Search Results (191)

Coherent structures play a pivotal role in wall-bounded turbulence, serving as primary carriers of momentum, energy, and scalar quantities across the flow. This study examines coherent structures, specifically streamwise streaks and intense Reynolds stress regions (Q structures), within a novel DNS dataset capturing a stepped transition from pure Poiseuille flow to pure Couette flow at $R{e}_{\tau }\approx 250$, based on the stationary wall. Structures are identified using a percolation algorithm to ensure well-defined boundaries, followed by three-dimensional clustering in Cartesian coordinates. They are further classified as wall-attached or wall-detached based on their proximity to the domain walls. Intense Reynolds stress structures are categorized into quadrants according to the signs of their averaged velocity components. The statistical properties of these structures—encompassing geometric characteristics, energy content, and spatial distribution—are thoroughly analyzed. Particular emphasis is placed on how these properties evolve across the transition from Poiseuille to Couette flow. The results reveal that increasing mean shear in Couette-like cases significantly influences the energy content and spatial distribution of the structures while their geometric characteristics remain relatively consistent across the dataset. This spatial distribution is closely linked to the large-scale structures of the streamwise velocity component in Couette flow, confirming that these structures are genuine physical features rather than artificial artifacts of the flow. Full article

Perovskite solar cells (PSCs) have emerged as a rising next-generational photovoltaic technology due to low fabrication costs through solution processing as compared to traditional silicon solar cells and high-power conversion efficiency. However, the poor long-term operational stability due to environmental and mechanical degradation remains a hindrance to commercialization. Herein, self-healing polymer additives are utilized by researchers to enhance the photovoltaic performance of PSCs by enabling self-restorative behavior from physical damage or chemical degradation. This review explores the design and application of self-healing polymers in both flexible and rigid PSCs, contrasting the two main reversible bonding mechanisms: physical bonds, such as hydrogen bonds, and chemical bonds, such as dynamic covalent disulfide bonds. Physical bonds provide passive healing at ambient conditions; meanwhile, chemical bonds offer a stronger restoration under external stimuli such as heat or light. These polymers are exceptionally effective at mitigating mechanical stress and cracks in flexible PSCs and combating moisture-induced degradation in rigid PSCs. The applications of self-healing polymers are categorized based on substrate type, healing mechanism, and perovskite composition, with the benefits and limitations of each approach highlighted. Additionally, the review explores the potential of multifunctional self-healing polymers to passivate defects at the grain boundaries and on surface of perovskite films, thereby enhancing the overall photovoltaic performance. Full article

Traditional herbal prescriptions composed of multiple botanicals remain central to ethnopharmacological practice; however, their chemical basis and classification remain poorly understood. Non-volatile compound analyses of herbal medicines are well established, but comparative studies focusing on volatile organic compounds (VOCs) across multi-herbal prescriptions are scarce. To enhance the chemical understanding of traditional formulations and clarify prescription-level characteristics, this study applied headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME–GC–MS) to characterize VOC-based chemical signatures in 30 prescriptions composed of 76 herbal ingredients. Multivariate analyses such as principal component analysis, partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) enabled systematic differentiation of various prescriptions and identified 25 discriminant VOCs, 9 of which were common among multiple therapeutic categories. These shared compounds, such as 5-hydroxymethylfurfural (5-HMF) and 4H-pyran-4-one derivatives, reflect recurrent chemical patterns associated with broad-spectrum applications, whereas category-specific volatiles (including isopsoralen, senkyunolide, and fenipentol) delineated therapeutic boundaries, even among prescriptions with overlapping botanicals. Capturing both shared and distinct volatile signatures clarified ambiguous boundaries between categories such as cold, inflammation, or diabetes versus kidney disorder prescriptions, thereby linking chemical patterns with ethnopharmacological indications. Together, these findings highlight VOC profiling as a valuable diagnostic and interpretive tool that bridges traditional categorization systems with modern chemical analysis, offering a robust framework for future pharmacological and mechanistic investigations. Such an approach not only substantiates traditional categorization but also provides a practical basis for quality control and pharmacological evaluation of multi-herbal formulations. Full article

