Search Results (29,403)

Background/Objectives: Auricular epitheses play a vital role in restoring facial symmetry and patient confidence following congenital or acquired defects. Traditional wax-based fabrication is labor-intensive and lacks reproducibility. This study proposes and evaluates a simplified, digitally driven workflow for auricular prosthesis manufacturing, integrating virtual patient creation, mirrored ear design, and three-part mold fabrication using two design platforms—CATIA V5R21 (industrial CAD) and Blender (open-source graphics software). Key outcomes include mold reusability, patient-centered evaluation, and workflow feasibility. Methods: A 28-year-old female patient with right-sided microtia was selected. A light-based 3D facial scan was performed, and the intact contralateral ear was mirrored and adapted virtually to the defect site. Molds were designed in both CATIA and Blender using a standardized three-parallelepiped approach and printed using FDM technology (Elegoo Neptune 4 Plus). Five silicone epitheses were carefully fabricated with each mold. Surface trueness was evaluated with Geomagic Control X, while subjective assessments were conducted by an independent prosthetic team and the patient using Visual Analogue Scales (VAS). Results: Both design workflows resulted in clinically usable prostheses. No statistically significant difference in RMS deviation was observed (p = 0.53), although CATIA-derived epitheses achieved higher subjective scores (mean 9.22/10) compared to Blender (mean 7.67/10). The preferred prosthesis (CATIA #4) was selected for final pigmentation and delivery to the patient. All molds were reused five times without any structural damage or significant signs of wear. Conclusions: This study demonstrates that both CATIA and Blender can support an effective, patient-centered digital workflow for auricular prosthesis fabrication. The methodology enables reproducibility, mold reusability, and improved clinical accessibility of custom prosthetics. Full article

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article

University students use the Internet regularly for study, socialising, and entertainment; moreover, in adolescents and young adults, Internet use increases with age. More than ever before, the wide availability of online information requires critical thinking coupled with skills for evaluating online information, such as verifying the reliability of information and netiquette. These competencies might influence deep learning; however, few studies have analysed all these variables together. In addition, there is an ongoing academic debate as to whether using smartphones at an early age is beneficial for learning. Our study aimed to analyse, according to the age of the first smartphone, to what extent students’ critical thinking disposition, netiquette, and evaluation of the reliability of online information predict their capacity for deep learning. Our sample comprised 415 Spanish university students aged 18–36 (M = 19.98 and SD = 4.18). The instruments used were, for the assessment of Deep Learning, the Subscale of the questionnaire Attitudes towards learning of university students CEVAPU (to measure the Critical Thinking Disposition, we used the CTDS scale (Spanish adaptation of Bravo et al., 2020 and also the Competence Scale Evaluation of the reliability of online information (e-CEI) (Denoni & Cebollero-Salinas, 2025; and, finally, to assess Netiquette, the subscale of the questionnaire Evaluation of the quality of cyberbehavior “EsCaCiber” Multiple linear regression results indicated that in those participants who indicated they had acquired a smartphone before the age of thirteen, the two competencies of netiquette and evaluation of online information reliability were more strongly predictive of deep learning than in the group of participants who had their first smartphone when they were thirteen or older. Our study confirms that critical thinking disposition is a factor that favours deep learning in both groups (i.e., smartphone acquisition before and after 13 years old). The social and educational implications are along the lines of fostering a disposition to critical thinking, educating in digital literacy, especially in verifying the reliability of information, and communicating with netiquette for deep learning. Our findings indicate a potential association between critical thinking disposition and a greater propensity for deep learning in both groups (i.e., smartphone acquisition before and after 13 years old). A relevant educational implication of the results seems to indicate that a possible way to achieve deep university learning is to encourage critical thinking, to educate in digital literacy, especially in the verification of the reliability of information and to communicate with netiquette. Some of the limitations of the research design are the use of self-reports, convenience sampling and a cross-sectional design. Full article

