Search Results (11,180)

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model’s ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies. Full article

Before the Bronze age, when agricultural practices spread throughout the Iberian Peninsula, the diet of the native people was based on hunting, fishing, and gathering wild plants. In spite of modern agriculture, the popular gathering of wild species for medical use, food, craftwork, etc., for centuries has left a detailed knowledge on the use of many of these species. Of the 6176 Angiosperms native to the Iberian Peninsula and the Balearic Islands, over 200 species were introduced into cultivation during the Neolithic period outside the Iberian Peninsula. The names of 30 of the progenitors still popularly used as food are listed in this paper, together with the names of their derived crops. This review focuses on five wild species collected as food from ancient times, namely Borago officinalis L. Prunus spinosa L., Silene vulgaris (Moench) Garke subsp. vulgaris, Scolymus hispanicus L., and Asparagus acutifolius L. In response to great demand, they have been recently introduced into cultivation in Spain and are now harvested and commercialized as new crops. Special attention is paid to their basic bioactive compounds and pharmacological properties. The limitation of this study is that the published information about the bioactive compounds of these five plants originates from different parts of the world where they grow wild or are cultivated. Therefore, further research is needed to trace the metabolomic dynamics of these plants regarding geographical and ecological principles, as well as wild versus cultivated origins. Full article

Sustainable Development Goal 6 aims to ensure the availability and sustainable management of water and sanitation for all. This study aimed to describe the implementation, effect and process evaluation of an educational session focused on promoting hand hygiene among school children within a school-based health education program. Seven schools from Romania participated in a longitudinal study. Children were categorized in two groups: Intervention (participating in a session in which they learnt about the importance of hand hygiene and the technique of handwashing, as part of a comprehensive educational program) and Control (standard education). Data were gathered through confidential questionnaires at baseline (October–November 2019, 880 participants) and follow-up (December 2020–February 2021, 484 participants); 350 children participated in both assessments. Many children consistently practiced handwashing in several situations at both evaluations. At follow-up, both groups had improved several hand hygiene practices; students from the Intervention group showed a higher handwashing frequency after using the toilet and before meals in comparison with the Control group. The majority of students from the intervention group agreed the program helped improving their handwashing behavior; girls and children with parents of lower educational levels tended to have a more favorable opinion. Consistent efforts and reinforcement are necessary for the maintenance of correct hand-washing practices. Full article

The European Higher Education Area (EHEA) university learning framework, structured around the European Credit Transfer and Accumulation System (ECTS), integrates classroom hours with independent, non-classroom workloads outside the university, where students engage in self-directed learning. This study aimed to develop a standardized protocol to monitor and quantify non-presential study hours to identify and adjust anomalous workload values. Over a two-year period, data were gathered from two distinct student groups (local Spanish students and international exchange students) enrolled in the same fourth-year architecture course at the University of Alicante. The data analysis allowed for an exploration of correlations among three key variables: non-presential study hours, final grades, and student satisfaction (self-assessed course perceptions). The results reveal a direct proportional relationship among these variables, whereby an increase in weekly study hours corresponds with both higher final grades and improved student satisfaction with the course. Full article

Diabetes mellitus (DM), a global disease that significantly impacts public health, has become increasingly common over time. In this review, we aim to determine the potential benefits of St. John’s Wort (SJW) as an adjunct therapy for DM. We gathered information from studies conducted in vitro, in vivo, and in humans. In vitro studies investigated the concentrations of SJW extracts capable of inhibiting certain enzymes or factors involved in the inflammatory pathway, such as the β-signal transducer and activator of transcription 1, nuclear factor κB, methylglyoxal, and oxidative stress (OS). The extract was found to have positive effects on OS and anti-inflammatory properties in DM, suggesting it could serve as a protective agent against diabetic vascular complications, cell damage, and apoptosis. According to in vivo research, the essential components of the extract can stimulate thermogenesis in adipose tissue, inhibit several key inflammatory signaling pathways, and delay the early death of pancreatic β cells, all of which contribute to combating obesity. The extract may also help treat prediabetes and significantly reduce neuropathic pain. Human studies have also confirmed some of these results. However, some of the plant’s side effects need further investigation through clinical research before it can be used to treat DM. Full article