Accurately assessing rooftop photovoltaic (PV) potential requires precise identification of rooftop areas and availability. Current deep learning approaches using aerial imagery are faced with two challenges: inconsistent rooftop appearances caused by varying solar azimuths tend to mislead rooftop orientation extraction, and the existence of ancillary rooftop facilities often results in overestimation of solar potential. To tackle these challenges, a novel framework is proposed, with three components: automated extraction of rooftop areas and orientations, appearance-based estimation of rooftop availability coefficients, and PV potential calculation via a multi-orientation quantitative integration strategy. The segmentation network identifies geometric boundaries of rooftops and categorizes pitched roof segments into orientation-specific categories. High-level features of rooftop segments are then extracted from deep networks and clustered to compute availability coefficients at segment-level. Finally, the integration strategy leverages the symmetry assumption of sloped rooftops to mitigate classification errors and improve robustness in solar potential computation. Our framework is trained on the RID dataset with different category definition schemes, and estimation results are compared with solar radiation flux provided by NASA POWER. The overall relative error is less than 1%, which demonstrates the effectiveness of our framework. Full article

This study examined how variability in phonetic training input (high vs. low) influences the perception and acquisition of Spanish stop consonants by English-speaking beginners. A total of 128 participants completed 20 online identification sessions targeting /p, t, k, b, d, g/. In the high-variability condition (HVPT), learners heard tokens from six speakers, and in the low-variability condition (LVPT), all input came from a single speaker. Training followed an interleaved-talker design with immediate feedback, and perceptual learning was evaluated using a Bayesian hierarchical logistic regression analysis. Results showed improvement across sessions for both groups, with identification accuracy reaching ceiling by the end of the training sessions. Differences between HVPT and LVPT were small: LVPT showed steeper categorization trajectories in some cases due to slightly lower baselines, but neither condition yielded a measurable advantage. The pattern observed suggests that for boundary-shift contrasts such as Spanish stops, perceptual improvements are driven primarily by input quantity rather than variability. This interpretation aligns with input-based models of L2 speech learning (SLM-r, L2LP) and underscores the role of repeated exposure in restructuring phonological categories. Full article

The growth and development of plants are modulated by multiple genes, among which the LBD (Lateral Organ Boundaries Domain) family—a group of plant-specific transcription factors—plays pivotal roles. In this study, we utilized the latest reference genome to identify and characterize LBD genes in Chionanthus retusus (Oleaceae, 2n = 2x = 46) and further explored their expression profiles across the different floral development, as well as their potential functions in floral morphology development. Our analysis identified a total of 76 LBD gene family members in C. retusus, which were categorized into two major families: Class I and Class II. Class I was further subdivided into five subfamilies, while Class II comprised two subfamilies. Chromosomal mapping revealed that LBD genes are distributed across all 23 chromosomes of C. retusus. Additional analyses of gene structure, conserved domains, motifs, and synteny highlighted their structural and evolutionary conservation. Subsequent expression profiling of CrLBD genes across various floral morphologies identified three members—CrLBD3, CrLBD34, and CrLBD72—that are potentially involved in regulating floral morphology in C. retusus. Full article

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP location disclosure, categorized the topics using LDA topic modeling within the frameworks of communication privacy management, the networked public sphere, and digital democracy, and conducted sentiment analysis. We constructed separate semantic networks for positive and negative terms to examine co-occurrence patterns. The results show that Weibo users are generally negative about this policy, as IP location may reveal personally identifiable information about individuals involved in discussions of online social/political events. Mandatory transparency, while intended to enhance accountability, functions as a mandatory visibility regime that reshapes privacy boundaries and undermines inclusive deliberation. The findings contribute to the exploration of the impact of government-mandatory information privacy disclosure policies on the implementation of platform functionality, as well as changes in user sentiment, information behavior, and components of social media discourse. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that uniquely combines ABC optimization with Simulated Annealing temperature control and adaptive scout mechanisms for automated test case generation. The approach employs a four-tier categorical fitness function discriminating between boundary-valid, valid, boundary-invalid, and invalid values, with first-occurrence bonuses ensuring systematic exploration. Through comprehensive empirical validation involving 970 test suite generations across 97 parameter configurations, the hybrid algorithm demonstrates 68.3% improvement in fitness scores over pairwise testing (975.9 ± 10.6 vs. 580.0 ± 0.0, p < 0.001, d = 42.61). Statistical analysis identified three critical parameters with large effect sizes: MutationRate (d = 106.61), FinalPopulationSelectionRatio (d = 42.61), and TotalGenerations (d = 19.81). The value discrimination system proved essential, uniform weight configurations degraded performance by 7.25% (p < 0.001), while all discriminating configurations achieved statistically equivalent results, validating the architectural design over specific weight calibration. Full article

