Search Results (874)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Background/Objective: Smoking is known to impair fracture healing and worsen surgical outcomes, but its effect on psychological recovery in spine trauma patients remains unclear. The purpose of this study is to assess how smoking affects pain and anxiety in patients with spine fractures treated either conservatively or surgically. Methods: We conducted a retrospective analysis looking at spine fracture patients > 18 years old seen at a single institution between 11/2015 and 9/2019. Patient variables such as age, sex, race, ethnicity, mechanism of injury, fracture location, presence of spinal cord injury, surgical intervention, hospital and ICU LOS, disposition, and EQ-5D-3L at 3 and 12 months were collected and analyzed. Results: Non-operative management was selected for 403 patients, of which 304 never smoked and 99 were smokers. Surgical management was utilized for 126 patients, of which 90 never smoked and 36 were smokers. Studying non-smokers and current smokers, higher levels of extreme pain and anxiety at 3 and 12 months were reported in smokers managed conservatively. Smokers managed surgically reported higher levels of pain and anxiety than non-smokers at 3 months but not at 12 months. No significant differences were seen with regards to changes in pain or anxiety between the 3- and 12-month follow-up. Conclusions: Smoking is independently associated with higher levels of pain and anxiety in conservatively managed spine fracture patients. These findings suggest a need for early intervention and cessation efforts in the trauma setting. Further investigation is warranted to clarify whether underlying psychological or physiological phenomena are impacting patient outcomes. Full article

Urban areas are at the forefront in addressing global challenges such as climate change and biodiversity loss. Among the key responses are nature-based solutions, which are increasingly being integrated into policy frameworks but which require strong community engagement for their effective implementation. This paper presents the findings of surveys conducted within the Place Lab in Katowice, Poland, an initiative developed as part of an international project and used as a participatory tool for co-creating and implementing green infrastructure. The project applies both place-based and people-centred approaches to support European cities in their transition towards regenerative urbanism. Place Lab activities encourage collaboration between local authorities and residents, enhancing awareness and fostering participation in environmental initiatives. The survey data collected during the project allowed for the evaluation of changes in public attitudes and levels of engagement and for the identification of broader societal phenomena that may influence the implementation of nature-based solutions. The findings revealed, for instance, that more women were interested in supporting the project, that residents tended to be sceptical of governmental actions on climate change, and that views were divided on the trade-off between urban infrastructure such as parking and roads and the presence of green areas. Furthermore, questions of responsibility, awareness, and long-term commitment were frequently raised. Building on the survey results and the existing literature, the study proposes a set of indicators to assess the contribution of citizen participation to the adoption of nature-based solutions. While the effectiveness of nature-based solutions in mitigating climate change impacts can be assessed relatively directly, evaluating civic engagement is more complex. Nevertheless, when conducted transparently and interpreted by experts, indicator-based assessment can offer valuable insights. This study introduces a novel perspective by considering not only drivers of engagement but also the obstacles. The proposed indicators provide a foundation for evaluating community readiness and commitment to nature-based approaches and may be adapted for application in other urban settings and in future research on climate resilience strategies. Full article

Background/Objectives: Depression is a major disorder that has been described in terms of its underlying neurological characteristics, often measured via EEG. However, almost all previous research into the EEG correlates of depression has used a unitary model of Major Depressive Disorder (MDD), whereas there is strong evidence that MDD is heterogeneous in its symptomatology and neurological underpinnings. Methods: To investigate the EEG signatures of four subtypes of depression defined according to the previous literature, the Zung Self-rating Depression Scale was administered to 54 male and 46 female volunteers (M age = 32.53 yr). EEG data were collected during an Eyes Closed condition and examined for differences in connectivity across brain networks in the alpha- and beta-bands. Results: The results were examined in terms of the number and direction of connectivity differences between depressed and non-depressed participants within each depression subtype, the alpha- and beta-band connectivities, the regions of the brain that were connected, and the possible functional reasons why specific brain regions were differently connected for depressed and non-depressed participants within each MDD subtype. Conclusions: The results suggested some differences in the alpha- and beta-band connectivity between some of the MDD subtypes that are worth considering as representing different neurological signatures across the depression subtypes. These findings represent an initial challenge to defining depression as a unitary phenomenon, and suggest possible benefits for further research into the underlying neurological phenomena of depression subtypes. Full article