Research on age-friendly cities and communities (AFCC) has primarily taken a qualitative approach. This article extends insights from a quantitative perspective to understand the international perspectives of community living and well-being during the COVID-19 pandemic. Employing an intersectional approach, this online survey aimed to understand human behaviour within AFCC. This article contextualises the digital practices and the impact of technology experienced through the age-friendly city lens of adults aged 18–50+ years living in different types of communities. Using an original dataset collected from 2020 to 2021 across 11 sites and in 13 languages, the study gathered responses from a sample size of 3422 participants. Findings indicate that adults aged 50+ years reported significantly lower loneliness scores, and higher well-being scores compared to adults aged below 40. Factors including gender, education level, and marital and employment status were found to impact loneliness and well-being significantly. From a community perspective, individuals living in rural areas and small towns reported significantly lower loneliness scores and higher well-being scores than those living in metros and cities. These findings contribute to the ongoing discourse in AFCC and have the potential to aid policy responses intended to reduce loneliness and improve well-being through public health and pandemic preparedness planning. Full article

Background: Primary amebic meningoencephalitis (PAM) caused by Naegleria fowleri is a rare and devastating infection of the central nervous system, often diagnosed late, due to its rapid progression and nonspecific symptoms. Case Presentation: We report one of the youngest documented pediatric Vietnamese cases of PAM in a 10-month-old girl from the Mekong Delta, Vietnam. The diagnosis was confirmed through multiplex real-time PCR (MPL-rPCR), microscopy, and sequencing. Clinical data were gathered retrospectively from medical records, and additional details were provided by the patient’s family. Treatment regimens, disease progression, and diagnostic challenges were reviewed and compared to existing literature. With intensive treatment, the child survived for 14 days, representing one of the longest reported pediatric PAM survival durations. No direct exposure to untreated freshwater or other typical risk factors for Naegleria fowleri infection was identified, underscoring the unique epidemiological nature of this case. MPL-rPCR enabled timely detection of the pathogen and demonstrated its utility in resource-limited settings. Conclusions: This case highlights the critical need for rapid, accessible diagnostic tools such as MPL-rPCR, particularly in resource-constrained environments where traditional diagnostics may not be feasible. It also emphasizes the importance of international collaboration and investment in cost-effective diagnostics and novel therapeutic strategies. The geographical expansion of PAM due to climate change further underscores the urgency of these measures to improve health outcomes in vulnerable populations. Full article

As the demands of the modern workforce evolve, universities are increasingly challenged to provide academic knowledge and the practical and transferable skills necessary for students’ career success. This study investigates the alignment between undergraduate students’ career aspirations, their perceived skill development, and the role of higher education institutions in bridging the skills gap. To address this issue, a comprehensive survey was conducted among undergraduate students to gather data on their career aspirations, their awareness of the skills required for their chosen careers, and their perceptions of how well their university supports their skill development. Using machine learning methods such as hierarchical clustering and k-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney U and Chi-squared (${\chi }^2$) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals. Full article

Aircraft fatigue test is a critical step for new aircraft models to obtain certification. The primary task of fatigue test is to detect damage promptly and gather damage data. Therefore, to ensure the timeliness and effectiveness of non-destructive testing (NDT) data, it is essential to control and manage the quality of the NDT process in aircraft fatigue test. This paper, based on the characteristics and work features of NDT in aircraft fatigue test, aims to achieve the goal of timely damage detection by focusing on the five key aspects of NDT quality control, namely: personnel, equipment, materials, methods, and environment. It elaborates on the quality control process, identifies key aspects of quality management, and enhances the quality of NDT in aircraft fatigue test. Full article

