Search Results (201)

Ecosystem disservices (EDS)—ecosystem properties and functions that cause discomfort or harm—often shape public attitudes to urban biodiversity more strongly than ecosystem services, yet they remain weakly integrated into inclusive spatial planning. This study develops and tests an EDS classification and a decision-making tree intended to help planners recognise disservices, assess ES–EDS trade-offs, and select proportionate responses without defaulting to ecological simplification. The framework was derived from literature, survey evidence, and expert–stakeholder input from Eastern European cities, and then examined through five contrasting urban action situations in Estonia and Belarus. The cases show that a shared decision logic for EDS is transferable across settings, but that its practical uptake depends on governance conditions. Where communication was proactive and explanatory, participation was meaningful, and long-term management was institutionally secured, disservices were reframed or mitigated while ecological objectives were maintained. Where disservices were framed late, trust was low, or political intervention truncated deliberation, even modest nature-based interventions were stalled or redirected toward grey alternatives. These findings justify treating EDS as a routine planning concern and demonstrate how an EDS-aware approach can strengthen inclusive planning by making both benefits and burdens of urban nature explicit. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

This research investigates global public attitudes towards ChatGPT by analyzing opinions on X (Twitter) to better understand societal perceptions of generative artificial intelligence (AI) applications. As conversational AI systems become increasingly integrated into daily life, evaluating public sentiment is crucial for informing responsible AI development and policymaking. Unlike many prior studies that adopt a binary (positive-negative) sentiment framework, this research presents a three-class classification scheme-positive, neutral, and negative framework, enabling more comprehensive evaluation of public attitudes using X (Twitter) data. To achieve this, tweets referencing ChatGPT were collected and categorized into positive, neutral, and negative opinions. Several algorithms, including Naïve Bayes, Support Vector Machines (SVMs), Random Forest, and an Ensemble Learning model, were employed to classify sentiments. The Ensemble model demonstrated superior performance, achieving an accuracy of 86%, followed by SVM (84%), Random Forest (79%), and Naïve Bayes (66%). Notably, the Ensemble approach improved the classification of neutral sentiments, increasing recall from 73% (SVM) to 76%, underscoring its robustness in handling ambiguous or mixed opinions. These findings highlight the advantages of Ensemble Learning techniques in social media sentiment analysis and provide valuable insights for AI developers and policymakers seeking to understand and address public perspectives on emerging AI technologies such as ChatGPT. Full article

In the context of accelerated digitalization, enterprises are increasingly adopting information-driven solutions to support managerial decision-making, with Enterprise Resource Planning (ERP) systems playing a crucial role in organizational development. Despite its importance, ERP adoption varies significantly across industries, particularly between developed and developing economies, where technological and structural differences persist. This paper proposes and validates a classification framework for assessing industry readiness for ERP adoption, based on an integrated PLS-SEM-MCDA methodological approach. PLS-SEM identified statistically significant factors and transformed them into weights to compare ERP user attitudes across eleven industries in Serbia and Slovenia. In addition, the TOPSIS-Sort method classified industries into high, moderate, and low readiness as predefined order classes. Finally, sensitivity analysis and comparative analysis are performed with AHP expert weights and the PROMETHEE-FlowSort method to determine the robustness of the PLS-SEM-TOPSIS-Sort results. The results show that the IT industry is the most consistent in adopting ERP systems. In contrast, other industries exhibit varying levels of readiness, depending on their degree of digital maturity and organizational preparedness. The proposed framework’s methodological flexibility allows it to be adapted to various contexts, making it suitable for future academic research and comparative studies. Additionally, the practical implications of the research are twofold. For ERP suppliers, the findings provide guidance on how to approach market segmentation and strategic positioning tailored to the specific needs of individual industries. For ERP users, their success in ERP adoption can be amplified by using the research insights as a benchmarking model. Full article

