Search Results (3,071)

This study aims to examine the improvement of algebra problem-solving ability and metacognitive awareness among junior high school students through the use of visualization based on a deep learning approach. The research employed a quantitative method with a quasi-experimental design, specifically a pretest–posttest control group design. The population consisted of all students from public schools in Tangerang City, Indonesia. The sample comprised seventh-grade students studying algebra. A purposive sampling technique was used to determine the experimental and control groups, with a total sample size of 51 students. The instruments included an algebra problem-solving ability test consisting of nine essay questions and a metacognitive awareness questionnaire with 52 items. Data were collected using these two instruments, with a pretest administered before the intervention and a posttest administered afterward. Data analysis was conducted using a prerequisite test, continued with independent sample t-tests, nonparametric tests, ANCOVA, and multiple linear regression. The results based on statistics indicated a significant improvement in students’ algebra problem-solving ability with a large effect. Nevertheless, the absolute increase in problem-solving scores in the experimental group is very small (N-gain mean = 0.02). Additionally, metacognitive awareness was not found to be a significant predictor of problem-solving ability; instead, initial ability (pretest) emerged as the strongest predictor. Only understanding the problem has a moderate effect; planning strategies has a small effect, and otherwise there is no effect. In conclusion, the use of visualization-based worked examples with a deep learning approach has a statistically significant effect, but its impact on improving students’ abilities should be interpreted with caution. So the practical effects of the intervention are limited; however, metacognitive awareness is not the main predictor in algebra problem-solving ability. Full article

Background and Objectives: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive technique widely used for mediastinal staging and diagnosis in patients with lung cancer and extrathoracic malignancies. This study aimed to evaluate patient and procedural factors associated with malignant histopathological outcomes in individuals undergoing EBUS-TBNA for intrathoracic lymphadenopathy across three malignancy groups: primary lung cancer, extrathoracic solid organ malignancy, and hematological malignancy. Materials and Methods: This retrospective descriptive study included patients who underwent EBUS-TBNA at Ankara Bilkent City Hospital between March 2019 and December 2023. Demographic characteristics, histopathological findings, procedural details, additional sampling techniques, and imaging parameters, including FDG SUVmax values from pre-procedural PET-CT, were recorded. Histopathological outcomes were categorized as malignant or non-malignant. Binary and multinomial logistic regression analyses were performed to identify independent predictors of malignancy and to differentiate between malignancy groups and lung cancer subtypes. Results: A total of 776 patients underwent EBUS-TBNA, and 667 were included after excluding non-diagnostic samples. Malignancy was detected in 274 patients, including primary lung cancer (n = 213, 77.7%), extrathoracic malignancy (n = 43, 15.7%), and hematological malignancy (n = 18, 6.6%). Of the included patients, 426 (63.9%) were male; the median age was 63 (IQR = 16) years. Older age (OR = 1.03, 95% CI = 1.02–1.05, p < 0.001), male sex (OR = 2.05, 95% CI = 1.43–2.93, p < 0.001), and larger lymph node size (OR = 1.09, 95% CI = 1.06–1.11, p < 0.001) were independently associated with malignant outcomes. Younger age, female sex, and smaller lymph node size were associated with extrathoracic malignancy compared to primary lung cancer, while younger age was the only predictor of hematological malignancy. Larger lymph node size was inversely associated with adenocarcinoma and squamous cell carcinoma compared with small cell lung cancer. Conclusions: Older age, male sex, and larger lymph node size independently predict malignant EBUS-TBNA outcomes. Younger age and female sex favor extrathoracic malignancy, whereas small cell lung cancer is associated with more extensive nodal involvement. Additional bronchoscopic techniques may enhance diagnostic accuracy in selected patients. Full article

Universities play a critical role in knowledge creation and technological innovation, serving as key drivers of regional development. However, existing research has paid limited attention to the mechanisms through which university innovation inputs translate into firm-level performance, particularly in the context of science and technology corridors in emerging economies. This study investigates how university innovation resources affect enterprise performance in the G60 Science and Technology Corridor within China’s Yangtze River Delta, one of the country’s most dynamic innovation regions. Using a panel dataset of 55 universities across nine cities from 2008 to 2017, we employ spatial analysis and fixed-effects panel regression models to examine the relationship between university innovation inputs and firm performance and further explore the mediating roles of local human capital and firm R&D investment. The results show that university innovation inputs significantly enhance enterprise performance, although excessive human resource inputs exhibit a negative effect on both short-term and long-term outcomes. Local human capital and firm R&D investment serve as key mediating mechanisms, with input and output resources influencing enterprise performance through distinct pathways. Heterogeneity analysis reveals that non-state-owned enterprises and small- and medium-sized enterprises derive greater long-term benefits from university resources. These findings contribute to the literature by clarifying the conceptual distinction between university innovation inputs and outputs, and by demonstrating the micro-level mechanisms—R&D investment and human capital—through which university-generated knowledge affects firm performance. The results also provide empirical evidence from an emerging economic context, extending the applicability of knowledge spillover and absorptive capacity theories. Policy implications include optimizing university human resource allocation, strengthening university–enterprise collaboration, and providing targeted support for non-state-owned enterprises and SMEs. Future research may extend the analysis to include institutional factors and university heterogeneity. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

