Search Results (22,849)

Green streets—streets that systematically integrate vegetation-based and nature-based solutions into the public right-of-way as part of contemporary urban green infrastructure and climate adaptation strategies—have become an increasingly important planning and design approach. While historical precedents of vegetated and tree-lined streets exist, modern green streets represent a more integrated and performance-oriented paradigm that combines stormwater management, ecosystem service provision, climate resilience, and social functions within coordinated policy and infrastructure frameworks. This review synthesizes current knowledge on green street management practices and their performance across environmental, hydrological, ecological, and socio-spatial dimensions. The analysis examines design strategies, maintenance regimes, governance arrangements, and performance assessment methods reported in the literature. Evidence indicates that well-managed green streets can significantly reduce stormwater runoff, improve water quality, mitigate urban heat, enhance biodiversity, and contribute to pedestrian comfort and neighborhood livability. However, reported outcomes vary widely depending on local climate, design specifications, maintenance intensity, and institutional capacity. Persistent research gaps include limited long-term monitoring, underrepresentation of cities in the Global South, insufficient integration of governance, economic, and social dimensions, and a lack of standardized performance metrics. Comparative and longitudinal studies remain scarce, constraining understanding of lifecycle performance and trade-offs. Future research should prioritize standardized evaluation frameworks, long-term empirical monitoring, socio-spatial equity assessments, and the integration of emerging digital technologies for real-time monitoring and decision support. Strengthening these areas is essential to support evidence-based planning and scalable implementation of green streets as a key component of sustainable urban development. Full article

Standardized operating procedures are essential for ensuring safety and reproducibility in chemical laboratory experiments. However, real-time monitoring of manual laboratory operations, such as pipetting, remains challenging due to complex human–tool interactions, temporal dependencies between procedural steps, and operator variability. In this study, we propose a vision-based deep learning framework that leverages spatiotemporal features for automated monitoring of pipetting operations using non-contact visual sensing. Briefly, human poses and pipette interactions are extracted from video recordings using a YOLO-based perception model, while temporal execution patterns are captured through bidirectional long short-term memory networks. Experimental results demonstrate that the proposed approach can reliably distinguish between standard and non-standard pipetting behaviors across multiple predefined error categories and shows improved robustness compared with static or frame-level analysis. Overall, this work demonstrates the feasibility of vision-based AI systems for objective and scalable monitoring of laboratory pipetting operations, with potential applicability to other manual laboratory procedures. Full article

The Wallonia region of Belgium aims to transition to a modern hydrogen infrastructure. Given the relatively low density of hydrogen gas, it is important to understand its nature and behavior during transport through pipelines. This study aims to observe the pressure loss in pipelines due to surface roughness with H₂ and other singular losses to find a solution to minimize the amount of pressure loss that occurs during transportation. This study involves numerical methods and gas equation models to determine the pressure loss. This analysis includes the properties of hydrogen gas, the pipeline material used, the friction factor, pipeline efficiency, and other relevant properties of hydrogen and pipelines. To address this challenge, the study integrates numerical fluid dynamics methods with structural modelling of pipeline walls. It accounts for long-term friction effects, erosion over several years, radial pressure gradients (mixing pressure drop), acceleration effects, and gravity influences, considering the non-ideal behavior of gaseous hydrogen (GH2). This study provides a systematic comparison between AGA-based analytical models and CFD simulations using a scaled pipeline approach, enabling reliable estimation of pressure losses in long-distance hydrogen pipelines. The proposed methodology integrates scaling, numerical validation, and CFD simulation to compute pressure losses in a hydrogen pipeline. Full article

