Search Results (118)

Against the backdrop of Shenzhen’s high-density urban environment, the multifunctional design of water purification plants offers dual benefits: providing residents with urban green spaces while simultaneously mitigating NIMBY sentiments due to their inherent characteristics. Unlike traditional urban development, Shenzhen’s water purification plants integrate into residents’ daily lives. Therefore, optimizing the built environment and road network structure to enhance residents’ perceptions of proximity benefits while reducing NIMBY (Not In My Backyard effect) sentiments holds significant implications for the city’s sustainable development. To address this question, this study adopted the following three-step mixed-methods approach: (1) It examined the relationships among residents’ YIMBY (Neighboring Benefits Effect) and NIMBY perceptions, perceptions of park spaces atop water purification plants, and perceptions of accessibility through questionnaire surveys and structural equation modeling (SEM), establishing a scoring framework for comprehensive YIMBY and NIMBY perceptions. (2) Random forest models and Shapley Additive Explanations (SHAP) analysis revealed nonlinear relationships between the built environment and composite YIMBY and NIMBY perceptions. (3) Spatial syntax analysis categorized the upgraded road network around the water purification plant into grid-type, radial-type, and fragmented-type structures. Scatter plot fitting methods uncovered relationships between these road network types and resident perceptions. Finally, negative perceptions were mitigated by optimizing path enclosure and reducing visual obstructions around the water purification plant. Enhancing neighborhood benefits—through improved path safety and comfort, increased green spaces and resting areas, optimized path networks, and diversified travel options—optimized the built environment. This approach proposes design strategies to minimize NIMBY perceptions and maximize YIMBY perceptions. Full article

Residual inflammatory risk after acute coronary syndromes (ACSs) remains a critical contributor to atherosclerosis progression and plaque destabilization. Inflammatory biomarkers such as interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) may provide additional insights into coronary lesion complexity and vulnerability. The main aim of the study was to evaluate the association of interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) with coronary disease extent; functional significance of non-culprit lesions, assessed by fractional flow reserve (FFR); and plaque vulnerability, assessed by optical coherence tomography (OCT) in patients with acute coronary syndrome (ACS). This prospective study enrolled 93 ACS patients undergoing invasive coronary assessment for an ACS. Inflammatory biomarkers were measured at admission and 6 months post-event. Patients were stratified post hoc into tertiles by biomarker distribution. SYNTAX score, FFR, and OCT-defined thin-cap fibroatheroma (TCFA) were used to characterize lesion burden and morphology. Multivariate logistic regression was performed adjusting for conventional cardiovascular risk factors and ACS type. Higher tertiles of IL-1ra, resistin, and CRP were significantly associated with increased SYNTAX score (p < 0.05), FFR < 0.80 (68% in the highest tertile), and presence of TCFA (62% vs. 20%, p < 0.01). All biomarkers correlated with coronary disease severity. In multivariate logistic models, IL-1ra (OR 1.23 per 100 pg/mL, p = 0.03), resistin (OR 2.35 per 1 ng/mL, p = 0.001), and CRP (OR 1.11 per 0.001 ng/mL, p = 0.006) independently predicted high-risk coronary profiles. IL-1ra, resistin, and CRP are independently associated with lesion complexity, functional significance, and vulnerability in ACS. Inflammatory biomarker profiling may provide complementary anatomical and physiological assessment in future ACS risk stratification strategies. Full article

Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research. Full article

The globalization of hardware design flows has increased the risk of Hardware Trojan (HT) insertion during the design phase. Graph-based learning methods have shown promise for HT detection at the Register Transfer Level (RTL). However, most existing approaches rely on representing RTL designs through a single graph structure. This single-view modeling paradigm inherently constrains the model’s ability to perceive complex behavioral patterns, consequently limiting detection performance. To address these limitations, we propose GNN-MFF, an innovative multi-view feature fusion model based on Graph Neural Networks (GNNs). Our approach centers on joint multi-view modeling of RTL designs to achieve a more comprehensive representation. Specifically, we construct complementary graph-structural views: the Abstract Syntax Tree (AST) capturing structure information, and the Data Flow Graph (DFG) modeling logical dependency relationships. For each graph structure, customized GNN architectures are designed to effectively extract its features. Furthermore, we develop a feature fusion framework that leverages a multi-head attention mechanism to deeply explore and integrate heterogeneous features from distinct views, thereby enhancing the model’s capacity to structurally perceive anomalous logic patterns. Evaluated on an extended Trust-Hub-based HT benchmark dataset, our model achieves an average F1-score of 97.08% in automated detection of unseen HTs, surpassing current state-of-the-art methods. Full article