Exposure to road traffic noise is an increasing public health concern in developing countries. However, limited research has explored its effect on children’s cognitive function in contexts with common lifestyles and socioeconomic conditions in these countries. This study aims to evaluate the association between residential outdoor traffic noise exposure in Sincelejo, Colombia, the multidimensional poverty index (MPI) and the effects on cognitive functions in children with a cross-sectional deisgn. Noise levels were estimated using the CNOSSOS model and spatially linked to selective attention and working memory of children, assessed with standardized cognitive tests. Associations were estimated with logistic regression models adjusted for sociodemographic and school characteristics and stratified by MPI. Sensitivity analyses were conducted to evaluate the consistency of the associations. The results indicated a statistically significant yet weak association between a 1 dBA increase in noise levels and reduced processing speed (≤95) in selective attention tasks, particularly in the area with the highest prevalence of MPI < 50. However, sensitivity analyses did not corroborate these findings, and the observed association should therefore be interpreted with caution as exploratory and hypothesis generating. Full article

Latin America faces growing challenges in the management of municipal solid waste (MSW). This is particularly evident in medium-sized and metropolitan cities where rapid urbanization, limited infrastructure, and high proportions of organic waste (40–70%) converge. This review synthesizes the most recent advances in organic waste management, valorization strategies, environmental performance, and policy frameworks in Mexico and Latin America. To provide a comprehensive overview, evidence from studies on informal recycling systems, route optimization, sustainable landfill siting, food waste valorization, life cycle assessments (LCAs), and biogas production is integrated. Techno-economic analyses of energy recovery from organic fractions are specifically reviewed. This review highlights that valorization of organic waste through composting, anaerobic digestion, food supplementation, and bioproduct generation can reduce greenhouse gas emissions by 40–70% compared to landfilling, with AD–composting hybrids achieving the highest reductions of 60–70%. Community composting achieved moderate reductions, 30–50%, but at significantly lower cost and with greater social co-benefits. These alternatives for valorizing the organic fraction extend the lifespan of both confined and open landfills. It also contributes to mitigating the public health impacts related to open dumping, disease vectors, and contaminated leachate. In short, this review also highlights shortcomings in policy coherence, financial mechanisms, source separation, and technology adoption. A strategic framework is proposed that prioritizes decentralized treatment systems, the integration of informal recyclers, tax incentives, community-based waste separation, and planning based on Life Cycle Assessment (LCA). The findings point to a viable strategy for transitioning from landfill dependency to circular waste management systems that improve the quality of life for the population of Latin America and the Caribbean. Full article

The digital economy is reshaping urban development and may contribute to more inclusive and sustainable cities. Using the 2016 and 2017 China Migrants Dynamic Survey (CMDS), this study constructs a city-level digital economy index covering digital industrialization, industrial digitization, and digital infrastructure, and examines its effects on migrants’ settlement intentions. The results show that the digital economy significantly promotes migrants’ settlement intentions, with digital industrialization as the primary driver. The positive effect is more robust for long-term settlement intention, whereas its association with hukou transfer intention is less stable. Heterogeneity analysis shows that the effect is stronger among women and highly educated migrants, but weaker among migrants with rural hukou. It is also more pronounced in cities with lower ecological quality and varies across regions and city sizes. Mechanism analysis suggests that the digital economy promotes settlement intentions mainly through social integration and income enhancement, thereby supporting more stable and sustainable urban living by facilitating migrants’ long-term integration into host cities. Digital industrialization plays a stronger role in the social integration channel, whereas industrial digitization is more strongly linked to income enhancement. These findings suggest that digital development can contribute to inclusive and sustainable urbanization in the digital era by improving employment quality, narrowing the digital divide, strengthening migrants’ social integration, and promoting more differentiated urban governance. Full article

Sóller, a highly touristic town in Mallorca, has been affected by gentrification problems related to the tourism industry. Recently, another gentrification process has appeared, affecting the retail fabric and leading to the disappearance of traditional locally owned shops and their substitution with tourist-focused stores. Using data from different sources, such as the City Hall documentary data, the Commerce Association archives and Google Street View images, this research highlights the gentrification process affecting two of the main commercial areas of the town. The results confirm that a commercial gentrification process, already identified in large cities such as Barcelona or Venice, can also affect medium-sized towns, creating a retail mutation that impacts local residents and their shopping capabilities. Full article