Digital twin technology is emerging as a core technology that models physical objects or systems in a digital space and links real-time data to accurately reflect the state and behavior of the real world. For the effective operation of such digital twins, high-performance visualization methods that support an intuitive understanding of the vast amounts of data collected from sensors and enable rapid decision-making are essential. The proposed system is designed as a balanced 3D monitoring solution that prioritizes intuitive, real-time state observation. Conventional 3D-simulation-based systems, while offering high physical fidelity, are often unsuitable for real-time monitoring due to their significant computational cost. Conversely, 2D-based systems are useful for detailed analysis but struggle to provide an intuitive, holistic understanding of multiple assets within a spatial context. This study introduces a visualization approach that bridges this gap. By leveraging sensor data, our method generates a physically plausible representation 3D CAD models, enabling at-a-glance comprehension in a visual format reminiscent of simulation analysis, without claiming equivalent physical accuracy. The proposed method includes GPU-accelerated interpolation, the user-selectable application of geodesic and Euclidean distance calculations, the automatic resolution of CAD model connectivity issues, the integration of Physically Based Rendering (PBR), and enhanced data interpretability through ramp shading. The proposed system was implemented in the Unity3D environment. Through various experiments, it was confirmed that the system maintained high real-time performance, achieving tens to hundreds of Frames Per Second (FPS), even with complex 3D models and numerous sensor data. Moreover, the application of geodesic distance yielded a more intuitive representation of surface-based phenomena, while PBR integration significantly enhanced visual realism, thereby enabling the more effective analysis and utilization of sensor data in digital twin environments. Full article

Background/Objective: Fentanyl plays a pivotal role in the opioid epidemic, defined by four waves of overdose deaths. To analyse fentanyl research trends, examining its links to mental health, pharmaceutical development, healthcare, diseases, and pathophysiology within the broader social and health context of the time. Methods: To understand the evolution of scientific publications on fentanyl and its relationship to the opioid crisis, a search using Web of Science Core Collection and PubMed was conducted. A total of 53,670 documents were retrieved related to opioid scientific production, among which 1423 articles (3%) focused specifically on fentanyl. The 21,546 MeSH terms identified in these documents were analysed by publication year and specific fields: Psychiatry and Psychology, Chemicals and Drugs, Healthcare, Diseases, and Phenomena and Processes. R-statistical/FactoMineR libraries were used for the correspondence analysis. Results: In the first overdose death wave, research focused on improving therapies and reducing side effects. The second wave emphasised detoxification methods with naltrexone, methadone, and behavioural therapies. The third wave addressed psychological treatments and HIV-syringe-sharing prevention. The fourth wave prioritised less addictive analogues and understanding consumer profiles to combat the epidemic. Conclusions: Fentanyl research has evolved alongside real-world challenges, reinforcing the connection between patients’ needs, healthcare professionals’ roles, illicit users, policymakers, and the research community’s contributions to addressing both therapeutic use and its broader societal impact. These findings highlight the necessity for an interdisciplinary approach to scientific research integrating prevention, treatment, education, legal reform, and social support, emphasising the need for public health policies and collaborative research to mitigate its impact. Full article

Recent advances in passive acoustic monitoring (PAM) have markedly improved the ability to study marine soundscapes by enabling long-term, non-invasive monitoring of biological sounds across large spatial and temporal scales. Among aquatic organisms, fish are primary contributors to biophony, producing sounds associated with feeding, reproduction, and social behavior. However, the majority of previous research has focused on individual vocalizations, with limited attention to collective acoustic phenomena such as fish choruses. This study quantitatively analyzes choruses produced by the small yellow croaker (Larimichthys polyactis), an ecologically and commercially important species in the Northwest Pacific Ocean. Using power spectral density (PSD) analysis, we examined long-term underwater recordings from a sea cage containing approximately 2000 adult small yellow croakers. The choruses were centered around ~600 Hz and exhibited sound pressure levels 15–20 dB higher at night than during the day. These findings highlight the ecological relevance of fish choruses and support their potential use as indicators of biological activity. This study lays the foundation for incorporating fish choruses into soundscape-based PAM frameworks to enhance biodiversity and habitat monitoring. Full article