Background and Objectives: Coronary atherosclerosis remains a leading cause of global morbidity and mortality. Chronic systemic inflammation has emerged as a key factor in atherosclerosis development. Tooth loss—often the final consequence of periodontitis—has been proposed as a potential clinical marker of systemic inflammation and cardiovascular risk. Objective: This narrative review synthesizes the available literature on the relationship between tooth loss and coronary artery disease (CAD) severity, exploring biological mechanisms, key epidemiological findings, and clinical implications. Materials and Methods: We reviewed observational studies, meta-analyses, and clinical reports assessing whether tooth loss is predictive of CAD severity and adverse outcomes. Results: A consistent association is reported between tooth loss and increased coronary involvement. Proposed mechanisms include periodontal inflammation, dysbiosis, systemic inflammatory responses, and translocation of oral bacteria. However, confounders such as smoking, diabetes, and socioeconomic status complicate causality. Conclusions: Tooth loss may serve as a simple, non-invasive clinical indicator of systemic inflammation and CAD severity. Incorporating oral health evaluation into cardiovascular risk assessment could enhance early detection and prevention strategies. Further longitudinal and interventional studies are required to establish causality and inform clinical guidelines. Full article

Background and Objectives: Factor VIII (FVIII) and the von Willebrand factor (vWF) are key components of hemostatic balance. Disruption of the vWF-ADAMTS13 axis, characterized by elevated vWF and reduced ADAMTS13 activity has been implicated in thrombotic disorders, including COVID-19-asscoiated coagulopathy, where this imbalance correlates with disease severity and mortality. This study evaluated the relationship between plasma FVIII and vWF levels and the severity of coronary artery disease (CAD), as assessed by the SYNTAX score. Methods: We enrolled 82 patients with chronic coronary syndrome (CCS) and a positive treadmill test who underwent elective coronary angiography. Based on the SYNTAX score, patients were divided into three groups: Group I (≤22), Group II (23–32), and Group III (≥33). Results: FVIII levels varied significantly (Group I: 2.25 ± 0.75; Group III: 2.97 ± 0.95; p = 0.007), with an OR of 3.632 (95% CI: 1.116–11.826; p = 0.03). vWF levels differed significantly across SYNTAX groups (Group I: 1.16 ± 0.59; Group II: 1.52 ± 0.62; Group III: 1.49 ± 0.80; p = 0.040). vWF > 1.75 was more frequent in Groups II and III, with an odds ratio (OR) of 4.909 (95% CI: 1.429–16.864; p = 0.01) for Group III vs. Group I. Fibrinogen and C-reactive protein (CRP) were elevated in patients with SYNTAX scores ≥33. In multinomial logistic regression analysis, FVIII emerged as the sole independent predictor of CAD complexity (p = 0.004), while the vWF showed significance in pairwise comparison (Group II vs. Group I; OR = 3.433, p = 0.049). Conclusions: This study demonstrated significant differences in hemostatic and inflammatory biomarkers across SYNTAX score categories reflecting CAD severity in CCS patients. FVIII emerged as an independent predictor of CAD complexity, while the vWF demonstrated significant associations in specific subgroup comparisons. The observed vWF-ADAMTS13 axis dysregulation supports the rationale for investigating vWF-targeted therapeutics, including agents such as caplacizumab, in cardiovascular disease management. These findings require validation in larger studies. Full article

To address the limitation of existing static defect detection methods in capturing code semantics and structural relationships—which leads to incomplete feature representation—we propose a multimodal feature fusion approach for source code defect detection. First, semantic features are extracted from code character sequences while structural features are derived from Abstract Syntax Trees (ASTs). Second, a structural attention mechanism dynamically models interdependencies between these two modalities and fuses them into comprehensive representation vectors. Finally, defect detection is performed based on the integrated representations. Experimental results on the Sard dataset demonstrate: Compared to baseline methods using single representations (semantic or structural), our approach improves F1-score by 1.96% to 11.76%. Against other feature fusion methods, it achieves 1.36% to 1.66% higher F1-score. The method demonstrates good stability when dealing with imbalanced defect category data. By effectively fusing multimodal code information, this approach significantly enhances the accuracy and adaptability of code defect detection in open-source environments. Full article