Lymphomas involving the central nervous system (CNS) have worse outcomes, including both primary and secondary CNS lymphomas, which are associated with poorer overall survival outcomes. The World Health Organization classifies CNS lymphoma as arising from the brain, leptomeninges, and spinal cord, but this simplified CNS anatomical definition fails to incorporate areas of ambiguity that can be clinically relevant for treatment decision making. In this article, we review the anatomical boundaries of CNS lymphoma within select border-zone biological structures located at the CNS borders in order to gain a consensus working definition of CNS disease boundaries. We review anatomical localizations with border-zone CNS boundaries, including the dura, cavernous sinus, circumventricular organs, pituitary gland, and cranial nerves. Though some portions of the eye would be considered CNS and others extra-CNS, recommendations for this structure are outside the scope of this review. Through this review, we examine the impact of lymphomatous invasion on select CNS-bordering anatomical structures, aiming to better define treatment categorization as CNS or extra-CNS, with a focus on B cell lymphoma types. Full article

Understanding the spatial patterns of ethnic inter-embeddedness is essential for promoting sustainable development in multi-ethnic regions. This study develops a novel “Clustered-Boundary-Cellular” typological model to interpret the spatial differentiation of traditional villages in China’s Hehuang region. Using an integrated approach that combines GIS spatial analysis (Kernel Density Estimation, Ripley’s K-function, and Standard Deviational Ellipse), spatial statistics (Global Moran’s I), and other statistical tests (Kruskal–Wallis tests and multinomial logistic regression), we categorized and analyzed 153 nationally designated traditional villages. The results indicate the following: (1) The villages exhibit significant spatial differentiation, falling into three distinct scenarios. Clustered–Isolation villages (107/153, 69.9%) are predominantly located in topographically constrained areas and display strong spatial clustering; Boundary–Permeation villages (24/153, 15.7%) are distributed along transport corridors and show the highest road density (0.55 km/km²); Cellular–Symbiosis villages (22/153, 14.4%) occur in multi-ethnic cores areas and exhibit a relatively random spatial distribution. (2) This differentiation results from the synergistic effects of multidimensional drivers: natural environmental constraints (notably elevation and proximity to rivers), religious–cultural adaptation (Global Moran’s I analysis confirms the strong clustering of Tibetan and Salar groups, reflecting distinct religious spatial logics), and economic transition dynamics (transportation infrastructure serves as a key catalyst). This study demonstrates the value of the proposed model as an analytical tool for diagnosing ethnic spatial relations. The findings offer important insights and spatial guidance for formulating context-sensitive strategies for sustainable governance, cultural heritage preservation, and ethnic integration. Full article

Background and Objectives: Clinical ethical judgments are often elicited through scenario-based (vignette-based) dilemmas that guide interpretation, reasoning, and moral judgment. Despite its importance, little is known about how healthcare professionals and students respond to such scenario-based dilemmas in Eastern European settings. This study explored differences in ethical decision-making between senior medical/dental students and practicing clinicians in Romania, focusing on how scenarios-based dilemmas influence conditional versus categorical responses. Materials and Methods: A cross-sectional survey was conducted with 244 participants (51 senior students; 193 practitioners). Respondents completed a validated 35-item questionnaire presenting hypothetical ethical scenarios across seven domains: informed consent, confidentiality, medical errors, public health duties, end-of-life decisions, professional boundaries, and crisis ethics. Each scenario used a Yes/No/It depends response structure. Group comparisons were analyzed using chi-square and non-parametric tests (α = 0.05). Results: Scenario-based dilemmas elicited frequent conditional reasoning, with “It depends” emerging as the most common response (47.8%). Strong consensus appeared in rejecting concealment of harmful errors and in treating unvaccinated families, reflecting robust professional norms. Divergences arose in areas where scenario-based dilemmas emphasized system-level duties: students more often supported annual influenza vaccination (52.9% vs. 32.6%, p = 0.028) and organ purchase authorization (76.47% vs. 62. 18%, p = 0.043), while practitioners more frequently endorsed higher insurance contributions for unhealthy lifestyles (48.7% vs. 23.5%, p = 0.003). Conclusions: Scenario-based dilemmas strongly shape moral decision-making in healthcare. While students tended toward principle-driven transparency, practitioners showed pragmatic orientations linked to experience and system stewardship. To promote high-quality clinical work and align decision-making with best practice and health policy, our findings support institutional protocols for transparent error disclosure, continuing professional development in ethical communication, the possible adoption of annual influenza vaccination policies for healthcare personnel as policy options rather than categorical imperatives, and structured triage frameworks during crisis situations. These proposals highlight how scenario-based ethics training can strengthen both individual reasoning and systemic resilience. Full article

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R² values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63–98.28% for silt, 0.26–36.16% for clay, and 0.64–96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. Full article