Search Results (1,364)

Negative driving emotions constitute a significant factor compromising road safety. Current intelligent vehicle human machine interaction (HMI) systems predominantly focus on functional implementation, lacking the capability to perceive and adapt to the driver’s psychological state. To address this issue, this study investigates the intrinsic relationship between driving emotions and HMI through multimodal experiments. Experiment One reveals the distribution patterns of drivers’ visual attentional scope under different emotional states. Experiment Two establishes a color preference model for HMI interfaces corresponding to specific emotions. Experiment Three quantitatively analyzes the impact of emotional variations on the perceptual efficiency of auditory warnings. Based on the experimental data, an interaction design principle matching “Emotion-Scene-Modality” is formulated, guiding the design of a data-driven, emotion-adaptive HMI prototype system. This system can perceive the driver’s emotional state in real time via multimodal sensors and dynamically adjust interface color themes, information layout, warning sound effects, and voice interaction style according to predefined interaction strategies. Usability testing demonstrates that, compared to traditional static HMI, this affective adaptive system effectively mitigates the driver’s negative emotional load and provides alerts that are more perceptible and less likely to cause irritation during critical moments. Consequently, it offers a significant theoretical foundation and practical reference for constructing a safer and more comfortable next-generation intelligent vehicle cockpit interaction paradigm. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

Rapid urbanization has transformed the face of Texas by converting agricultural and natural lands into expanding built-up areas. This study analyzes and simulates land-use and land-cover (LULC) changes in Kaufman County, Texas, one of the fastest-growing counties in the United States, using a hybrid Cellular Automata–Artificial Neural Network (CA–ANN) model within the Quantum Geographic Information System (QGIS) Modules for Land-Use Change Evaluation (MOLUSCE) framework. Multitemporal NLCD datasets (2001, 2011, and 2021) and six spatial drivers: Elevation, Slope, Aspect, Distance from Roads and Rivers, and Built-up Density were used in the modeling framework. Transition relationships were calibrated using the 2001–2011 LULC data, and the model was validated by simulating the 2021 LULC map from the 2011 baseline. The calibrated model was then used to simulate future LULC scenarios for 2031, 2041, and 2051. Model validation yielded an overall Kappa value of 0.84 and a correctness of 90.9%, indicating high similarity between the observed and simulated maps. The results indicate simulated urban expansion, with built-up areas increasing by nearly 30% by 2051 at the expense of cropland and open areas, with forest and water bodies slightly increasing, and wetlands remaining stagnant. The CA–ANN model effectively captured the nonlinear, spatially dependent land-transition patterns using open-source tools. These findings provided useful information for sustainable land-use planning and environmental management, with the potential to incorporate spatial modeling into regional development strategies in rapidly urbanizing areas of Texas. Full article

Vegetation elements on buildings such as green roofs are increasingly recognized as nature-based solutions to address urban environmental challenges. Green roofs can be adapted to diverse climates and building types. Their implementation in Slovakia has been rising, yet it remains limited in scale and technological ambition. Projects funded from public resources often remain conventional, with rare ambition to implement novel stormwater management systems and solutions that enhance biodiversity. Currently, the majority of investments in green roofs are limited to the private sector, while public institutions lag behind. Thus, public buildings with novel green systems and elements can still be considered non-conventional, innovative, and influential. This study investigates the development of green roofs on public buildings in the city of Nitra, Slovakia, from the first installation in 1992 to recent projects in the 2020s. By systematically mapping all existing public green roofs and conducting qualitative narrative interviews with key stakeholders, this research aims to identify the main motivations, actors, and barriers behind the implementation of green roofs in public investments. The novelty of this research lies in its mixed-methods approach, combining quantitative and qualitative analyses to draw conclusions from a comprehensive dataset. By capturing all existing examples within their spatial and temporal context, rather than relying on a random subsample of case studies, this study provides a highly representative evaluation of green roof adoption. Preliminary findings provide insights into the temporal and spatial diffusion patterns of green roofs in a medium-sized Central European city and highlight the main drivers of public decision-making. The results contribute to a better understanding of how urban sustainability initiatives emerge in public sector contexts and aim to inform policy and planning to initiate and boost more green roof implementation. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

Sustainability literacy is increasingly invoked in construction and planning research, yet it is most often framed as an educational construct concerned with awareness, knowledge, and attitudes. This framing provides limited explanatory power for understanding how sustainability values are translated into in real-world planning decisions, particularly under conditions of uncertainty and value conflict. In parallel, artificial intelligence (AI) has been introduced into planning practice largely as an optimization-driven analytical tool, reinforcing instrumental conceptions of rationality. This study reconceptualizes sustainability literacy as a decision capability and develops an AI-enabled theoretical framework that positions AI as a cognitive partner in sustainability-oriented construction planning. Methodologically, the study adopts a conceptual research design grounded in a systematic interdisciplinary literature synthesis spanning planning theory, circular economy, social sustainability, and AI-enabled decision support, combined with theory-building and framework development procedures. The proposed framework clarifies how human judgment can be cognitively augmented through AI-supported interpretation, trade-off exploration, and value-informed deliberation, thereby reframing sustainability as an internal driver of planning judgment rather than an external performance criterion. By conceptualizing human–AI collaboration as an iterative, reflective process, the study establishes a coherent theoretical basis for context-sensitive sustainability planning in the built environment, with implications for decision-support system design, planning practice, and professional education. Full article

