Search Results (2,799)

This study examines the transformation of literary translation practices in Georgia from the Soviet era to the post-Soviet and neoliberal periods, using postcolonial translation theory as the main analytical lens. Translation is treated not merely as a linguistic transfer but as a process shaped by ideological control, cultural representation, and global power hierarchies. In the Soviet era, censorship policies rooted in socialist realism imposed direct ideological interventions; children’s literature such as Maya the Bee and Bambi exemplified how religious or individualist themes were replaced with collectivist narratives. In the post-Soviet period, overt censorship has largely disappeared; however, structural factors—including the absence of a coherent national translation policy, economic precarity, and dependence on Western funding—have become decisive in shaping translation choices. The shift from Russian to English as the dominant source language has introduced new symbolic hierarchies, privileging Anglophone literature while marginalizing regional and non-Western voices. Drawing on the Georgian Book Market Research 2013–2015 alongside archival materials, paratextual analysis, and contemporary case studies, including the Georgian translation of André Aciman’s Call Me By Your Name, the study shows how translators negotiate between market expectations, cultural taboos, and ethical responsibility. It argues that translation in Georgia remains a contested site of cultural negotiation and epistemic justice. Full article

With the increasing switching frequencies and power densities in modern power converters, the prediction and mitigation of common-mode (CM) noise are becoming increasingly essential. Even though powerful, simulation methods are hindered by the difficulties in modeling power semiconductors and the long simulation time. As an alternative, the measurement-based substitution model is demonstrated in the paper, which simplifies the non-linear converter with a linear circuit network with multiple independent sources. Transfer functions are then defined and characterized to evaluate the conversion ratio from different sources to the CM noise produced on the attached cables. Good agreements are observed between the predicted and measured CM noise under several test conditions. Additionally, the proposed method facilitates dominant noise source identification and the corresponding noise suppression. The proposed method offers advantages over the existing approach, including simplicity in the characterization of transfer functions and the least disturbance to the test setup. Full article

Urban heat exposure has become an increasingly critical environmental issue under the dual pressures of global climate warming and rapid urbanization, posing significant threats to public health and urban sustainability. However, conventional linear regression models often fail to capture the complex, nonlinear interactions among multiple environmental factors, and studies confined to single LCZ types lack a comprehensive understanding of urban thermal mechanisms. This study takes the central urban area of Zhengzhou as a case and proposes an integrated “Local Climate Zone (LCZ) framework + random forest-based multi-factor contribution analysis” approach. By incorporating multi-temporal Landsat imagery, this method effectively identifies nonlinear drivers of heat exposure across different urban morphological units. Compared to traditional approaches, the proposed model retains spatial heterogeneity while uncovering intricate regulatory pathways among contributing factors, demonstrating superior adaptability and explanatory power. Results indicate that (1) high-density built-up zones (LCZ1 and E) constitute the core of heat exposure, with land surface temperatures (LSTs) 6–12 °C higher than those of natural surfaces and LCZ3 reaching a peak LST of 49.15 °C during extreme heat events; (2) NDVI plays a dominant cooling role, contributing 50.5% to LST mitigation in LCZ3, with the expansion of low-NDVI areas significantly enhancing cooling potential (up to 185.39 °C·km²); (3) LCZ5 exhibits an anomalous spatial pattern with low-temperature patches embedded within high-temperature surroundings, reflecting the nonlinear impacts of urban form and anthropogenic heat sources. The findings demonstrate that the LCZ framework, combined with random forest modeling, effectively overcomes the limitations of traditional linear models, offering a robust analytical tool for decoding urban heat exposure mechanisms and informing targeted climate adaptation strategies. Full article