Pharmacist interventions (PIs) are central to optimising pharmacotherapy, preventing drug-related problems, and improving patient outcomes. In Portugal, the absence of a validated tool to consistently document and classify PIs limits data comparability and service development. Given these gaps, this study aimed to describe hospital pharmacists’ attitudes towards PI documentation and classification, following confirmatory factor analysis (CFA) of a survey instrument, and to provide a comprehensive overview of current practices and behaviours in hospital settings across Portugal. An online questionnaire, previously validated, was distributed online to all hospital pharmacists registered with the Portuguese Pharmaceutical Society (October–December 2024). Sociodemographic data and the cognitive and behavioural domains of pharmacists’ attitudinal model were analysed descriptively, and CFA tested the three-factor structure (Process, Outcome, Satisfaction) of the attitudinal affective domain. Of 1848 pharmacists, 260 responded (14%). Respondents reported performing a mean of 49 PIs/month (SD = 196), although many never recorded (28.8%), classified (56.2%), or analysed (52.3%) interventions. Only 2.7% declared to use a validated classification framework. The CFA supported the structural coherence of the Process factor but revealed some overlapping between Process and Outcome and instability in the Satisfaction factor. The nationwide scope and application of CFA provided partial support for the hypothesised model and highlighted areas for refinement, including revision of Satisfaction items and reconsideration of Process and Outcome as overlapping constructs. Findings highlight strong professional commitment to PIs but persistent barriers, including less clear procedures and satisfaction, underscoring the need for a unified, standardised national system to support consistent recording, classification, and evaluation. Full article

The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a survey was administered to university students (n = 280). Participant scores for the survey sections containing five-point Likert-type questions on climate change awareness were calculated using min–max normalization. The normalized data was then processed using the k-means algorithm, a well-known technique in unsupervised machine learning. This resulted in a classification (clustering) related to climate change awareness. The number of clusters was determined using the Silhouette index. Three clusters identified using k-means and Silhouette index ($S\approx 0.55)$ revealed the knowledge and application levels of student groups regarding climate change awareness. As a result of clustering, it was determined that Cluster-3 students (n = 134, 47.9%), defined as having a high level of knowledge and application, had a higher impact value in their overall assessments of green space-focused issues related to climate change awareness compared to the overall assessments of students in other clusters. Some notable findings concerning the attitudes of Cluster-3 students highlight climate change awareness-related practices. These include minimizing water consumption to levels necessary for ecosystem water management (mean = 95.7, std. deviation = 10.9) and exercising controlled, sustainable daily energy use to alleviate pressure on green spaces (mean = 94.4, std. deviation = 12.5). This study offers practical insights for policymakers, educators, and institutions, emphasizing the need to enhance climate education and to promote the active involvement of younger generations in shaping sustainable environments. Full article

This study explores the concept of environmental responsibility and assesses the attitudes and perceptions of young adults towards environmental challenges. Applying a hybrid approach based on feature selection, machine learning methods (classification and regression trees (CART) and recursive feature elimination (RFE)) and statistical methods (chi-squared tests), we analyzed survey data from 500 students across three universities. The results reveal that 82% of students rate their climate knowledge as moderate or good, while 92% perceive climate change as a serious threat. Women are more likely than men to report engagement in pro-environmental initiatives. Students’ environmental orientation weakens in the middle years of study but re-emerges in the final year, possibly reflecting greater maturity and a stronger sense of responsibility before graduation. The willingness to establish sustainable enterprises does not always correspond to a high level of knowledge or daily environmental practices. While undergraduates report high levels of climate awareness, they often fail to translate this into concrete actions, indicating a gap between knowledge, motivation, and practice. The insights from the research can inform environmental education strategies, institutional practices, and youth engagement programs within higher education. Full article

Background/Objectives: Cerebral palsy (CP) is the most common cause of motor disability in childhood and is frequently associated with cognitive impairments that limit autonomy and participation. While motor function is a known predictor of functional outcomes, the specific contribution of cognitive domains within the International Classification of Functioning, Disability and Health (ICF) framework remains unexplored. This systematic review examines the relationship between cognitive domains and the ICF components of Activities and Participation, and Environmental Factors in people with CP. Methods: Following PRISMA guidelines, a systematic search was conducted across six databases (PubMed, PsycINFO, CENTRAL, CINAHL, ERIC, and WOS) for studies published between 2002 and 2025. Eligible studies included participants with CP (n = 3056) and analyzed associations between cognitive functions and ICF domains using standardized tools and statistical methods. Risk of bias was evaluated using the Oxford Centre for Evidence-Based Medicine criteria. Results: Forty-four studies met inclusion criteria, involving mostly children and adolescents with spastic CP and mild to moderate motor impairment. General intellectual functioning, language, and visual perception were the most studied domains, showing consistent associations with ICF chapters such as Learning and applying knowledge, Communication, and Mobility. Although fewer studies examined Environmental Factors, relevant associations emerged with support systems, attitudes, and services. Heterogeneity in assessment methods and participant profiles was observed, and adult representation was limited. Conclusions: Cognitive functioning is significantly associated with multiple ICF domains in CP. Environmental Factors remain insufficiently addressed. Further research should consider CP heterogeneity and promote standardized assessments to support ICF-based intervention planning. Full article