Mangrove ecosystems play a critical role in climate regulation, carbon sequestration, and pollution mitigation. However, their long-term resilience to accelerating sea-level rise (SLR) under global climate change scenarios remains uncertain. Vertical soil accretion is a critical factor in determining the vulnerability of mangrove wetlands to SLR. In this study, vertical soil accretion rates in a mangrove wetland in the Beilun estuary were measured using a ²¹⁰Pbex dating method. Based on recently acquired data and previously available data, we conducted the first systematic assessment of SLR risk in mangrove wetlands in the Guangxi coastal zone in the context of increasing global climate change and extreme weather. The results show that the vertical soil accretion rate of 6.72 ± 1.91 (4.22–10.54) mm/a in the Beilun estuary is slightly higher than SLR rate in the Guangxi coastal zone. Concurrently, our results indicate that mangroves with thriving root systems enhance soil accretion through biotic controls in the Beilun estuary, while significant changes in soil sources and hydrodynamic forces during the 1980s and 2000s contributed to adaptation to SLR. Additionally, by linking sedimentation dynamics with SLR projections, we reveal that current accretion rates in some mangrove areas in the Guangxi coastal zone are insufficient to offset the projected SLR by the end of 2050 and 2100. This finding offers a new perspective on the traditional assumption of inherent resilience in mangroves while revealing the adaptive capacity of mangroves in the Beilun estuary and Guangxi coastal zone under projected SLR scenarios. It underscores the need for integrated management strategies that balance sediment supply maintenance and ecological restoration, which are critical to ensuring the long-term resilience of mangrove ecosystems, in line with sustainability principles. Full article

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired clonal hematologic disorder caused by somatic mutations in the PIGA gene of hematopoietic stem cells, leading to the absence of GPI-anchored proteins, including the complement regulators CD55 and CD59. This deficiency results in uncontrolled complement activation, causing intravascular and extravascular hemolysis, thrombosis, and bone marrow failure. Historically associated with substantial morbidity, PNH management has been transformed by the advent of complement inhibitors. Eculizumab, the first approved C5 inhibitor, significantly reduced thrombotic risk and improved survival but did not eliminate anemia due to extravascular hemolysis. Newer agents now target proximal complement components, offering broader control and improved convenience. This review summarizes the pathophysiology of PNH, evaluates established and emerging complement inhibitors, and discusses ongoing therapeutic challenges and future directions. Full article

The ordered tetragonal FeNi L1₀ phase, tetrataenite, is a promising candidate for rare earth-free permanent magnets due to its competitive magnetic properties and the low cost of the constituent elements. In this work, we have investigated the effect of molybdenum and aluminum substitution on the formation of the ordered L1₀ phase. The alloys were prepared with die casting and melt spinning techniques, further processed using cold rolling and cryomilling, and finally annealed below the estimated order–disorder temperature (T_OD). To study the influence of composition and processing of the alloys, structural characterization and microstructural analysis were performed with synchrotron radiation X-ray diffractometry (SR-PXD) and Scanning Transmission Electron Microscopy (STEM), respectively. The presence of tetrataenite in the alloys investigated in this work could not be confirmed. In situ SR-PXD and STEM indicated minimal structural changes in the temperature stability range of the materials. A full-loop hysteresis curve acquired using a vibrating sample magnetometer (VSM) indicated no signs of magnetic hardening of the alloys with the measured coercivity being below 10 Oe, and thus consistent with FeNi without ordering. Full article

Background/Objectives: Vision plays a key role in acquiring and automating fundamental movement skills. Evidence from Italy is scarce. This study compared Test of Gross Motor Development–2 (TGMD-2) performance between Italian children with visual impairment (VI) and sighted peers and explored differences by degree of VI (severe VI vs. blindness). Methods: This was a cross-sectional study including 38 children (VI: n = 19, 5–12 y; sighted: n = 19, 5–11 y). VI was classified per the WHO criteria. TGMD-2 was administered with adaptations suitable for VI (e.g., high-contrast cones, auditory balls, verbal cueing). Group differences (VI vs. sighted; blindness vs. severe VI; severe VI vs. sighted) were analyzed with Mann–Whitney U (α = 0.05). For participants > 10 y, raw scores were analyzed and age-equivalent scores were summarized to calculate developmental gaps. Results: Children with VI scored lower than sighted peers on locomotor, object control, and total TGMD-2 scores (all p < 0.001). Within VI, blindness was associated with lower locomotor, object control, and total scores than severe VI (p = 0.013; p = 0.043; p = 0.013). Children with severe VI also scored lower than sighted peers across outcomes (all p < 0.001). Based on age-equivalent estimates, average gross motor performance in VI was ~4–5 years below chronological age; values < 3 years were set to 3 years for calculation (the floor effect). Conclusions: Italian children with VI show reduced gross motor competence compared with sighted peers, with a substantial developmental lag. Findings support early adapted physical education and structured movement opportunities to promote participation and inclusion. Future studies should examine long-term outcomes and contextual factors shaping motor competence in youth with VI. Full article