Access to quality seed is a critical driver of smallholder productivity and household food security in South Africa, yet rural communities in the Eastern Cape continue to rely heavily on informal seed systems. Limited seed literacy among farmers and vendors is widely recognized as a constraint to the effective selection and use of high-quality seed. The purpose of this study is to assess seed literacy levels among smallholder farmers in KwaMkhiva village and evaluate how knowledge gaps shape farmers’ seed sourcing patterns and access to quality seed. The study hypothesizes that low seed literacy significantly increases reliance on informal seed systems and reduces adoption of certified or improved varieties. A quantitative, cross-sectional survey design was used to collect data from 50 smallholder farmers and 12 informal seedling vendors, complemented by semi-structured interviews with three extension officers. Descriptive statistics, chi-square tests, correlation analysis, and a composite Seed Literacy Index (SLI) were employed to assess literacy dimensions and their association with seed choices. Findings show that 49% of farmers rely on local markets and 40% use farm-saved seed, with 75% assessing quality visually rather than through germination or varietal indicators. Only 10% had received any seed-related training, and awareness of seed adaptability and crop rotation was below 20%. Higher SLI scores were positively associated with adoption of certified seed (r = 0.42, p < 0.01) and crop diversification. The study concludes that seed literacy is a critical yet underserved capability that shapes smallholder seed access within dual seed economies. Strengthening farmer-centred seed literacy programmes, revitalising extension services, and supporting community seed banks could enhance access to quality seed and improve smallholder resilience. Full article

Cardoon (Cynara cardunculus var. altilis DC.) is a Mediterranean crop valued for biomass production and bioactive compounds; however, information on the use of biostimulants in soilless systems remains limited. This study evaluated the effects of two biostimulants, a legume-derived protein hydrolysate (PH) and an Ascophyllum nodosum seaweed extract (SW), applied as weekly foliar sprays, on growth, physiological performance, mineral composition, and phytochemical traits of cardoon cultivated in a floating system. Biostimulant application significantly affected plant performance, inducing distinct treatment-dependent responses. Both PH and SW increased fresh and dry biomass compared with untreated plants. SW predominantly promoted vegetative growth, chlorophyll content, and nutrient accumulation, whereas PH markedly enhanced nutraceutical quality by increasing total phenolic content and antioxidant activity, reaching 64.4 mg GAE g⁻¹ dry extract and the lowest IC₅₀ value (172 µg mL⁻¹). Harvest timing modulated the magnitude of biostimulant effects, with maximum biomass yield observed at intermediate developmental stages (up to 8.17 kg m⁻²), while phenolic concentration and antioxidant capacity declined at later stages. Multivariate analyses confirmed that PH and SW induced complementary metabolic strategies. Overall, the biostimulant type emerged as the primary driver of plant response, with harvest timing acting as a modulating factor. Targeted biostimulant management, therefore, represents a promising strategy for optimizing the productivity and phytochemical quality of cardoon in soilless cultivation systems. Full article

Soil erosion is a growing environmental problem in arid regions, where land-use changes and landscape fragmentation directly influence land degradation. This study estimated soil loss in the Tarabillas sub-basin, located in the Chihuahuan Desert, Mexico. To this end, the Universal Soil Loss Equation (USLE) was applied and integrated with Geographic Information System (GIS) tools. Landsat TM and OLI satellite imagery were classified through supervised techniques, achieving overall accuracies above 89%. The analysis was supported by comparing erosion patterns associated with land-use changes occurring during the 1990–2021 period, assessed through cross-tabulation matrices and landscape metrics. The results show that although the average erosion potential of the sub-basin remained constant at approximately 12.45 t ha⁻¹ yr⁻¹, erosion redistributed spatially, concentrating in areas where agriculture has replaced natural vegetation. Shrublands and grasslands continue to dominate the high erosion categories due to their wide spatial extent and high erodibility. These findings highlight that fragmented agricultural expansion constitutes the main driver of landscape transformation and soil vulnerability, emphasizing the importance of integrating remote sensing, GIS, and empirical models to support sustainable land management in arid regions. Full article