CdS nanowires have garnered considerable attention lately for their promising potential in next-generation nanolaser devices, attributed to their relatively high stability and exceptional emission efficiency within the Ⅱ–Ⅵ semiconductor family. In this study, tin-doped CdS nanowires with varying dimensions were synthesized, and the underlying mechanisms responsible for the formation of micro-cavities within these nanowires were systematically explored through scanning electron microscopy (SEM) analysis and photoluminescence mapping. The results show that a very distinct hexagonal-shaped micro-cavity is observed on the cross-section of CdS nanowires, and the size of the micro-cavity is determined by the radius of the nanowire. Additionally, through the use of angle-resolved micro-fluorescence Fourier imaging technology, it is found that under high excitation density conditions, the micro-cavity mode is more prominent at higher collection angles, which is consistent with the mode of the wall-pass cavity micro-cavity. Finally, the formation of the full reflection spectrum of the micro-cavity mode is confirmed through the wavelength shift and intensity shift phenomena related to the excitation power. These results further deepen our understanding of the micro-cavity modes in tin-doped cadmium sulfide nanowires, which may be of great significance for the application of these nanowires in new optical devices. Full article

There is a growing global interest in making existing buildings more energy-efficient. However, stakeholders seem to have differing views on the matter, especially in developing countries, thus raising the issue of awareness amongst key stakeholders at the operational stage of existing buildings. This study aimed to examine stakeholders’ awareness of the benefits of passive retrofit in residential buildings using a convergent mixed-methods approach. Quantitative data were collected from 118 property managers and 163 owners of residential buildings, and qualitative data were collected from six government officials in Lagos State, Nigeria. The quantitative data collected were analysed using fuzzy synthetic evaluation, which addresses the fuzziness in judgement-making on multi-criteria phenomena. The results revealed that property managers and owners had a moderately high level of awareness of the environmental, economic, and social benefits of the passive retrofitting of residential buildings. However, while property managers generally had a higher level of awareness than owners, a significant gap was found in their awareness of environmental benefits. Conversely, the qualitative analysis results showed that government officials demonstrated a strong awareness of environmental benefits (energy reduction, air quality, and natural lighting) and economic advantages (cost savings and lower implementation costs). In contrast, their awareness of social benefits was limited to health improvements. The findings have practical implications for policy development and awareness campaigns. Building agencies need to further reinforce their targeted awareness programmes for owners, who demonstrated fair awareness of environmental benefits while leveraging the intermediary role of property managers in promoting home retrofit practices. Economic benefits should also be an integral part of policy frameworks to drive wider adoption across all stakeholder groups. Full article

Background/Objectives: Full-arch implant-supported prostheses have become a widely accepted solution for edentulous patients, yet long-term biological and mechanical complications remain a clinical concern. Methods: This retrospective study included 70 fully edentulous patients (362 implants) rehabilitated with either fixed or removable implant-supported prostheses. Data were collected on demographics, medical status, type and location of prostheses, implant type, abutments, method of fixation, and complications. Statistical analysis included Fisher’s exact test, the Mann–Whitney U test, and chi-squared tests, with a significance level set at p < 0.05. Results: Mechanical complications occurred in 41.4% of patients (29 out of 70), with framework fractures reported in eight cases (27.6%), ceramic chipping in six cases (20.7%), and resin discoloration in four cases (13.8%). The prostheses were fabricated using monolithic zirconia, metal–ceramic crowns, zirconia on titanium bars, and hybrid resin/PMMA on cobalt–chromium frameworks. Gingival inflammation was also noted in 41.4% of cases (n = 29), predominantly in posterior implant regions. Younger patients and those without systemic diseases showed a significantly higher incidence of mechanical complications. Conclusions: Two years post-treatment, mechanical and biological complications appear to be independent phenomena, not significantly associated with most prosthetic variables. Patient-specific factors, particularly age and general health status, may have greater predictive value than prosthetic design. Limitations of the study include its retrospective design and the lack of radiographic data to assess peri-implant bone changes. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

We advance a mathematical framework for collective conviction by deriving a continuum theory from the network-based model introduced by us recently. The resulting equation governs the evolution of belief through a degenerate anisotropic logistic–diffusion process, where diffusion slows as conviction saturates. In one spatial dimension, we prove global well-posedness, demonstrate spectral front pinning that arrests the spread of influence at finite depth, and construct explicit traveling-wave solutions. In two dimensions, we uncover a geometric mechanism of curvature–induced quenching, where belief propagation halts along regions of low effective mobility and curvature. Building on this insight, we formulate a variational principle for optimal control under resource constraints. The derived feedback law prescribes how to spatially allocate repression effort to maximize inhibition of front motion, concentrating resources along high-curvature, low-mobility arcs. Numerical simulations validate the theory, illustrating how localized suppression dramatically reduces transverse spread without affecting fast axes. These results bridge analytical modeling with societal phenomena such as protest diffusion, misinformation spread, and institutional resistance, offering a principled foundation for selective intervention policies in structured populations. Full article

Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of intending to narrow the disparity between source and target domains by utilizing fully labeled source data and unlabeled target data. However, it is costly even to attain labels from source domains in many cases, rendering sufficient labeling as used in prior work impractical. In this work, we investigate an extreme and realistic scenario where unsupervised domain adaptation methods encounter sparsely labeled source data when handling HSICC tasks, namely, few-shot unsupervised domain adaptation. We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. RBPCL captures category-level semantic features of hyperspectral data and performs feature alignment through in-domain refined prototypical self-supervised learning and bi-directional cross-domain prototypical contrastive learning, respectively. Furthermore, our framework introduces the class-balanced multicentric dynamic prototype strategy to generate more robust and representative prototypes. To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. Finally, extensive experiments and ablation analysis implemented on two public cross-scene data pairs and three pairs of self-collected ultralow-altitude hyperspectral datasets under different illumination conditions verify the effectiveness of our method, which will further enhance the practicality of hyperspectral intelligent sensing technology. Full article

One of the important parameters of the sheet metal forming process is the coefficient of friction (CoF). Therefore, monitoring the friction coefficient value is essential to ensure product quality, increase productivity, reduce environmental impact, and avoid product defects. Conventional CoF monitoring techniques pose a number of problems, including the difficulty in identifying the features of force signals that are sensitive to the variation in the coefficient of friction. To overcome these difficulties, this paper proposes a new approach to apply unsupervised artificial intelligence techniques with unbalanced data to classify the CoF of DP780 (HCT780X acc. to EN 10346:2015 standard) steel sheets in strip-drawing tests. During sheet metal forming (SMF), the CoF changes owing to the evolution of the contact conditions at the tool–sheet metal interface. The surface topography, the contact loads, and the material behaviour affect the phenomena in the contact zone. Therefore, classification is required to identify possible disturbances in the friction process causing the change in the CoF, based on the analysis of the friction process parameters and the change in the sheet metal’s surface roughness. The Kohonen self-organising map (SOM) was created based on the surface topography parameters collected and used for CoF classification. The CoF determinations were performed in the strip-drawing test under different lubrication conditions, contact pressures, and sliding speeds. The results showed that it is possible to classify the CoF using an SOM for unbalanced data, using only the surface roughness parameter Sq and selected friction test parameters, with a classification accuracy of up to 98%. Full article

The western region of Lake Erie has been experiencing severe water-quality issues, mainly through the infestation of algal blooms, highlighting the urgent need for action. Understanding the drivers and the intricacies associated with algal bloom phenomena is important to develop effective water-quality remediation strategies. In this study, the influences of multiple bloom drivers were explored, together with Harmonized Landsat Sentinel-2 (HLS) images, using the datasets collected in Western Lake Erie from 2013 to 2022. Bloom drivers included a group of physicochemical and meteorological variables, and Chlorophyll-a (Chl-a) served as a proxy for algal blooms. Various combinations of these datasets were used as predictor variables for three machine learning models, including Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Random Forest (RF). Each model is complemented with the SHapley Additive exPlanations (SHAP) model to understand the role of predictor variables in Chl-a estimation. A combination of physicochemical variables and optical spectral bands yielded the highest model performance (R² up to 0.76, RMSE as low as 8.04 µg/L). The models using only meteorological data and spectral bands performed poorly (R² < 0.40), indicating the limited standalone predictive power of meteorological variables. While satellite-only models achieved moderate performance (R² up to 0.48), they could still be useful for preliminary monitoring where field data are unavailable. Furthermore, all 20 variables did not substantially improve model performance over models with only spectral and physicochemical inputs. While SVR achieved the highest R² in individual runs, XGB provided the most stable and consistently strong performance across input configurations, which could be an important consideration for operational use. These findings are highly relevant for harmful algal bloom (HAB) monitoring, where Chl-a serves as a critical proxy. By clarifying the contribution of diverse variables to Chl-a prediction and identifying robust modeling approaches, this study provides actionable insights to support data-driven management decisions aimed at mitigating HAB impacts in freshwater systems. Full article