Background/Objectives: Stroke survivors with dysphagia are usually fed with different feeding routes ranging from oral to percutaneous endoscopic gastrostomy (PEG). However, the impact of the feeding route on the gastric myoelectric activity (GMA) is little-studied. This work examined the effect of feeding route on GMA changes in stroke survivors with dysphagia. Methods: This study included 50 patients (20% women) who were divided into three groups based on their feeding route: an oral group (n = 20), a nasogastric group (NGT) (n = 20), and a PEG group (n = 10). For all participants, a nutritional assessment was conducted, and the GMA was measured using a transcutaneous multichannel electrogastrogram (EGG) with a water load satiety test before and after water loading. The EGG-related parameters used in the analysis included the average power distribution by frequency region and the average dominant frequency (ADF). Results: The study sample experienced ischemic stroke (66%) or hemorrhagic stroke (34%). At the baseline phase, the PEG group exhibited significantly longer periods of normogastria compared to the NGT and oral groups. Moreover, protein intake was significantly higher in the PEG tube feeding group compared to the other groups. Based on the type of stroke, the ischemic stroke group showed significantly higher tachygastria periods during postprandial EGG recording (p = 0.022). The energy and protein consumptions were significantly higher in the hemorrhagic stroke group (p = 0.001, p = 0.028, respectively). Conclusions: The GMA pattern is distinctive for the type of stroke. The PEG feeding route showed more periods with normogastria and the best protein intake. Full article

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

The coupled factors of high temperature, high humidity, and high salinity in tropical marine atmospheres severely threaten the long-term service performance of power transmission and transformation infrastructure. This paper establishes an accelerated cyclic testing protocol (salt spray → drying → damp heat → drying) to evaluate performance and elucidate the dynamic corrosion failure mechanisms of the organic Zn14Al1.4 composite coating. By integrating multiphysical characterization techniques (SEM, EDS, XPS) with electrochemical analysis, this study for the first time elucidates the dynamic transformation of corrosion products: initially dominated by Zn(OH)₂, progressing to complex passive phases such as Zn₅(OH)₈Cl₂·H₂O, Zn₅(OH)₆(CO₃)₂, and Zn₆Al₂(OH)₁₆CO₃ in the mid-term, and ultimately dominated by Fe-based products (FeO, Fe₂O₃, Fe₃O₄, FeOOH) that drive interfacial failure. And a four-stage corrosion evolution model was defined: incubation period, accelerated degradation phase, substrate nucleation stage, and catastrophic failure phase. The investigation reveals a shift in the coating/substrate interface failure mechanism from purely physical barrier effects to electrochemical synergy, providing a theoretical framework for the optimized design and service-life prediction of anticorrosive coatings for transmission and transformation equipment in tropical environments. Full article

Graphene/h-BN/Si heterostructures show considerable potential for future use in infrared detection and photovoltaic technologies due to their adjustable electrical behavior and well-matched interfacial structure. The near-lattice match between graphene and hexagonal boron nitride (h-BN) enables the deposition of low-defect-density graphene on h-BN surfaces. This study presents a thorough exploration of the low-frequency electrical noise behavior of graphene/h-BN/Si heterojunctions under both forward and reverse bias conditions at room temperature. Graphene nanolayers were directly grown on h-BN films using microwave plasma-enhanced CVD. The h-BN layers were formed by reactive high-power impulse magnetron sputtering (HIPIMS). Four h-BN thicknesses were examined: 1 nm, 3 nm, 5 nm, and 15 nm. A reference graphene/Si junction (without h-BN) prepared under identical synthesis conditions was also studied for comparison. Low-frequency noise analysis enabled the identification of dominant charge transport mechanisms in the different device structures. Our results demonstrate that grain boundaries act as dominant defects contributing to increased noise intensity under high forward bias. Statistical analysis of voltage noise spectral density across multiple samples, supported by Raman spectroscopy, reveals that hydrogen-related defects significantly contribute to 1/f noise in the linear region of the junction’s current–voltage characteristics. This study provides the first in-depth insight into the impact of h-BN interlayers on low-frequency noise in graphene/Si heterojunctions. Full article

