluding, a networked approach at universities involving students, lecturers, counseling services, and health management is recommended. Full article

Background/Objectives: Developmental research has shown that mental health and functioning is determined by social background and child and family characteristics. Until now, there have been few longitudinal studies which considered several aspects at the same time and observed children’s development over ten or more years. Methods: The aim of this 18-year-longitudinal study is to find out to which degree different child, family, and socioeconomic factors during early childhood (4 years of age) are associated with educational and professional outcomes in young adulthood (22 years of age). Of the initial sample of 280 participating families, 225 could again be investigated with standardized interviews and questionnaires at the 18 years follow-up (retention rate: 80%). Results: Educational degree of the parents was predictive of the child’s school success (β = −0.267, p < 0.001, in regression analysis). Maternal mental health (β = −0.005, p = 0.953), parenting behavior (β = −021, p = 0.782), and early child mental health problems (β = 0.071, p = 0.551) only had a low impact. The child’s sex did not predict school success. Better early learned skills (i.e., crystalline intelligence), but not cognitive skills, as measured by the child-specific intelligence test K-ABC, made children more likely to achieve good school-leaving grades (β = −0.240, p = 0.008). Children’s early mental health problems had no relevant impact on school degree (d = 0.00, p = 0.934/d = 0.02, p = 0.52 3) or professional status (d = 0.04, p = 0.157/d = −0.02, p = 0.299) at age 22. Conclusions: Besides the not-changeable parental education level, (learnable) competency aspects may be more predictive of a child’s educational success until young adulthood than earlier mental health problems in parents and children. This is good news as it supports the idea that mental health deficits can be compensated for through learning and competency training. Full article

In many tropical countries, woody forage plays an important role in ruminant nutrition. Five woody species commonly used in south-eastern Mali were subjected to a preference test, in order to examine their potential of being included in the rations of stall-fed sheep. A complete randomised block (Latin square) experiment with four rams was carried out during two test periods of five days each to test the appreciation of fresh and dried leaves, respectively, of the five species. On each test day, the five species were offered concomitantly to the rams for 30 min in the morning, and the parameters of their consumption along with the nutritional value were determined. The average consumption of fresh leaves was 160.9 g, 115.8 g, 66.2 g, 11.6 g, and 5.7 g per kilogram of live weight, respectively, for Pterocarpus lucens, Entada africana, Ficus sycomorus, Pterocarpus erinaceus, and Khaya senegalensis. For dry leaves, the consumption per kilogram of live weight averaged 69.3 g, 39.3 g, 25.0 g, 8.4 g, and 3.7 g for P. lucens, F. sycomorus, P. erinaceus, K. senegalensis, and E. africana, respectively. These results indicate that P. lucens, followed by E. africana when fresh, and by F. sycomorus when dry were the most liked species and therefore show potential to be used as a feed supplement in the rations of stall-fed sheep. Full article

Planning and harvesting anterolateral thigh flaps (ALT) requires precise perforator selection and accurate tissue perfusion assessment. Unfortunately, variable perforator anatomy and perfusion patterns often result in extensive exploratory dissection. We aimed to assess the impact of preoperative color-coded duplex sonography (CCDS) and intraoperative indocyanine green fluorescence angiography (ICGFA) on perforator selection and operative morbidity. Fifty-three ALTs were performed with preoperative CCDS and intraoperative ICGFA. Flaps had one, two, or three suitable perforators. Additional perforators were either included, or ligated following temporary clamping with ICGFA-based perfusion assessment. If perfusion was sufficient, further dissecting of additional perforators of unfavorable course was abstained from. The impact on perforator selection and operative outcomes was studied. Seven flaps were raised on a single, 34 on 2, and 12 on 3 perforators. There was no flap loss. Comparing the subgroups of fully dissected versus partially clamped and subsequently ligated perforators revealed significantly shorter harvest times in the latter (268 ± 71 versus 216 ± 47 min, p = 0.006). The unnecessary dissection of 21 additional perforators in 16 cases was avoided. Combining preoperative CCDS and intraoperative ICGFA aids in designing ALTs and guarantees the intraoperative selection of suitable perforators. This allows for significant reductions in operative time and donor-site morbidity by limiting unnecessary dissection. Full article