Search Results (214)

As oilfield development enters the mid-to-late stages, conventional water flooding techniques face increasing challenges such as high water cut and limited improvement in recovery efficiency. Gas flooding has gradually become a critical method for enhancing oil recovery (EOR). However, significant heterogeneity in pore structures within complex reservoirs severely affects flow capacity and development performance during gas flooding processes. To elucidate the microscale flow mechanisms influenced by heterogeneity, this study constructs a series of two-dimensional pore network models with varying degrees of heterogeneity based on an improved Quartet Structure Generation Set algorithm. Gas-oil two-phase flow simulations were conducted using the multiphase flow module of COMSOL Multiphysics® 6.2. By adjusting the bimodal pore size ratio and pore distribution parameters, the heterogeneity level of the reservoir was systematically controlled, and relative permeability curves were extracted to inform macro-scale development strategy design. Simulation results indicate that (1) strong heterogeneity reduces the stability of the displacement front, leading to pronounced gas channeling; (2) in strongly heterogeneous pore structures, residual oil saturation significantly increases, with small pore regions forming residual oil-enriched zones that are difficult to mobilize; (3) relative permeability curves vary markedly under different heterogeneity conditions—oil-phase permeability declines rapidly during displacement, while gas-phase permeability rises sharply at high gas saturation levels. This study systematically investigates, for the first time, the microscale impact of pore structure heterogeneity on gas flooding behavior and applies pore-scale simulation outcomes to optimize macro-scale development strategies. The findings offer theoretical support and a technical pathway for gas injection design in complex heterogeneous reservoirs. While two-dimensional pore-network models enable controlled mechanistic and sensitivity analyses of heterogeneity, they do not fully capture three-dimensional connectivity and tortuosity. Accordingly, our results are positioned as mechanistic priors that are calibrated to field data during upscaling. Full article

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

The calculation of energy consumption in building plans is usually carried out after design completion, resulting in high time costs and hindering their application in the early design stage. This study focused on the heating and cooling demands of nearly zero energy residential buildings in Jinan and developed an envelope optimization model for the design stage. Firstly, field research on residential buildings in Jinan was conducted, and the shape coefficient based on research data was determined. Subsequently, ten design parameters were selected, and a prediction function was established through multiple linear regression. Finally, the mechanisms between the parameters and energy consumption were revealed, and the reliability of the model was verified. Results showed that the most energy-efficient shape coefficient is an 18-story rectangular building with a length of 52.6 m, a width of 15.1 m, and a floor-to-floor height of 3 m. The goodness of fit of the prediction function is 0.994. The adjusted R² and RMSE of the neural network model in interpretable analysis are 0.933 and 0.089, respectively. The window-to-wall ratio significantly impacts energy consumption. This study addresses the lack of energy optimization by establishing a process that first determines energy-efficient parameter combinations and then refines the architectural scheme, and provides software to assist architects in design during schematic phases. Full article