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. Full article

Sustainable development is a central objective for contemporary firms. It involves both long-term organizational resilience and improved environmental, social, and governance (ESG) performance. Structural tools that support long-term stability and strategic continuity play a critical role in achieving these goals. However, their adoption depends on the interaction between formal and informal institutional forces. Drawing on institutional theory, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to data from Chinese listed firms. We examine how four institutional dimensions jointly shape structural tool adoption: governance structure, intergenerational heterogeneity, institutional and cultural context, and market-driven and mimetic forces. Structural tools facilitate governance consolidation and leadership succession, which are essential for sustainable development. Our findings show that no single institutional condition is sufficient to trigger adoption. Instead, multiple conditions must combine to enable firms to implement structural tools. The seven configurations identified reveal diverse governance paths across different institutional contexts, including complementary, substitutive, and conflicting relationships between formal and informal institutions. We also find clear causal asymmetry: the conditions that promote adoption differ fundamentally from those that inhibit it. Structural tools provide an institutional foundation for balancing short-term pressures with long-term sustainability commitments. Firms lacking these mechanisms face greater risks of leadership succession failure and long-term instability. Additional analyses using mean difference tests and fixed-effects models further confirm that structural tool adoption significantly enhances both sustainable development capacity and ESG performance. Overall, this study advances institutional theory. It shows how the interaction between formal and informal institutions shapes governance choices. It also explains how governance structures are linked to sustainable development outcomes. Full article

Adoption of electronic health records (EHRs) has significantly advanced healthcare by enabling extensive data storage and analysis for clinical decisions and research. However, sensitive personally identifiable information (PII) within EHRs presents major challenges concerning patient privacy, data security, and regulatory compliance. Effective automated de-identification techniques for detecting and removing protected health information (PHI) are thus essential. This study presents one of the first focused studies on Turkish EHR de-identification, comparing traditional sequence-based neural architectures with advanced transformer-based large language models (LLMs) for PHI detection. We introduce and publicly release a manually annotated benchmark dataset of TEHRs, covering diverse PHI types, supporting further research in Turkish clinical text. Two methodologies were evaluated: bidirectional long short-term memory (BiLSTM) models (with and without Conditional Random Fields (CRFs)) and six fine-tuned pre-trained LLMs. Experiments demonstrated the superior performance of transformer-based LLMs, achieving a macro F1 score of 92.20%, significantly outperforming traditional methods. Among sequence-based models, BiLSTM + CRF attained an 83.00% F1 score, exceeding the baseline BiLSTM 78.40%. Results highlight the potential of transformer-based models for privacy-preserving Turkish clinical text and underscore the importance of annotated benchmark datasets. Full article

Gait analysis plays a critical role in assessing mobility and identifying risks such as frailty and falls, where accurate spatiotemporal measurements are essential for early intervention, particularly in aging populations and clinical screening contexts. However, robust gait characterization remains challenging due to noise contamination and variability in sensor-based signals. To address these limitations, this study presents a stride-length estimation framework formulated as a modified processing-and-estimation pipeline integrated with Long Short-Term Memory (LSTM) networks. The pipeline includes wavelet-based denoising and cubic-spline interpolation as front-end preprocessing, followed by a Kalman-filtering stage with dynamic gain regulation guided by acceleration zero-crossing events to mitigate transient errors around abrupt turning points. Experimental data were collected from twelve healthy participants (seven females, mean age: 26.76 ± 3.01 years; five males, mean age: 25.81 ± 1.63 years) walking at self-selected speeds on a treadmill, using both an inertial sensor-based gait monitoring system and a motion capture system as the ground-truth reference. The proposed framework demonstrated a substantial improvement in stride-length estimation accuracy, reducing the absolute mean error from 29.78% to 7.77% and the standard deviation from 20.31 to 7.17. Furthermore, the LSTM models trained on Modified EKF-preprocessed data achieved superior performance metrics, with a Mean Absolute Error (MAE) of 0.0376 and a coefficient of determination (R²) of 0.7066. These results highlight the effectiveness of combining Modified EKF preprocessing with LSTM learning to enhance stride-length estimation reliability. This integrated approach offers a robust, noise-resilient solution for wearable gait analysis, providing valuable insights for clinical diagnostics, rehabilitation monitoring, and health management applications. Full article