This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. Full article

Text2Cypher is a text-to-text task that converts natural language questions into Cypher queries. Recent research by Neo4j on Text2Cypher demonstrates that fine-tuning a baseline language model (a pretrained and instruction-tuned generative model) using a comprehensive Text2Cypher dataset can effectively enhance query generation performance. However, the improvement is still insufficient for effectively learning the syntax and semantics of complex natural texts, particularly when applied to unseen Cypher schema structures across diverse domains during training. To address this challenge, we propose a novel refinement training method based on baseline language models, employing reinforcement learning with Group Relative Policy Optimization (GRPO). This method leverages extracted semantic information, such as key-value properties and triple relationships from input texts during the training process. Experimental results of the proposed refinement training method applied to a small-scale baseline language model (SLM) like Qwen2.5-3B-Instruct demonstrate that it achieves competitive execution accuracy scores on unseen schemas across various domains. Furthermore, the proposed method significantly outperforms most baseline LMs with larger parameter sizes in terms of Google-BLEU and execution accuracy scores over Neo4j’s comprehensive Text2Cypher dataset, with the exception of colossal LLMs such as GPT4o, GPT4o-mini, and Gemini. Full article

Chronic coronary syndrome (CCS) and atrial fibrillation (AF) are prevalent cardiovascular conditions that share numerous risk factors and pathophysiological mechanisms. While clinical scores commonly used in AF—such as CHA₂DS₂VA (which includes congestive heart failure, hypertension, age ≥ 75, diabetes, stroke/TIA, vascular disease, and age 65–74), HAS-BLED (which incorporates hypertension, abnormal renal/liver function, stroke, bleeding history, labile INR, elderly age, and drug/alcohol use), and C₂HEST (incorporating coronary artery disease, COPD, hypertension, elderly age ≥ 75, systolic heart failure, and thyroid disease)—are traditionally applied to rhythm or bleeding risk prediction, their value in estimating the angiographic severity of coronary artery disease (CAD) remains underexplored. We conducted a prospective, single-center study including 131 patients with suspected stable CAD referred for coronary angiography, stratified according to coronary angiographic findings into two groups: significant coronary stenosis (S-CCS) and non-significant coronary stenosis (N-CCS). At admission, AF-related scores (CHA₂DS₂, CHA₂DS₂VA, CHA₂DS₂VA-HSF, CHA₂DS₂VA-RAF, CHA₂DS₂VA-LAF, HAS-BLED, C₂HEST, and HATCH) were calculated. CAD severity was subsequently assessed using the SYNTAX and Gensini scores. Statistical comparisons and Pearson correlation analyses were performed to evaluate the association between clinical risk scores and angiographic findings. Patients in the S-CCS group had significantly higher scores in CHA₂DS₂VA (4.09 ± 1.656 vs. 3.20 ± 1.338, p = 0.002), HAS-BLED (1.98 ± 0.760 vs. 1.36 ± 0.835, p < 0.001), CHA₂DS₂VA-HSF (6.00 ± 1.854 vs. 5.26 ± 1.712, p = 0.021), and C₂HEST (3.49 ± 1.501 vs. 2.55 ± 1.279, p < 0.001). Multivariate logistic regression identified HAS-BLED and C₂HEST as independent predictors of significant coronary lesions. A threshold value of HAS-BLED ≥ 1.5 and C₂HEST ≥ 3.5 demonstrated moderate discriminative ability (AUC = 0.694 and 0.682, respectively), with acceptable sensitivity and specificity. These scores also demonstrated moderate to strong correlations with both Gensini and SYNTAX scores. AF-related clinical scores, especially HAS-BLED and C₂HEST, may serve as practical and accessible tools for early CAD risk stratification in patients with suspected CCS. Their application in clinical practice may serve as supplementary triage tools to help prioritize patients for further diagnostic evaluation, but they are not intended to replace standard imaging or testing. Full article

Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or “code clones”, complicate the program structure and increase maintenance costs. Code clones are categorized into four types: Type-1, Type-2, Type-3, and Type-4. This study aims to address the adverse effects of code clones by introducing LeONet, a hybrid Deep Learning approach that enhances the detection of code clones in software systems. The hybrid approach, LeONet, combines LeNet-5 with Oreo’s Siamese architecture. We extracted clone method pairs from the BigCloneBench Java repository. Feature extraction was performed using Abstract Syntax Trees, which are scalable and accurately represent the syntactic structure of the source code. The performance of LeONet was compared against other classifiers including ANN, LeNet-5, Oreo’s Siamese, LightGBM, XGBoost, and Decision Tree. LeONet demonstrated superior performance among the classifiers tested, achieving the highest F1 score of 98.12%. It also compared favorably against state-of-the-art approaches, indicating its effectiveness in code clone detection. The results validate the effectiveness of LeONet in detecting code clones, outperforming existing classifiers and competing closely with advanced methods. This study underscores the potential of hybrid deep learning models and feature extraction techniques in improving the accuracy of code clone detection, providing a promising direction for future research in this area. Full article

Background/Objectives: Randomized studies of patients with unprotected left main coronary artery (ULMCA) disease involve highly selected populations. Therefore, we sought to investigate the 60-month event-free survival of consecutive patients undergoing ULMCA percutaneous coronary intervention (PCI) and determine the best risk score system and independent predictors of event-free survival. Methods: All patients who underwent ULMCA PCI at our center between 1 January 2007 and 31 December 2014 were included. The primary endpoint was the time to cardiac death, target lesion myocardial infarction, or target lesion revascularization (whichever came first) with a follow-up of 60 months. Results: A total of 513 patients (mean age 68 ± 12 years, 64% male, 157 elective, 356 acute) underwent ULMCA PCI. The 60-month incidence of events was 16.8% and 38.0% in elective and acute patients, respectively. There were significantly more events in the acute group during the first 6.5 months. Of the risk scores, the ACEF (AUC = 0.786) and SYNTAX II (AUC = 0.716) scores had the best predictive power in elective and acute patients, respectively. The SYNTAX score proved to be the least predictive in both groups (AUC = 0.638 and 0.614 in the elective and acute groups, respectively). Left ventricular function (hazard ratio (HR) for +10% 0.53 [95% CI, 0.38–0.75] and 0.81 [95% CI, 0.71–0.92] in elective and acute patients, respectively) and, in acute patients, access site (femoral vs. radial HR 1.76 [95% CI, 1.11–2.80]), hyperlipidemia (HR 0.58 [95% CI, 0.39–0.86]), and renal function (HR for +10 mL/min/1.73 m² higher GFR: 0.87 [95% CI, 0.78–0.97]) were independent predictors of event-free survival. Conclusions: Acute ULMCA PCI patients have worse prognosis than elective patients, having more events during the first 6.5 months. Besides anatomical complexity, clinical and procedural parameters determine the prognosis. Full article

Background: Patients with ST-elevation myocardial infarction (STEMI) and multivessel coronary artery disease (MVD) who undergo complete revascularization (CR) have a more favorable prognosis than those who receive incomplete revascularization (IR), as evidenced by recent randomized controlled trials. Despite the absence of a survival benefit associated with CR in these trials, positive outcomes were ascribed to combined endpoints, such as repeat revascularization, myocardial infarction, or ischemia-driven rehospitalization. In light of the significant burden that rehospitalization from STEMI imposes on healthcare systems, we examined the long-term effects of CR on ischemia-driven rehospitalization and cardiovascular (CV) mortality in STEMI patients with MVD. Methods: In our retrospective study, we included patients with STEMI and MVD who underwent successful primary percutaneous coronary intervention (PCI) at the University Medical Centre Ljubljana between 1 January 2009, and 11 April 2011. The combined endpoint was ischemia-driven rehospitalization and CV mortality, with a minimum follow-up period of six years. Results: We included 235 participants who underwent CR (N = 70) or IR (N = 165) at index hospitalization, with a median follow-up time of 7 years (interquartile range 6.0–8.2). The primary endpoint was significantly higher in the IR group than in the CR group (47.3% vs. 32.9%, log-rank p = 0.025), driven by CV mortality (23.6% vs. 12.9%, log-rank p = 0.047), as there was no difference in ischemia-driven rehospitalization rate (log-rank p = 0.206). Ischemia-driven rehospitalization did not influence CV mortality in the CR group (p = 0.49), while it significantly impacted CV mortality in the IR group (p = 0.03). After adjusting for confounders, there were no differences in CV mortality between CR and IR groups (p = 0.622). Predictors of the combined endpoint included age (p = 0.014), diabetes (p = 0.006), chronic kidney disease (CKD) (p = 0.001), cardiogenic shock at presentation (p = 0.003), chronic total occlusion (CTO) (p = 0.046), and ischemia-driven rehospitalization (p = 0.0001). Significant risk factors for the combined endpoint were cardiogenic shock at presentation (p < 0.001), stage 4 kidney failure (p = 0.001), age over 70 years (p = 0.004), female gender (p = 0.008), and residual SYNTAX I score > 5.5 (p = 0.017). Conclusions: Patients with STEMI and MVD who underwent CR had a lower combined endpoint of ischemia-driven rehospitalizations and CV mortality than IR patients, but after adjustments for confounders, the true determinants of the combined endpoint and risk factors for the combined endpoint were independent of the revascularization method. Full article