Background: Handgrip strength asymmetry is a critical yet underexplored basketball component. While the digit ratio (2D:4D) is linked to strength, its interplay with age, body composition, and biomechanics is unclear. This study aimed to quantify these independent and interactive effects on asymmetry in female basketball players. Methods: Maximum handgrip strength was measured bilaterally in three arm postures in 26 adolescent and adult players. Linear Mixed Model with a random intercept tested the effects. Results: Omnibus tests revealed no statistically significant main effects or interactions for age group, lean body mass (LBM), or 2D:4D ratio. However, a planned contrast showed that asymmetry was significantly lower in an overhead arm posture compared to an extended arm posture (p = 0.035). A simulation-based power analysis determined the study was significantly underpowered (11.5%) to detect small-to-medium interaction effects. Conclusions: While biomechanical position subtly modulates strength asymmetry, the influence of age, lean mass, and digit ratio may be negligible or require substantially larger samples to detect. Individual differences, which accounted for 57% of the variance, appear to be the dominant drivers of handgrip asymmetry in this athletic cohort, highlighting the complexity of strength imbalances and the critical need for adequately powered research in this domain. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on lagged and accumulative effects. To address this gap, a drought sensitivity model was established by integrating both lagged and accumulative effects derived from long-term remote sensing datasets. To leverage both predictive power and interpretability, the XGBoost–SHAP framework was employed to model nonlinear associations and identify the threshold effects of driving factors. In addition, the Geodetector model was applied to examine spatially explicit interactions among multiple drivers, thereby uncovering the coupling effects that jointly shape forest drought sensitivity across China. The results reveal the following: (1) Drought had lagged and accumulative effects on 99.52% and 95.55% of forest GPP, with evergreen broadleaf forest showing the strongest effects and deciduous needleleaf forest the weakest. (2) Evergreen needleleaf forests have the highest proportion of extremely high drought sensitivity (16.94%), while deciduous needleleaf forests have the least (1.02%), and the drought sensitivity index declined in 67.12% of forests over decades. (3) Temperature and precipitation are the primary drivers of drought sensitivity, with clear threshold effects. Evergreen forests are mainly driven by climatic factors, while forest age is a key driver in deciduous needleleaf forests. (4) Interactive effects among multiple factors significantly amplify spatial variations in drought sensitivity, with water–heat coupling dominating in evergreen forests and structure–climate interactions prevailing in deciduous forests. Full article

Background/Objectives: Previous multi-ethnic genome-wide association studies (GWAS) have identified the GALNT13 rs10196189 polymorphism as a potential genetic marker linked to sprint–power performance. However, its relevance in East Asian populations, particularly the Han Chinese, remains untested. This study aimed to replicate the association of rs10196189 with elite sprint–power athlete status in Han Chinese males and examine its potential influence on physical performance traits and tissue-specific gene regulation. Methods: A total of 188 healthy Han Chinese males (49 elite sprint–power athletes and 139 non-athletic controls) were genotyped using the TaqMan assay. Assessments included strength, sprint, jump, anaerobic power, DXA-derived body composition, and muscle ultrasound. Logistic regression and ROC analyses evaluated the predictive value of rs10196189. Linear regression models adjusted for age and BMI tested genotype–phenotype associations. Tissue expression and functional networks were analyzed using GTEx and HumanBase databases. Results: The G allele frequency was significantly higher in athletes (12.2%) than in controls (5.4%, p = 0.042). Dominant and additive models effectively predicted athlete status (OR = 2.53–2.58, p < 0.05). Although most traits showed no significant associations post-correction, medial gastrocnemius thickness showed a nominal association (β = 0.371, p = 0.011). Functional analyses revealed high GALNT13 expression in brain tissue and co-expression networks enriched in synaptic signaling and glycosylation pathways. Conclusions: This is the first study to validate the association of GALNT13 rs10196189 with elite athletic status in Han Chinese males. Findings provide novel population-specific evidence and propose tissue-specific glycosylation and neural mechanisms as pathways linking this variant to sprint–power phenotypes. Full article