The sugarcane crop is of great economic relevance to Brazil, and the precise productivity estimation is a major challenge in production. Therefore, the aim of this study was to estimate the productivity of sugarcane cultivars in different regions, using multispectral sensors embedded in RPAs and biometric variables sampled in the field. The study was conducted in two experimental areas, located in the municipalities of Itirapina-SP and Iracemápolis-SP, with 16 cultivars in a randomized block design. The images were acquired using the multispectral sensor MicaSense Altum, allowing the extraction of spectral bands and vegetation indices. In parallel, biometric variables were collected at 149 and 295 days after planting (DAP). The machine learning models Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were calibrated using different sets of variables, and, despite the similar performance, it was decided to use the model derived from XGBoost in the analyses, since it deals more effectively with overfitting. The results indicated a good performance of the model (R² = 0.83 and 0.66; RMSE = 18.7 t ha⁻¹ and 25.3 t ha⁻¹; MAE = 15.7 and 20.2; RPIQ = 3.22 and 2.61) for the validations K-fold and Leave-one-out cross-validation (LOOCV). The correlations between biometric variables, spectral bands, and vegetation indices varied according to crop development stage. The leaf insertion angle presented a strong correlation with near-infrared (NIR) (r = 0.76) and the indices ExG and VARI (r = 0.70 and r = 0.69, respectively). The present work demonstrated that the integration between multispectral and biometric data represents a promising approach for estimating sugarcane productivity. Full article

In 1807, a group of Protestant missionaries driven by evangelistic ideals arrived in China, dedicated to “winning China for Christ.” Walter Henry Medhurst of the London Missionary Society was among them. In addition to his preaching and study of Chinese orthodox classics, he translated the agricultural work Nongzheng Quanshu by Xu Guangqi, the very influential scholar-official of the late Ming dynasty, into English. This study explores how Medhurst’s unwavering missionary convictions influenced his secular translation praxis by examining his translational motivation, methodology and quality. He aimed to dispel Western misconceptions regarding Chinese silk-weaving techniques and then secure institutional patronage and integrate Chinese civilization under the auspices of Christian culture. Driven by the missionary mandate to convey the real China, he meticulously selected the Chinese version; and adopted a bilingual juxtaposition methodology for translating agricultural terms, thus inspiring prospective missionary students to acquire Chinese. Moreover, his scheduled missionary priority, assigned by the affiliated mission, constrained his engagement with the “amateur issues,” resulting in the translated language being less semantically equivalent, which in turn provides a pragmatic justification for the need to “civilize” China. Medhurst’s translation not only advanced his missionary enterprise, but also boosted Britain’s silk-weaving industry during the Industrial Revolution and prepared the way for the Western understanding of Chinese agricultural science from the late 19th century to the present. To this end, this analysis clearly revealed that translation was inextricably linked to the propagation of Christianity in religious communication. Full article

This study’s objectives were to evaluate the life cycle of a 2 MW solar power plant in northern Poland and provide suggestions for enhancing this kind of installation’s environmental performance. Eight years of operating data were examined under the assumption that 2000 MWh of energy was produced annually on average. The evaluation took into account two waste management scenarios—landfill and recycling—and was carried out in accordance with the ReCiPe 2016 methodology. Human health and water resource usage had the most environmental effects (7.08 × 10⁵ Pt—landfill), but recycling greatly reduced these effects (−3.08 × 10⁵ Pt). Terrestrial ecosystems were negatively impacted by the turbines’ water consumption (8.94 × 10⁵ Pt—landfill), which was lessened in the recycling scenario. The water and soil environment was greatly impacted by released pollutants, such as zinc and chlorinated hydrocarbons, whose emissions were greatly decreased by material recovery. Particularly detrimental was sulfur dioxide (SO₂), which is the cause of PM 2.5 particle matter, which is dangerous to the public’s health. Recycling has helped to lower these pollutants and enhance the quality of the air. Reducing methane and other greenhouse gas emissions can help reduce CO₂ emissions, which were the most significant factor in the context of climate change (1.91 × 10⁴ Pt—landfilling). Recycling lessened these impacts and decreased the need to acquire virgin raw materials, but landfilling was linked to soil acidification and the depletion of mineral resources. According to the findings, even “green” technology, like photovoltaics, can have detrimental effects on the environment if they are not properly handled at the end of their useful lives. Recycling is turning out to be a crucial instrument for lowering negative effects on the environment, increasing resource efficiency, and safeguarding public health. Full article