Genome-editing (GE) techniques are gaining relevance in the agri-food system for their potential to enhance crop resilience and sustainability, raising questions about consumer acceptance and responsible innovation. Understanding public willingness to buy (WTB) GE foods is therefore essential. While trust in science is often cited as a key driver, its effects are not straightforward. This study examines mechanisms linking trust in science to WTB GE foods, testing the mediating role of attitudes and the moderating role of perceived literacy. A cross-sectional online survey was conducted with a representative sample of Italian adults. Using structural equation modelling, we tested three models: a mediation model, a model including a direct path between trust and WTB, and a moderated model incorporating perceived literacy. Trust predicted more favourable attitudes toward GE, and attitudes were strongly associated with WTB. However, when controlling for attitudes, the direct effect of trust on WTB was negative. Perceived literacy significantly moderated this relationship: higher perceived literacy strengthened the negative trust–WTB association. Overall, generalized trust in science is not sufficient for public acceptance of GE crops and foods. Communication strategies should move beyond trust-building and foster informed, critically engaged consumers. Full article

Public transport systems in rapidly urbanizing Gulf cities confront the simultaneous challenge of decreasing emissions while guaranteeing equal access for riders, many of whom rely on transit for economic reasons. Sustainable smart city development necessitates bus services that are both efficient and sensitive to rider needs in adverse weather conditions. This study develops and evaluates a wellbeing-focused planning framework for Dubai’s bus network, filling gaps in prior research that primarily focuses on temperate, choice-based transport environments. The study uses Generalized Structured Component Analysis (GSCA) to analyze how Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) impact overall journey wellbeing, based on a cross-sectional survey of 491 riders collected from July–August 2024. Data were collected during peak summer conditions, and the analysis followed a structured workflow that operationalized the proposed constructs into measurable indicators and estimated both the measurement and structural components of the GSCA model to find planning relevant wellbeing drivers. The model shows a strong fit (FIT = 0.684; GFI = 0.991; SRMR = 0.056), with SEA (β = 0.504) having the greatest influence on wellbeing, followed by SOA (β = 0.344) and PEPC (β = 0.070). Affordability and information quality are key SEA metrics, highlighting the necessity of economic access and multilingual, real-time communication. Overall, the findings indicate that wellbeing is most strongly shaped by accessibility-oriented service experience attributes particularly affordability and information quality followed by operational assurance, while comfort-related conditions remain significant under high heat exposure during waiting and transfers. On the other hand, the research indicates that operational reliability helps mitigate environmental discomfort in hyper-arid areas. The report suggests focusing on equal prices, digital information accessibility, dependable operations, and climate-adaptive infrastructure to promote sustainable mobility and long-term public transport use. Full article

Bone metastasis is a devastating complication of advanced osteotropic malignancies, notably breast, prostate, lung carcinomas, and malignant melanoma, and remains a primary driver of mortality. Historical paradigms have conceptualized skeletal dissemination almost exclusively as a hematogenous process wherein circulating tumor cells colonize receptive bone marrow niches. However, this model fails to reconcile why lymph node metastasis consistently serves as a potent predictor of bone involvement even though therapeutic lymphadenectomy rarely prevents distant spread. This discordance suggests that lymph nodes function not merely as passive reservoirs but as active ‘evolutionary gateways’ that sculpt bone-tropic metastatic clones. In this review, we introduce the Lymphatic–Bone Axis, a framework integrating lymphatic biology into models of bone metastasis. We synthesize emerging evidence elucidating how the lymph node microenvironment primes tumor cells through CCR7-CXCR4 switching, induction of osteomimicry programs, and metabolic reprogramming that favors survival within the bone marrow. We also discuss preclinical data demonstrating direct intranodal intravasation via high endothelial venules (HEVs), providing a rapid route into the systemic circulation that bypasses the thoracic duct. Beyond consolidating current knowledge, we outline a research agenda for dissecting this axis, including longitudinal single-cell transcriptomic mapping and functional assessments of lymph node-derived tumor cells. Finally, we consider translational implications, highlighting why bone-targeted agents alone may prove insufficient once cells are conditioned within lymphatic niches. By mechanistically linking lymphatic priming to skeletal colonization, this review informs the rational design of multimodal therapeutic approaches that jointly target lymphatic transit and the bone microenvironment. Full article

