Search Results (164)

(1) Background: Climate change has intensified extreme heat and localized rainfall, exposing South Korea’s protected trees to new risks. Despite their ecological and cultural value, prior research has been largely local or qualitative, leaving little basis for nationwide prioritization. (2) Methods: We developed a composite risk index that integrates heat and rainfall exposure with species sensitivities, covering nearly the entire national inventory (≈10,000 individuals). Risks were calculated at the tree level, aggregated to district, provincial, and national scales, and tested for robustness across weighting and normalization choices. Spatial clustering was assessed with Moran’s I and LISA. (3) Results: High-risk clusters were consistently identified in southern and southwestern regions. Mean and tail indicators showed that average-based approaches obscure extreme vulnerabilities, while LISA confirmed significant High–High clusters. Rankings proved robust across scenarios, indicating that results reflect structural signals rather than parameter settings. Priority areas defined by the presence of extreme-risk individuals emerged as stable candidates for intervention. (4) Conclusions: The study establishes a transparent, operational rule for prioritization and offers tailored strategies—such as drainage infrastructure, shading, and root-zone management—while informing medium-term planning. It provides the first nationwide, empirically grounded framework for conserving protected trees under climate transition. Full article

The safety and reliability of modern power systems are increasingly challenged by adverse environmental conditions. (1) Background: Ice accumulation on power transmission lines is recognized as a severe threat to grid stability, as tower collapse, conductor breakage, and large-scale outages may be caused, thereby making accurate monitoring essential. (2) Methods: A rule-driven and interpretable stereo vision framework is proposed for three-dimensional (3D) detection and quantitative measurement of transmission line icing. The framework consists of three stages. First, adaptive preprocessing and segmentation are applied using multiscale Retinex with nonlinear color restoration, graph-based segmentation with structural constraints, and hybrid edge detection. Second, stereo feature extraction and matching are performed through entropy-based adaptive cropping, self-adaptive keypoint thresholding with circular descriptor analysis, and multi-level geometric validation. Third, 3D reconstruction is realized by fusing segmentation and stereo correspondences through triangulation with shape-constrained refinement, reaching millimeter-level accuracy. (3) Result: An accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03% were achieved in contour extraction, while a precision of 90%, recall of 82%, and an F1-score of 0.8594 with real-time efficiency (0.014–0.037 s) were obtained in stereo matching. Millimeter-level accuracy (Mean Absolute Error: 1.26 mm, Root Mean Square Error: 1.53 mm, Coefficient of Determination = 0.99) was further achieved in 3D reconstruction. (4) Conclusions: Superior accuracy, efficiency, and interpretability are demonstrated compared with two existing rule-based stereo vision methods (Method A: ROI Tracking and Geometric Validation Method and Method B: Rule-Based Segmentation with Adaptive Thresholding) that perform line icing identification and 3D reconstruction, highlighting the framework’s advantages under limited data conditions. The interpretability of the framework is ensured through rule-based operations and stepwise visual outputs, allowing each processing result, from segmentation to three-dimensional reconstruction, to be directly understood and verified by operators and engineers. This transparency facilitates practical deployment and informed decision making in real world grid monitoring systems. Full article

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

This paper reconceptualizes intellectual safety in mathematics spaces by centering the voices and lived experiences of BIPOC students. The marginalization of BIPOC students is compounded by structural racism, historical exclusion, and deficit narratives that continue to shape academic environments, especially in mathematics contexts. While definitions of intellectual safety reflect white, Eurocentric norms, we argue that for BIPOC students, intellectually safe environments must be anti-racist, culturally responsive, and rooted in belonging. We started with existing definitions of intellectual safety and incorporated a more critical approach to sense of belonging. Through ethnographic research design we gathered student interviews and daily journal entries from a 12-day anti-racist mathematics summer institute for secondary students. Analysis revealed that while existing attributes captured much of the scholar’s joy, cultural affirmation, and belonging, new themes, like pride/confidence, clarity/transparency, and being listened to, emerged directly from how students experienced intellectual safety in practice. This led to a refinement of our initial conceptualization. This study provides insight into how intellectual safety manifests in a space intentionally designed to support BIPOC youth in exploring mathematics in agentic and culturally sustaining ways. Full article

We study rainbow solutions to linear equations modulo a prime p, where the residue classes are partitioned into n color classes. Using the Fourier method, we derive a universal lower bound that depends only on the class densities and a single spectral parameter: the Fourier bias (the largest nontrivial Fourier coefficient) of each class. When the biases are at the square-root cancellation scale $p^{-1/2}$ (for random colorings, up to a $\sqrt{logp}$ factor), the bound recovers the optimal growth $p^{n-1}$ with an explicit leading constant and negligible error. Our results complement recent work: in low-bias regimes (pseudorandom or random) they yield sharper quantitative bounds with transparent constants, and the bound requires no extra hypotheses such as coefficient separability. Full article