Background: COVID-19 infection can have a protracted course in many survivors, with varied sociodemographic and medical characteristics, exhibiting a plethora of symptoms that have consequential impacts on their quality of life. This study sought to gather pertinent data about the prevalence of Long-Haul COVID (LC), the predisposing factors to this condition and the burden on the quality of life of Mauritian survivors. Research Setting: A cross-sectional study was performed using an adapted online questionnaire, using two definitions of Long COVID, namely the WHO and NICE, SIGN and RCGP definitions. Associations between LC and categorical variables were employed to explore relationships between LC and ratio (FAS, FSS, PCS-12, MCS-12) variables. Simple and multivariable logistic regression models were used to assess the predictors and outcomes associated with LC. Findings: Of 285 Mauritians with a confirmed history of COVID-19 infection, 64.2% developed Long COVID (WHO LC-38.9%, NICE, SIGN and RCGP LC-55.8%). The most prevalent symptoms were fatigue or muscle weakness (88.0%), cough (57.4%), difficulty concentrating (55.2%), trouble remembering or memorising (49.7%), insomnia or sleep disturbance (43.7%), amongst others. Statistically significant associations were determined between LC and age, gender, vaccination status, severity of acute illness, reinfections, self-perception of disease and having more than five acute symptoms. Long COVID positively correlated with fatigue. Both Long COVID and severe fatigue (F = 73.266, p < 0.001) negatively impacted PCS-12. Fatigue had no significant impact on MCS-12. Conclusions: This study demonstrated the presence of Long COVID in the Mauritian population. Long COVID manifests as a complex and long-lasting affliction that affects even young adults with disabling outcomes, owing to multiple lingering symptoms but, most importantly, fatigue. The latter brings about distressing declines in physical and overall quality of life that thump both individual and societal health and productivity. Full article

This study focuses on selecting a suitable 3D printer and defining experimental methods to gather the necessary data for determining the optimal filament material for printing components of the VEX GO and VEX IQ robotic kits. The aim is to obtain the required data to identify an appropriate filament material and set 3D printing parameters to achieve the desired mechanical properties of the parts while maintaining cost-effectiveness. Another key objective is achieving optimal operational functionality, ensuring the required part performance with minimal printing costs. It is desirable for the modeled and printed parts to exhibit the required mechanical properties while maintaining economic efficiency. Another crucial aspect is achieving optimal functionality of the produced parts with minimal printing costs. This will be assessed by analyzing the impact of key 3D printing technology parameters, focusing in this research phase on material selection. The criteria for selecting filament materials include ease of printability under the conditions of primary and secondary schools, simplicity of printing, minimal need for post-processing, and adequate mechanical properties verified through experimental measurements and destructive tests on original parts from VEX GO and VEX IQ kits. The study analyzed various filaments regarding their mechanical properties, printability, and cost-effectiveness. The most significant practical contribution of this study is selecting a suitable filament material tested through a set of destructive tests, emphasizing maintaining the mechanical properties required for the real-life application of the parts. This includes repetitive assembly and disassembly of various robotic model constructions and their activation for demonstration purposes and applications of STEM/STEAM/STREAM methods in the educational process to achieve the properties of original components. Additionally, the study aims to set up 3D printing such that even a beginner-level operator, such as a primary or secondary school student under the supervision of their teacher or a teacher with minimal knowledge and experience in 3D printing, can successfully execute it. Further ongoing research focuses on evaluating the effects of characteristic 3D printing parameters, such as infill and perimeter, on the properties of 3D-printed parts through additional measurements and analyses. Full article

This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale. Full article

Background and Objective: This study aimed to examine the prevalence of dental fear among schoolchildren in Bosnia and Herzegovina, analyze the distribution of dental anxiety by gender, age, and place of residence in relation to perceived sources of fear, and evaluate its association with oral health status. Materials and Methods: The sample included 355 schoolchildren between the ages of 12 and 15. Data were gathered using a self-assessment questionnaire, a brief clinical oral examination, and the Children’s Fear Survey Schedule–Dental Subscale (CFSS-DS). Results: Clinical examinations showed that 87.61% of the children had dental caries, with a mean DMFT score of 3.75 (SD = 2.93). The prevalence of dental caries was significantly higher in the older group compared to the younger group (p < 0.01). Dental fear was present in 21.7% of the children, with a mean total CFSS-DS score of 27.50 (SD = 13.85). The most feared aspect among the children was “Choking” (73.8%), followed by “Injections” (63.7%) and “The noise of the dentist drilling” (52.1%). Children with dental fear had a significantly greater number of decayed and missing teeth, higher DMFT scores, and poorer gingival health and oral hygiene compared to those without dental fear (p < 0.01), even after adjusting for sociodemographic factors. Conclusions: The study found a moderate level of dental fear among Bosnian schoolchildren, with younger children and those from urban areas showing more fear of injections. It also showed a consistent link between dental anxiety and clinical factors such as caries, gum disease, and oral hygiene, even after adjusting for sociodemographic factors. Full article

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. Full article