Amidst escalating global economic instability, urban economic resilience has emerged as a fundamental pillar for sustainable urban development. Using a dataset of 280 prefecture-level cities in China from 2008 to 2021, this study examines the impact of financial agglomeration on urban economic resilience. The entropy weight approach is used to measure urban economic resilience. The main empirical results show that financial agglomeration has a statistically significant positive impact on urban economic resilience, mainly through two mediating channels: the promotion of technical innovation and the optimization of the industrial structure. The beneficial effects of financial agglomeration increase with city size, according to a threshold effect analysis, giving urban sustainable development a stronger boost. Furthermore, compared to resource-based cities, cities in the central and western regions, and cities with low levels of digital finance development, this promotional effect is much more noticeable in non-resource-based cities, cities in the eastern regions, and cities with a high degree of digital finance development. This study underscores the pivotal influence of financial clustering on reinforcing urban economic robustness, offering policy recommendations for fostering sustainable growth and urban development. Full article

Mobility of the migrant population plays a crucial role in shaping urban spaces, neighbourhood change and socio-economic development. While extensive research has been conducted on the spatio-temporal dynamics of migration in large metropolitan areas, there remains a notable lack of understanding of the impact of migration on medium-sized cities, on their internal spatial distribution and socio-spatial differentiation. This study aims to fill this gap by examining the urban settlement patterns of migrants in two medium-sized Danish cities: Aalborg and Odense. The research explores the intra-urban spatial distribution of various migrant groups, considering their origins and residential preferences. Additionally, it analyses the social and structural pull-factor proxies that influence these patterns, including urban housing market dynamics and access to amenities and services. Through an exploratory spatial analysis and data visualisation approach, this study reveals detailed insights into the determinants of migrant settlement. The findings indicate a significant intra-urban concentration of certain migrant groups, especially in the city centres, which often correspond to areas with a higher concentration of essential amenities. By focusing on mid-sized cities and adopting a case-based, comparative methodology through an extensive data visualisation approach, this research enhances urban science knowledge by illuminating underexplored urban contexts and providing a fresh view on the interplay between migration, urban development and spatial planning in medium-sized cities. Full article

The primary objective of water supply systems is to ensure a reliable delivery of water in appropriate quantity, quality, and pressure. Designing water supply networks involves determining their geometric layout and capacity by selecting suitable pipe routes and sizes. Since the network layout influences pipe diameters, routing and sizing should be conducted simultaneously. This paper presents an application of the fractal-based method for designing water distribution networks (WDNs) in which the pipe routes and diameters are mathematically justified. The proposed approach takes into account the total pipe length, the total angular change in pipeline routing, construction costs, and water delivery priorities. Additionally, the method was tested under both quasi-real conditions (in the virtual city of Micropolis) and in real-world complex settlement. The results of the sizing process were also compared with those obtained using the genetic algorithm approach. Verification of the proposed method in both quasi-real and real-world scenarios showed a smaller total pipe length (by 9.53% and 12.17%), a lower maximum water age (11 and 87 h), and a comparable energy demand. The SRS method enables simultaneous determination of pipe diameters and layout routing, while ensuring proper hydraulic performance of the network due to the application of evolution theory rules which results in quasi-optimal solutions for WDN designing. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

Parking management in mid-sized Latin American cities is often limited by weak enforcement, scarce off-street supply, and widespread irregular parking. This study uses a stated preference experiment to analyze parking choices among 227 drivers in Loja, Ecuador. Six choice tasks evaluated four alternatives—regulated on-street, private off-street, irregular parking, and leaving the vehicle at home—based on cost, walking distance, search time, availability, expected fines, and security. Multinomial logit (MNL) and mixed logit (ML) models were estimated, including income- and gender-based segmentations. Results show that cost (β = −0.332, p < 0.01) and walking distance (β = −0.0026, p < 0.001) are the primary determinants of formal parking choice. The willingness to pay to avoid 100 m of walking is USD 0.77 per 2-h period. Low-income users are 4.8 times more sensitive to cost. Mixed logit results reveal significant heterogeneity in preferences for cost, search time, and enforcement sensitivity. Policy simulations indicate that increasing enforcement (70% probability, USD 250 fine) reduces illegal parking demand by 93%, while lowering regulated tariffs to USD 0.50 raises its share by 4.2 percentage points. These findings support sustainable mobility policies by promoting efficient parking management, reducing illegal parking, and improving equitable access to urban space. Full article

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article