Missing values in hourly outdoor air temperature series are common and can bias building energy assessments that rely on uninterrupted temperature profiles. This paper examines how the polynomial degree can be used to reconstruct incomplete temperature data from the duration curve, which affect the energy indicators of an air-source heat pump (ASHP). Using an operational dataset from Opole, Poland (1 September 2019–31 August 2020; 5.1% gaps), global polynomials of degree n = 3…11 were fitted to the sorted hourly temperatures, and the reconstructions were mapped back to time. The reconstructions drive a building–ASHP model evaluated for two supply-water regimes (L_WT, leaving water temperature = 35 °C and 45 °C). Accuracy is assessed with mean absolute error (MAE), root-mean-square error (RMSE), and R² on observed, filled, and full subsets—including cold/hot tails—and propagated to energy metrics: seasonal space-heating demand (Q_season); electricity use (E_el); seasonal coefficient of performance (SCOP); peak electrical power (P_el,max); seasonal minimum coefficient of performance (COP_min); and the share of error due to filled hours (WFE_fill). All degrees satisfy $\left|{RE}_{Q_{season}}\right|\le 2\%$. For L_WT = 35 °C, relative changes span ${RE}_{E_{el}}$ ≈ −2.22…−1.63% and ${RE}_{N_{el,max}}$ ≈ −21.6…−7.7%, with ${ER}_{SCOP}$ ≈ +0.53…+0.80%. For L_WT = 45 °C, ${RE}_{E_{el}}$ remains ≈ −0.43% across degrees. A multi-criterion selection (seasonal bias, stability of energy indicators, tail errors, and WFE_fill) identifies n = 7 as the lowest sufficient degree: increasing n beyond seven yields negligible improvements while raising the overfitting risk. The proposed, data-driven procedure makes degree selection transparent and reproducible for gap-filled temperature inputs in ASHP studies. Full article

Despite major policy, industry, and individual efforts to reduce global environmental damage, the industry-induced carbon footprint continues to persist under changing geographical patterns. Having shifted significantly from advanced economies to emerging economies and developing world regions, greenhouse gas emissions from footprint-heavy activities, such as raw material sourcing and waste disposal, are not addressed by institutional and corporate solutions due to different regional standards or the overall absence of mandatory reporting. Rooted in the analysis of industry practices and past literature, the present research presents an integrated theme-based perspective on the interplay between focal firms and their suppliers in the context of advanced and emerging economies in underreported Scope 3 activity carbon footprint management. We argue that it is technology-driven unified efforts, which enforce factors such as traceability, transparency, and predictive and prescriptive capabilities within Scope 3 activities, that need to be addressed to ensure the activation and maintenance of a truly sustainable global value chain (GVC). By departing from traditional command-and-control practices and extending upon the existing governance-focused framework of sustainable value creation, this paper highlights the essential co-creating stance of non-focal actors in achieving a circular approach to sustainability within GVCs. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support. Forecasting hydroelectricity generation is vital for utilizing alleviating resources effectively, optimizing energy production, and ensuring sustainability. This paper provides an innovative approach to hydroelectricity generation forecasting (HGF) of a 138 MW hydroelectric power plant (HPP) in the Eastern Mediterranean by taking electricity productions from the remaining upstream HPPs on the Ceyhan River within the same basin into account, unlike prior research focusing on individual HPPs. In light of tuning hyperparameters such as number of trees and learning rates, this paper presents a thorough benchmark of the state-of-the-art tree-based machine learning models, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM). The comprehensive data set includes historical hydroelectricity generation, meteorological conditions, market pricing, and calendar variables acquired from the transparency platform of the Energy Exchange Istanbul (EXIST) and MERRA-2 reanalysis of the NASA with hourly resolution. Although all three models demonstrated successful performances, LightGBM emerged as the most accurate and efficient model by outperforming the others with the highest coefficient of determination (R²) (97.07%), the lowest root mean squared scaled error (RMSSE) (0.1217), and the shortest computational time (1.24 s). Consequently, it is considered that the proposed methodology demonstrates significant potential for advancing the HGF and will contribute to the operation of existing HPPs and the improvement of power dispatch planning. Full article

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning framework for dam displacement prediction, combining Hydraulic–Seasonal–Temporal model (HST), Random Forest (RF), and Bidirectional Gated Recurrent Unit (BiGRU) models as base learners. A stacking strategy is employed to enhance predictive accuracy, and the Grey Wolf Optimizer (GWO) is used for hyperparameter optimization. To improve model transparency, the Shapley Additive Explanations (SHAP) algorithm is applied for interpretability analysis. Extensive experiments demonstrate that the proposed ensemble model outperforms individual models, achieving a Root Mean Squared Error (RMSE) of 0.2241 and a Coefficient of Determination (R²) of 0.9993 on the test set. The SHAP analysis further elucidates the contribution of key variables, providing valuable insights into the displacement prediction process and offering a robust technical foundation for arch dam safety monitoring and early risk warning. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