Background: Coronary artery disease (CAD) is a leading global cause of mortality. The role of calcium (Ca), a key metabolic and structural element, in atherosclerosis and inflammation remains unclear. Ca influences immune cell function and is a component of atherosclerotic plaques. Hair analysis reflects long-term mineral exposure and may serve as a non-invasive biomarker. Objectives: This pilot study aimed to investigate the association between hair Ca levels and acute coronary syndrome (ACS), and to evaluate correlations with the Systemic Inflammatory Index (SII), Systemic Inflammatory Response Index (SIRI), and selected CAD risk factors. Methods: Ca levels were measured in hair samples from patients undergoing coronary angiography for suspected myocardial infarction. Associations with ACS diagnosis, Syntax score, SII, SIRI, and CVD risk factors were analyzed. Results: Serum calcium levels were not significantly associated with the presence of acute coronary syndrome (ACS) (p = 0.392) or with its clinical subtypes, including ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina (UA) (p = 0.225). Diagnosis of ACS was linked to higher SII (p = 0.028) but not SIRI (p = 0.779). Ca levels correlated negatively with Syntax score (R = −0.19, p = 0.035) and SII (R = −0.22, p = 0.021) and positively with HDL-C (R = 0.18, p = 0.046). Conclusions: Hair calcium content may reflect subclinical inflammation and CAD severity. Although no direct link to ACS was observed, the associations with SII, HDL-C, and Syntax score suggest a potential diagnostic role which should be further explored in larger, well-controlled studies. Full article

Background: There is insufficient data in the literature on the relationship between carotid intima–media thickness (cIMT) measured in non-ST segment elevation myocardial infarction (NSTEMI) and cardiovascular (CV) mortality. Therefore, we aimed to determine the effect of cIMT value on long-term mortality in patients with NSTEMI. Methods: This retrospective cohort study included 279 patients with NSTEMI. In addition to clinical, demographic, laboratory, and angiographic investigations, cIMT, femoral IMT (fIMT), and aortic IMT (aIMT) were measured by B-mode ultrasonography. All patients received follow-up evaluation for CV mortality. The patients were grouped as with and without mortality. Results: Patients with NSTEMI received follow-up evaluations for 7.51 ± 0.85 years and 77 (27.6%) patients had mortality. Age, creatinine, blood urea nitrogen, cIMT, aIMT, fIMT, and SYNTAX score values were significantly higher in patients with mortality compared to patients without mortality. Hemoglobin, total cholesterol, LDL cholesterol, triglyceride levels, and left ventricular ejection fraction were significantly lower in patients with mortality compared to patients without mortality. In multivariate analysis, cIMT, age, and creatinine level were found to be independent predictors of mortality. Among these parameters, an increase in age (each year), carotid IMT (each 0.1 mm), and serum creatinine (each 0.1 mg/L) levels predicted an increase in mortality by 8%, 46.5%, and 12.6%, respectively. In ROC analysis, age, cIMT, and creatinine level were found to determine the development of mortality due to NSTEMI with acceptable sensitivity and specificity when an age of 65 years, 0.80 mm, and 0.90 mg/L were taken as cut-off values, respectively. Discussion: In patients with NSTEMI, cIMT measurement is independently associated with the development of long-term mortality. Full article