Background: Brace adherence is a key determinant of treatment success in adolescents with idiopathic scoliosis. However, adherence is influenced by multiple clinical, psychological, and social factors, and reported wear times vary widely across studies. This systematic review and meta-analysis aimed to identify determinants of brace adherence and assess their quantitative impact on real wear. Methods: A comprehensive search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar from database inception to November 2025. A total of 1040 records were identified, 620 were screened, and 45 full-text articles were assessed for eligibility. In total, 17 studies met the inclusion criteria and were included in the qualitative synthesis, and 10 provided extractable quantitative data and were included in the meta-analysis. A random-effects model was used to calculate pooled mean differences for identified determinants, including sex, age, early adherence, and sensor-based monitoring. Results: In total, 17 studies involving 1716 adolescents were included, and 10 provided extractable quantitative data for meta-analysis. Objective sensor-based monitoring was consistently associated with higher adherence, with a pooled mean difference of 25.6 percent compared with non-sensor methods. Early adherence significantly predicted long-term compliance, with a mean difference of 9.6 percent. Younger adolescents demonstrated greater adherence than older patients, with a mean difference of 19.1 percent, while sex differences favored females but did not reach statistical significance. Psychosocial determinants such as body image perception, stress, family dynamics, and religious environment played an important role in modulating adherence. Higher body mass index (BMI) and reduced quality of life were associated with poorer compliance. Overall, studies evaluating positive determinants reported a pooled mean adherence of 89.6 percent compared with 67.7 percent in studies characterized by negative determinants. Conclusions: Brace adherence is determined by a combination of clinical and psychosocial factors. Sensor-based monitoring, strong early adherence, and supportive environments consistently enhance compliance, whereas stress, poor body image, and higher BMI hinder wear. Targeted interventions, early counseling, and standardized adherence metrics are needed to improve outcomes in brace-treated scoliosis. Full article

Seasonal groundwater (GW) pumping and climatic variability significantly impact the dynamics of greenhouse-dominated agricultural systems, yet quantitative evaluations at the local scale remain limited. This study explores non-parametric statistical and deep learning (DL) models for analyzing seasonal GW trends and predicting GW levels near greenhouse agriculture systems in Gyeongsangnam-do, South Korea. The modified Mann–Kendall (MK) test and Sen’s slope estimator were used to estimate long-term seasonal trends for the summer (wet season) and winter (dry season), based on monthly GW-level time series from six monitoring wells. Findings indicate that seasonal asymmetry is strong (winter trends have greater magnitudes and greater variability than summer trends), and that winter trends are negative (ranging from −0.45 to +1.70 m year⁻¹) and summer trends are positive (ranging from −0.02 to +0.31 m year⁻¹). At Jinju1 and Jinju4, statistically significant increasing trends were observed in both seasons (p < 0.05), but at other stations, weak or non-significant trends were observed due to short records or high variance. Long short-term memory (LSTM) and spatio-temporal graph neural network (STGNN) models were deployed and compared to predict at the GW level. The STGNN was found to be superior to LSTM in terms of R² (0.799–0.994) and reduced RMSE of up to 64.6, especially in winter, when spatially synchronized pumping is dominant in GW behavior. Despite advanced modeling, there is a serious concern about data limitations. Findings show that combining seasonal trend analysis with spatiotemporal modeling of DLs can significantly enhance knowledge and forecasting of GW dynamics in intensive greenhouse farming. Full article