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed “SCFS-PP” framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM. Full article

The genus Corynebacterium is commonly isolated from camel uteri, yet it is rarely identified to the species level. During our routine clinical examination of she-camels brought to the hospital with history of reproductive and systemic health issues, four isolates from the uterus and one isolate from blood could not be assigned to any valid Corynebacterium species. Therefore, we aim to identify these isolates, determine any potential virulence factors, and describe how gene turnover contributed to the evolution of these species. Genome-based and phenotypic identification, along with resistome, mobilome, virulome and phylogenomics analysis, was used to characterize the isolates. The isolates were Gram stain-positive, catalase-positive, and rod-shaped. The isolates were assigned to the genus Corynebacterium based on 16S rRNA gene sequence similarity and phylogenetic analysis. The isolates 3274 and ayman were classified as two new Corynebacterium species based on the average nucleotide identity (ANI) values of 78.46% and 68.88% and digital DNA–DNA hybridization (dDDH) values of 20.9% and 22.4%. The isolates 2581A, 2583C, and 4168A constitute a single Corynebacterium species based on their pairwise ANI value of 99% and dDDH value of more than 90%. In addition, isolates 2581A, 2583C, and 4168A showed ANI values of 75.99%, 75.86%, and 76.04% and dDDH values of 23.1%, 23%, and 22.5% with closely related species, and were designated as single new Corynebacterium species. Genes for mycolic acid and menaquinone biosynthesis were detected in all isolates. The isolates were susceptible to ceftiofur, linezolid, penicillin, erythromycin, and tetracycline. All isolates harbored the antiseptic resistance gene qacA. Moreover, virulence factors involved in cell adhesion and iron acquisition were detected. The evolution of these species is dominated by gene gain rather than gene loss. The majority of these genes are acquired through horizontal gene transfer, mediated by prophages and genomic islands. In summary, we characterized three new Corynebacterium species, expanding the number of new Corynebacterium species from animals. Moreover, we described the mechanism underlying the genome evolution of these new species. The clinical findings and detection of virulence genes highlight the significance of these isolates as possible pathogens, contributing to the development of endometritis in camels. Full article

Few-shot instance segmentation (FSIS) is devised to address the intricate challenge of instance segmentation when labeled data for novel classes is scant. Nevertheless, existing methodologies encounter notable constraints in the agile expansion of novel classes and the management of memory overhead. The integration workflow for novel classes is inflexible, and given the necessity of retaining class exemplars during both training and inference stages, considerable memory consumption ensues. To surmount these challenges, this study introduces an innovative framework encompassing a two-stage “base training-novel class fine-tuning” paradigm. It acquires discriminative instance-level embedding representations. Concretely, instance embeddings are aggregated into class prototypes, and the storage of embedding vectors as opposed to images inherently mitigates the issue of memory overload. Via a Region of Interest (RoI)-level cosine similarity matching mechanism, the flexible augmentation of novel classes is realized, devoid of the requirement for supplementary training and independent of historical data. Experimental validations attest that this approach significantly outperforms state-of-the-art techniques in mainstream benchmark evaluations. More crucially, its memory-optimized attributes facilitate, for the first time, the conjoint assessment of FSIS performance across all classes within the COCO dataset. Visualized instances (incorporating colored masks and class annotations of objects across diverse scenarios) further substantiate the efficacy of the method in real-world complex contexts. Full article

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m²), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m², despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines. Full article

Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing between vibration units is a significant challenge in the design of haptic interfaces. This work focuses on the joint optimization design of vibration source characteristics and packaging materials of vibration units. From a theoretical modeling perspective, we explore the correlation between material properties and the amplitude of vibrations generated on the skin surface. A three-layer vibration unit optimization design scheme using a pogo pin structure is thus proposed. Parameters are optimized through finite element analysis, and experimental results prove that the three-layer vibration unit with pogo pins has amplitude modulation capabilities, laying the foundation for the design of array-based vibration tactile feedback interfaces and human-inspired grasp control. Full article