This study examines how university students perceive AI-powered tools for mediation in higher education, with a focus on the influence of communication richness and social presence on trust and the intention to use such systems. Although AI is increasingly used in educational settings, its role in handling academic mediation, where ethical sensitivity, empathy, and trust are essential, remains underexplored. To fill this gap, this study presents a model that integrates Media Richness Theory, Social Presence Theory, Technology Acceptance Models, and Trust Theory, incorporating digital fluency and conflict ambiguity as key moderating elements. Using a convergent mixed-methods design, the research involves 287 students from a variety of academic institutions. The quantitative findings indicate that students’ willingness to adopt AI mediation tools is significantly influenced by automation, efficiency, and trust, while their perceptions are shaped by how clearly the conflict is understood and by students’ digital skills. The qualitative insights reveal concerns about emotional responsiveness, transparency, and institutional capacity. According to the results, user trust rooted in perceived presence, fairness, and emotional connection is a central factor in terms of AI acceptance, and emotionally aware, transparent, algorithmic and context-sensitive design strategies should be a system-level priority for institutions when integrating AI mediation tools into academic environments. Full article

We present new spectroscopic orbits for the bright binaries Mizar B, 3 Pup, $\nu$ Gem, 2 Lac, and $\varphi$ Aql. Our analysis is based on medium-resolution ($R\approx$ 12,000) échelle spectra obtained with the 0.81-m telescope and fiber-fed eShel spectrograph of the Three College Observatory (Greensboro, NC, USA) between 2015 and 2024. Orbital elements were inferred with an affine-invariant Markov-chain Monte-Carlo sampler; convergence was verified through the integrated autocorrelation time and the Gelman–Rubin statistic. Errors quote the 16th–84th-percentile credible intervals. Compared with previously published orbital solutions for the studied stars, our method improves the root-mean-square residuals by 25–50% and bring the 1$\sigma$ uncertainties on the radial velocity (RV) semi-amplitudes down to 0.02–0.15 km $s^{-1}$. These gains translate into markedly tighter mass functions and systemic RVs, providing a robust dynamical baseline for future interferometric and photometric studies. A complete Python analysis pipeline is openly available in a GitHub repository, ensuring full reproducibility. The results demonstrate that a Bayesian RV analysis with well-motivated priors and rigorous convergence checks yields orbital parameters that are both more precise and more reproducible than previous determinations, while offering fully transparent uncertainty budgets. Full article

As global awareness of environmental and social challenges continues to rise, companies are increasingly re-evaluating how they connect with consumers. This study investigates the role of digital marketing in promoting more sustainable consumer behaviours. Based on a systematic review of peer-reviewed literature retrieved from the Scopus database, and conducted following the PRISMA framework, this research analysed 84 academic publications. The findings highlight that strategies such as personalised messaging, social media engagement, influencer collaborations, and eco-conscious branding are significantly influencing purchasing decisions. Approaches rooted in transparency, emotional storytelling, and ethical data practices appear to enhance consumer trust and strengthen brand relationships. Although the field is technically well developed, it remains underexplored in areas such as digital accessibility and ethical governance. Overall, this study suggests that, when aligned with sustainable values, digital marketing becomes more than a promotional tool—it emerges as a key driver of responsible consumption and the cultivation of long-term, value-based connections between consumers and brands. Full article

During the hydraulic performance experiment, significant vibration and noise were observed in the mixed-flow pump operating in the hump region. Cavitation occurrence in the impeller flow channels was confirmed through the transparent chamber. To analyze cavitation flow structure evolution in the mixed-flow pump, this paper integrates numerical and experimental approaches, capturing cavitation flow structures under the valley condition through high-speed photography technology. During the various stages of cavitation development, the cavitation forms are mostly vortex cavitation, cloud cavitation, and perpendicular vortex cavitation. Impeller rotation induces downstream transport of shedding cloud cavitation shedding structures. Flow blockage occurs when cavitation vortexes obstruct specific passages, accelerating cavitation growth that culminates in head reduction through energy dissipation mechanisms. Vortex evolution analysis revealed enhanced density of small-scale vortex structures with stronger localized core intensity in the impeller and diffuser. Despite larger individual vortex scales, reduced core intensity persists throughout the full flow domain. Concurrently, velocity profile characteristics across flow rates and blade sections (spanwise from tip to root) indicate heightened predisposition to flow separation, recirculation zones, and low-velocity regions during off-design operation. This study provides scientific guidance for enhancing anti-cavitation performance in the hump region. Full article