This study evaluates the feasibility of using a ground-coupled ammonia heat pump as a heat source for the district heating system in Ustka, Poland. A three-dimensional transient thermal model of a 122-borehole field was developed in ANSYS 2023 R1 using local geological data and hourly meteorological inputs. Three extraction loads—0.50, 0.75, and 1.00 MW—were analysed, together with regeneration periods of one month (August) and six months following the heating season. Ground temperatures were assessed across all geological layers down to 250 m. The simulations show that each of the tested loads leads to a noticeable and lasting reduction in ground temperature. For 1.00 MW, the temperature in the main heat-exchange layers remains more than 2 K below the initial value even after six months of regeneration. At 0.75 MW the deficit is smaller but still persists in the layers that dominate heat transfer. Even the 0.50 MW scenario does not return to thermal balance: the active layers stay more than 1 K cooler after the regeneration period, indicating cumulative long-term cooling. Although the model includes standard engineering simplifications, the large-scale thermal behaviour is consistent across all scenarios. The analysis shows that the analysed GSHP (ground-source heat pump) configuration cannot serve as a primary heat source for the Ustka network in the analysed configuration. Alternative low-emission solutions, such as air-source heat pumps supported by renewable electricity, are more suitable for this site. Full article

Aim: This study compared spectral profile analysis thresholds, speech-in-noise perception, and cerebral asymmetry among Carnatic musicians, Bharatanatyam dancers, and non-trained individuals and examined the influence of training duration on these measures. Method: A total of 105 right-handed adults (18–30 years) with normal hearing were divided into Carnatic musicians (n = 35), Bharatanatyam dancers (n = 35), and non-trained controls (n = 35). Spectral stream segregation was measured using the spectral profile analysis task, and speech-in-noise perception was evaluated using the Kannada QuickSIN under right, left, and binaural conditions. Cerebral asymmetry was derived from the Laterality Index. As data were non-normally distributed, non-parametric tests were used. Results: Significant group differences emerged for spectral profile thresholds, with dancers outperforming musicians and controls. Both trained groups showed superior speech-in-noise performance compared to non-trained individuals across all listening conditions, though no differences were observed between musicians and dancers. Non-trained listeners displayed a clear right-ear advantage, whereas trained groups showed minimal or no hemispheric asymmetry. Training duration negatively correlated with selected spectral profile thresholds in both trained groups and with binaural SNR-50 in dancers, indicating training-related auditory enhancement. Conclusions: Musicians and dancers demonstrate better spectral discrimination, improved speech-in-noise perception, and reduced cerebral asymmetry compared to non-trained peers. These findings underscore training-induced auditory neuroplasticity and suggest that long-term engagement in music or dance promotes efficient auditory processing and greater bilateral hemispheric involvement. Full article

As global climate change intensifies and extreme weather events become more frequent, understanding the historical spatial distribution of vegetation is of critical importance. However, most vegetation studies are temporally limited to the post-1980 period due to satellite data constraints. To bridge this gap, we integrated tree-ring width chronologies from the International Tree-Ring Databank with Landsat-derived Enhanced Vegetation Index (EVI) data and evaluated three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to reconstruct annual, spatially explicit EVI for the period 1850–1985 in Diqing, Yunnan, China. RF regression was the best among the three with highest adjusted R² (0.90) and lowest Root Mean Square Error (0.032). The RF-based reconstruction indicated a consistent increase in regional EVI from 1991 to 2005. Breakpoint analysis identified three distinct sub-periods, each with unique spatiotemporal variation patterns. In current times, the EVI value shows a significant positive correlation with average temperatures in June, July, August, and December. In the contemporary period, it also correlates significantly and positively with winter average temperatures, March average precipitation, and spring average precipitation. The spatial pattern for the past 100 years reflects the succession of the local vegetation ecosystem and provides an insight into the influences of natural disturbances (low-temperature damages and droughts) on vegetation growth. This study demonstrates the feasibility of reconstructing high-resolution, long-term vegetation spatial dynamics using tree-ring proxies and machine learning. Full article