Enhancer–promoter interactions (EPIs) play a key role in epigenetic regulation of gene expression, dominating cellular identity and functional diversity. Dissecting these interactions is crucial for understanding transcriptional regulatory networks and their significance in cell differentiation, development, and disease. Here, we propose a novel deep learning framework, EPIFBMC (Enhancer-Promoter Interaction prediction with FBMC network) that leverages DNA sequence and genomic features for accurate EPI prediction. The FBMC network consists of three key modules: the Four-Encoding module first encodes the DNA sequence in multiple dimensions to extract key sequence information; then the BESL (Balanced Ensemble Subset Learning) adopts an integrated subset learning strategy to optimize the feature-learning process of positive and negative samples; finally, the MCANet module completes the training of EPI prediction based on a Multi-channel Network. We evaluated EPIFBMC on three cell line datasets (HeLa, IMR90, and NHEK), and validated its generalizability across three independent datasets (K562, GM12878, HUVEC) through cross-cell-line experiments, comparing favorably with state-of-the-art methods. Notably, EPIFBMC balances genomic feature richness and computational complexity, significantly accelerating training speed. Ablation studies identified two key DNA sequence features—positional conservation and positional specificity score—which showed critical predictive value across a benchmark dataset of six diverse cell lines. The computational testing show that EPIFBMC shows excellent performance in the EPI prediction task, providing a powerful tool for decoding gene regulatory networks. It is believed that it will have important application prospects in developmental biology, disease mechanism research, and therapeutic target discovery. Full article

Facing challenges of low efficiency and severe wear in deep hard formations with conventional drilling bits, this study investigates the synergistic rock-breaking technology combining a pulsed CO₂ laser with mechanical bits. The background highlights the need for novel methods to enhance drilling speed in high-strength, abrasive strata where traditional bits struggle. The theoretical analysis explores the thermo-mechanical coupling mechanism, where pulsed laser irradiation rapidly heats the rock surface, inducing thermal stress cracks, micro-spallation, and strength reduction through mechanisms like mineral thermal expansion mismatch and pore fluid vaporization. This pre-damage layer facilitates subsequent mechanical fragmentation. The research employs finite element numerical simulations (using COMSOL Multiphysics with an HJC constitutive model and damage evolution criteria) to model the coupled laser–mechanical–rock interaction, capturing temperature fields, stress distribution, crack propagation, and assessing efficiency. The results demonstrate that laser pre-conditioning significantly achieves 90–120% higher penetration rates compared to mechanical-only drilling. The dominant spallation mechanism proves energy-efficient. Conclusions affirm the feasibility and significant potential of CO₂ laser-assisted drilling for deep formations, contingent on optimized laser parameters, composite bit design (incorporating laser transmission, multi-head layout, and environmental protection), and addressing challenges, like high in-situ stress and drilling fluid interference through techniques like gas drilling. Future work should focus on high-power laser downhole transmission, adaptive control, and rigorous field validation. Full article

This paper addresses stability issues in modern power grids arising from extensive integration of power electronic converters, which introduce complex multi-time-scale interactions. A symbolic simplification method is proposed to accurately model grid-connected converter dynamics, significantly reducing computational complexity through transfer function approximations and yielding efficient reduced-order models. An impedance-based approach utilizing impedance ratio (IR) is developed for stability assessment under active-reactive (PQ) and active power-AC voltage (PV) control strategies. The impacts of Phase-Locked Loop (PLL) and proportional-integral (PI) controllers on system stability are analysed, with a particular focus on quantifying remote converter interactions and delineating stability boundaries across varying network strengths and configurations. Furthermore, time-scale separation effectively simplifies Multi-Voltage Source Converter (MVSC) systems by minimizing inner-loop dynamics. Validation is conducted through frequency response evaluations, IR characterizations, and eigenvalue analyses, demonstrating enhanced accuracy, particularly with the application of lead–lag compensators within the critical 50–250 Hz frequency band. Time-domain simulations further illustrate the adaptability of the proposed models and reduction methodology, providing an effective and computationally efficient tool for stability assessment in converter-dominated power networks. Full article