Public attitudes toward immigration in Spain are influenced by media narratives, individual traits, and emotional responses. This study examines how portrayals of Arab and African immigrants may be associated with emotional and attitudinal variation. We address three questions: (1) How are different types of media coverage and social environments linked to emotional reactions? (2) What emotions are most frequently associated with these portrayals? and (3) How do political orientation and media exposure relate to changes in perception? A pre/post media exposure survey was conducted with 130 Spanish university students. Machine learning models (decision tree, random forest, and support vector machine) were used to classify attitudes and identify predictive features. Emotional variables such as fear and happiness, as well as perceptions of media clarity and bias, emerged as key features in classification models. Political orientation and prior media experience were also linked to variation in responses. These findings suggest that emotional and contextual factors may be relevant in understanding public perceptions of immigration. The use of interpretable models contributes to a nuanced analysis of media influence and highlights the value of transparent computational approaches in migration research. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration. Full article

Deep-sea navigation represents the future trend of maritime navigation; however, complex seakeeping conditions often lead to unconventional ship attitudes. Conventional calculation methods are insufficient for accurately assessing hull performance under heeled or extreme trim conditions. Drawing inspiration from Bonjean curve principles, this study proposes a Quasi-Bonjean (QB) method to compute ship performance elements in arbitrary attitudes. Specifically, the QB method first constructs longitudinally distributed hull sections from the Non-Uniform Rational B-Spline (NURBS) surface model, then simulates arbitrary attitudes through dynamic waterplane adjustments, and finally calculates performance elements via sectional integration. Furthermore, an Adaptive Surface Tessellation (AST) method is proposed to optimize longitudinal section distribution by minimizing the number of stations while maintaining high geometric fidelity, thereby enhancing the computational efficiency of the QB method. Comparative experiments reveal that the AST-generated 100-station sections achieve computational precision comparable to 200-station uniform distributions under optimal conditions, and the performance elements calculated by the QB method under multi-attitude conditions meet International Association of Classification Societies accuracy thresholds, particularly excelling in the displacement and vertical center of buoyancy calculations. These findings confirm that the QB method effectively addresses the critical limitations of traditional hydrostatic tables, providing a theoretical foundation for analyzing damaged ship equilibrium and evaluating residual stability. Full article

To achieve the forward movement of the aircraft landing attitude ultra-limit, this paper builds a deep learning-based aircraft landing attitude warning system. The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. The necessary feature parameters are extracted to form a deep learning training dataset. Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. Evaluation metrics are used to determine the optimal model parameters. Finally, the confusion matrix is introduced to test the prediction effect of the model, and through the secondary indicators of the confusion matrix, the prediction accuracy of the established landing attitude warning system is 94.83% for the pitch angle and 91.18% for the roll angle. It also provides pilots with a 5 s time margin to avoid risks. The system can effectively issue early warnings for ultra-limit landing attitude events and, based on the prediction results, identify the types of risks. Full article

Background: Infection control is fundamental in dental practice, especially following the COVID-19 pandemic, which highlighted the variability in students’ adherence to disinfection protocols. This study aimed to evaluate the knowledge, attitudes, and practices of the fifth-year dental students at the National and Kapodistrian University of Athens regarding antisepsis and infection control, and to assess the effectiveness of an educational intervention. Methods: A pre-post interventional study was conducted involving two in-person seminars, supplementary e-learning material, and a structured questionnaire administered before and after the intervention. The survey assessed the knowledge, clinical practices, and attitudes toward infection control, including vaccination history and prior exposure incidents. Results: The intervention led to statistically significant improvements in infection control knowledge, especially in risk-based sterilization strategies, disinfectant classification, and PPE use. Students with prior hepatitis B vaccinations and antibody testing demonstrated higher baseline scores and more significant knowledge gains. However, some misconceptions, particularly regarding surface disinfection and prosthetic care, persisted after the intervention. Conclusions: The findings support the effectiveness of structured educational interventions in improving infection control awareness among dental students. Practical, simulation-based training and earlier curriculum integration are recommended to enhance compliance and ensure safe clinical practice. Full article