Periodontal disease and oral squamous cell carcinoma (OSCC) are highly prevalent conditions that contribute substantially to global morbidity, as documented by recent Global Burden of Disease analyses. The growing epidemiologic and experimental literature has prompted interest in potential links between chronic periodontal dysbiosis—particularly infection with Porphyromonas gingivalis—and molecular pathways involved in oral carcinogenesis, including β-catenin signaling. This narrative review synthesizes epidemiologic, clinical, and experimental studies examining associations between periodontal disease, P. gingivalis, and OSCC, with focused evaluation of β-catenin as a context-dependent signaling component within broader inflammatory and metabolic networks. Population-based studies report heterogeneous associations between periodontitis and OSCC that are frequently confounded by tobacco use, alcohol consumption, and socioeconomic factors, supporting correlation rather than causal inference. Experimental investigations in vitro and in vivo demonstrate that P. gingivalis can influence β-catenin availability and transcriptional activity through noncanonical mechanisms, including junctional disruption, proteolytic interference with regulatory complexes, and interaction with inflammatory, immune, and metabolic pathways. However, these findings derive largely from simplified model systems and should be interpreted as biologically plausible rather than definitive for human disease. Rather than acting as a dominant oncogenic driver, β-catenin appears to function as an integrative signaling node within a complex network shaped by chronic microbial and inflammatory stress. The principal contribution of this review lies in critically integrating dispersed evidence across study types while explicitly distinguishing association, mechanistic plausibility, and causality. Future longitudinal human studies and mechanistically informed experimental models are required to clarify whether modulation of periodontal dysbiosis or associated signaling pathways has relevance for OSCC risk assessment or prevention. Full article

Tillage–residue management is a controllable lever for improving maize yield and system resilience under climate variability. Here we propose a mixed-effects spatiotemporal learning framework (ME-LSTM) that integrates multi-source observations to enable robust yield prediction and management system evaluation across heterogeneous sites and years. First, we construct multi-year sliding-window inputs to represent legacy effects and cumulative influences of past management and environment. Second, a deep temporal encoder learns nonlinear dependencies from climate–soil–remote-sensing sequences to enhance interannual extrapolation. Third, a mixed-effects module explicitly separates management fixed effects from hierarchical random effects (e.g., source/study, site, year, and plot), absorbing source-specific biases and unobserved heterogeneity while improving interpretability. Finally, we parameterize management × climate/soil interactions to quantify system-specific sensitivities to environmental drivers and to support scenario-based comparison and recommendation of management options. Across multi-ecological maize datasets, ME-LSTM achieved an R² of 0.8989 with an RMSE of 309.83 kg ha⁻¹ on the test set. Ablation analyses show that removing remote-sensing features or ground-based temporal information substantially degrades performance, confirming the complementary value of multi-source fusion. Benchmarking against strong temporal baselines (LSTM, GRU, BiGRU, and Transformer) further demonstrates consistent accuracy gains of ME-LSTM, highlighting its suitability for small-sample, noisy, and hierarchically structured agricultural data. Overall, ME-LSTM provides an interpretable and scalable tool for climate-adaptive optimization of tillage–residue management and supports robust, actionable decision-making across diverse agro-ecological conditions. Full article

Background: Parenteral nutrition (PN) is a life-sustaining therapy essential for patients who are unable to meet their nutritional needs enterally. However, individualized PN formulations impose substantial economic burdens on healthcare systems. This study aims to quantify the cost of individualized PN bags across different patient populations and identify key cost drivers to inform cost-effective clinical practice and policy development. Methods: A retrospective cohort study was conducted at King Fahad Medical City, Riyadh, Saudi Arabia, analyzing 900 unique patient-specific PN orders between February 2023 and August 2023. Patients were stratified into three groups: adults (≥18 years), pediatrics (1 month to 17 years), and neonates (<1 month), with 300 unique patients per group. The cost assessment included macronutrients, micronutrients, consumables, equipment, and personnel time, all measured using a standardized work sampling methodology. Descriptive statistics characterized demographic and clinical profiles. One-way ANOVA was used to compare costs across groups, and multivariate linear regression identified significant cost predictors, with log-transformation applied to address the skewness in the cost data. Results: Mean cost per PN bag varied significantly among patient groups (ANOVA, p < 0.001): adults 517.1 ± 274 SAR, pediatrics 383.2 ± 86.75 SAR, and neonates 243.14 ± 98 SAR. We found that PN volume, lipid dose, and the number of additives were the primary modifiable drivers of PN cost. Multivariate regression analysis identified PN volume (β = 0.182, p < 0.001), lipid dose (β = 0.145, p = 0.002), and number of additives (β = 0.098, p = 0.028) as significant predictors of cost, explaining 91.2% of the cost variance (R² = 0.912). Consumables contributed 18–22% of total costs across groups. Pediatric patients demonstrated markedly longer therapy duration (median 98 days, IQR 65–142) compared to adults (median 18 days, IQR 8–35) and neonates (median 24 days, IQR 12–42). Conclusions: This study provides the first stratified, real-world cost benchmarks for individualized PN in a Saudi tertiary-care setting and quantifies actionable cost drivers. Actionable implications include standardizing stable-patient procedures, implementing pharmacist-led appropriateness screening, and earlier transition to enteral nutrition to reduce costs while maintaining quality of care. Future research should evaluate the cost-effectiveness of standardized versus individualized formulations and investigate the relationship between cost variations and clinical outcomes. Full article