Acidity is a primary factor leading to the deterioration of paper-based cultural heritage, and deacidification treatment is a crucial preventive conservation measure for extending their lifespan. Traditional deacidification techniques, such as the particle suspension method and vapor phase method, have limitations in terms of penetration uniformity, treatment efficacy, or safety. Nanoscale alkaline materials, represented by nano-calcium hydroxide and nano-magnesium hydroxide, offer an innovative solution with the potential to achieve more uniform, efficient, and long-lasting paper deacidification, owing to their high specific surface area, enhanced reactivity, and superior penetration capacity derived from the nanoscale dimension. It is important to note that the realized uniformity and depth of treatment are contingent upon substrate properties (e.g., fiber density, porosity) and application parameters. This paper provides a systematic review of the main types of nanomaterials applied in the deacidification of paper artifacts—including their synthesis and dispersion stabilization methods—application techniques (such as immersion and spraying) and performance evaluation systems (including pH value, alkaline reserve, and mechanical properties). Through comparative analysis and case studies, the advantages and current challenges of nano-deacidification technology are elaborated. Finally, future directions for nano-deacidification technology are discussed, particularly focusing on material optimization, standardized evaluation, and prospects for scalable application tailored to the practical needs of cultural heritage conservation. Full article

The agrarian sector, as the key source of livelihood in Sub-Saharan Africa (SSA), has become highly vulnerable to changes in extension service deliveries. Farmers mainly lack access to technical advice, financial credits, farming inputs and mechanization tools while environmental challenges reinforce the adaptation of sustainable management practices. Therefore, an understanding how multi-functional actor relationships determine agricultural knowledge and information (AKI) sharing is required. This study contributes to filling this gap by characterizing horizontal and vertical interactions. By applying a social network analysis, we mapped actor relations along public–private-community co-operations to provide insights into structural dependencies at different administrative levels. Related to three sites distributed over Burkina Faso and Ghana, local perceptions were collected in stakeholder workshops to generate social network narratives. These narratives were analyzed by various metrics to identify patterns of partnerships and key actors. Study results reveal for Burkina Faso a slight shared network topology, while both sites in Ghana reflect a top-down flow of AKI. The statistical findings indicate that agricultural extension services are primarily delivered to farmers through a few key actors such as NGOs and farm-based organizations/cooperatives. Especially at the community level, the results show many reciprocal links between farmers, business actors and NGOs. This highlights a shift toward a pluralistic agricultural extension service system and underpins the demand for policies to support the long-term viability of these actors, in particular for regions where public extension agents are under-represented. Full article

The thioredoxin h-type (Trxh) proteins play a crucial role as convergence points within plants’ responses to abiotic and biotic stresses. Previously, we demonstrated that the protein TdTrxh2 of durum wheat (Triticum durum Desf.) has a chaperone function and it promotes tolerance to abiotic stress. The aim of this study was to evaluate the antimicrobial effect of TdTrxh2 and its role in the response of durum wheat to Fusarium graminearum attack. First, we demonstrated the involvement of TdTrxh2 in the response of durum wheat to this fungus via the analysis of its expression profile under this fungus attack. In fact, the outcomes showed that the induction of TdTrxh2 expression is spatiotemporal in leaves and roots of durum wheat under F. graminearum infection. Interestingly, this induction was accompanied by H₂O₂ accumulation under short- and long-term stress in roots and leaves, respectively. Besides, the cis elements related to the two phytohormones ET and MeJA, and those implicated in defense and wound stress, were identified in the TdTrxh2 promoter’s sequence. Second, the purified TdTrxh2 protein possessed antimicrobial effects against a diverse range of bacteria and fungi in vitro. Finally, the expression of TdTrxh2 in transgenic Arabidopsis plants enhanced their tolerance to F. graminearum attack through the activation of the two H₂O₂-scavenging enzymes, CAT and POD, and via the induction of a subset of SA- and ABA-related genes. Moreover, the exogenous SA and ABA applications improved the growth of the transgenic lines compared to the non-transformed plants. Taken together, the results highlighted that TdTrxh2 generates tolerance of durum wheat’s response to F. graminearum attack, via the regulation of H₂O₂ homeostasis and the induction of hormone-associated genes. Thus, the TdTrxh2 gene could be considered as an interesting candidate gene to improve wheat tolerance to F. graminearum attack. Full article