Background: As the United States population becomes increasingly diverse, the representation of minorities in health professions is critical to addressing health disparities. Few investigations have been conducted among students enrolled in the first semester of the nursing program, a vulnerable and adjustment period for most nursing majors. Thus, this study examined the association between reading comprehension and science aptitude on student retention and standardized test scores. Method: A cross-sectional repeated measures study was conducted to investigate the outcomes from a compendium of programmatic interventions implemented among n = 80 nursing students enrolled in the first semester of a pre-licensure baccalaureate nursing program in one Hispanic-serving institution. These interventions included the Weaver™ reading online program, case studies, NCLEX-type practice tests, test-taking skills, and peer-mentoring. Data collection was conducted in Spring 2024. Multivariate statistical analysis was used to determine predictors associated with student retention and standardized test scores. An independent t-test was used to examine any significant difference in the reading comprehension level among the cohort’s participants. A qualitative investigation using thematic analysis was conducted to understand students’ experiences with the programmatic interventions. Results: Students’ baseline reding comprehension level was significantly associated with failure in the first semester of the nursing program (β = −0.815; SE = 0.349; Wald = 5.444; p < 0.05). End-of-term reading comprehension level was significantly associated with end-of-course HESI score in the Foundations in Nursing course (β = 26.768; SE = 10.049; Beta = 0.445; p < 0.05) while science GPA was significantly associated with end-of-course HESI score for Health Assessment (β = 3.022; SE = 1.315; Beta = 0.434; p < 0.05. Cohort retention was 75%. The independent t-test result indicated a significant difference in reading level was found between those who dropped out from the cohort (M = 4.23, SE = 0.173 and those who did not (M = 5.15, SE = 0.188), t (68) = −3.037, p < 0.01. A reading level of grade 10 and above was associated with student progression to the next semester (M = 10.16, SE = 0.375, t (70) = −0.560, p < 0.05. Although the participants found the reading comprehension modules tedious, test-taking strategies, applying the nursing process in case studies, and the expertise of a nurse educator, who understood the learning needs of first-semester students, were perceived as critical to academic success. Conclusions: Reading comprehension and science aptitude are essential to students’ early success in the nursing program. Addressing gaps in reading comprehension and science aptitude before admission to a nursing program would increase chances of success in the early stages of a nursing major. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Background: Communication difficulty restricts education, healthcare, and social participation, yet population-level data for Saudi Arabia have been scarce. This study analysed the 2017 Saudi National Disability Survey to estimate prevalence, describe severity and demographic patterns, and identify factors linked to these difficulties. Objectives: We aimed to estimate national and regional prevalence, assess severity, and gender differences, and identify socio-demographic and disability-related correlates. Methods: A cross-sectional, two-stage stratified cluster sample of 33,575 households (weighted N = 20,408,362 citizens) provided self-reported data on communication difficulty and socio-demographics. Weighted frequencies described prevalence and multivariable logistic regression identified independent correlates. Results: Among all Saudi citizens, 7.1% reported at least one functional difficulty, and of this group 15.7%—equivalent to 1.1% of the total population (n = 226,510)—had a communication difficulty; within that communication difficulty stratum, (n = 185,508) (0.9% of all citizens) experienced it alongside additional impairments, whereas (n = 41,002) (0.2% of all citizens) reported communication difficulty in isolation. The communication difficulties exhibit significant regional variation, ranging from 0.45% in Najran to 1.55% in Aseer. Most cases were classified as being associated with some difficulty (72%); females were over-represented in the extreme category despite a modest male excess overall (adjusted odds ratio [AOR] = 1.09). Higher education, married status, and bilateral first-cousin marriage (AOR = 1.22) were associated with greater risk. Chronic disease (44%) and perinatal causes (13%) predominated, and 84% of cases co-occurred with at least one other disability. Independent predictors included a long duration (AOR = 4.18), disease or delivery-related cause, and consanguinity. Conclusions: Findings highlight geographically clustered need, genetic risk factors, and substantial multimorbidity, indicating the importance of region-specific screening, premarital counselling, and integrated rehabilitation within chronic disease services. Full article

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development. Full article

This study examines the effectiveness of the strategic session format in teaching sustainable development within a university ecology course, with a particular focus on fostering student engagement. A pedagogical experiment was conducted with first-year undergraduate students, who were divided into four stakeholder groups—Ecologists, Developers, Residents, and Authorities—to work on the following question: “What should a sustainable city of the future be like?” Team roles were assigned based on a diagnostic survey assessing individual collaboration styles. The online session was structured in two stages, combining small-group discussions and plenary meetings, and was moderated by third-year students. The collaboration was supported by digital tools, including online boards and structured templates. Data collection involved student surveys, discussion transcripts, and moderator observations. The results indicate that students preferred the interactive strategic session format over conventional instruction methods. Participants demonstrated high levels of engagement, an ability to analyze complex sustainability issues, and a willingness to reconcile differing stakeholder perspectives. The findings also revealed areas for improvement, which informed further adjustments to the format. This paper offers a documented example of using the strategic session as an educational tool for sustainable development, aligning with active learning principles. It highlights the format’s potential for interdisciplinary learning and its adaptability through accessible digital platforms. Full article

Information retrieval serves as a critical methodology for accurately and efficiently obtaining the required information from massive amounts of data. In this paper, we propose an information retrieval framework (SE-MSLC) that utilizes information theory to improve the retrieval effectiveness of inverted index retrieval, thus achieving higher-quality retrieval results in intelligent vertical domain search engines. First, we propose a semantic entropy-driven keyword importance analysis method (SE-KIA) in the query understanding module. This method combines search query logs, the corpus of the search engine, and the theory of semantic entropy, enabling the search engine to dynamically adjust the weights of query keywords, thereby improving its ability to recognize user intent. Then, we propose a hybrid recall strategy that combines a multi-stage strategy and a logical combination strategy (HRS-MSLC) in the recall module. It separately recalls the keywords obtained from the multi-granularity word segmentation of the query in the form of multi-queue recall and simultaneously considers the “AND” and “OR” logical relationships between the keywords. By systematically managing retrieval uncertainty and giving priority to the keywords with high information content, it achieves the best balance between the quantity of the retrieval results and the relevance of the retrieval results to the query. Finally, we experimentally evaluate our methods using the Hit Rate@K and case analysis. Our results demonstrate that the proposed method improves the Hit Rate@1 by 7.3% and the Hit Rate@3 by 6.6% while effectively solving the bad cases in our vertical domain search engine. Full article

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed network adopts a “coarse detection–fine estimation” phased framework; the first stage employs a lightweight detection module, which integrates a dynamic hybrid backbone (DHB) and dynamic switching mechanism to efficiently filter background noise and generate coarse localization boxes of marker regions. Specifically, the DHB dynamically switches between convolutional and Transformer branches to handle features of varying complexity (using depthwise separable convolutions from MobileNetV3 for low-level geometric features and lightweight Transformer blocks for high-level semantic features). The second stage constructs a Transformer-based homography estimation module, which leverages multi-head self-attention to capture long-range dependencies between marker keypoints and the scene context. By integrating enhanced multi-scale feature interaction and position encoding (combining the absolute position and marker geometric priors), this module achieves the end-to-end learning of precise homography matrices between markers and hoisting equipment from the coarse localization boxes. To address data scarcity in construction scenes, a multi-dimensional data augmentation strategy is developed, including random homography transformation (simulating viewpoint changes), photometric augmentation (adjusting brightness, saturation, and contrast), and background blending with bounding box extraction. Experiments on a real bridge-erecting machine dataset demonstrate that the network achieves detection accuracy (mAP) of 97.8%, a homography estimation reprojection error of less than 1.2 mm, and a processing frame rate of 32 FPS. Compared with traditional single-stage CNN-based methods, it significantly improves the alignment precision and robustness in complex environments, offering reliable technical support for the precise control of automated hoisting in bridge-erecting machines. Full article

This study systematically analyzes the microstructure and radial variation of Cercis glabra wood, revealing its adaptive strategies for arid environments. The results show that the wood consists of thick-walled fibers (63%) and vessels (17.7%), with a semi-ring-porous structure and 48.4% average cell wall percentage. Fiber proportion peaks early (4 years), ensuring mechanical support, while vessel adjustment occurs later (19 years), balancing water transport. Rays decline sharply in the first 9 years, stabilizing thereafter, reflecting a shift from growth to structural stability. The high fiber proportion and occasional tyloses enhance durability, making it suitable for high-quality pulp, furniture, and humid environments such as shipbuilding. A rotation period ≥ 20 years ensures stable properties. Genetic breeding could shorten the juvenile stage and optimize vessel distribution. Future research should integrate multi-omics and environmental data to deepen our understanding of its adaptation mechanisms. This study provides a basis for the utilization of C. glabra resources. Full article

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article