Search Results (617)

Catheter ablation (CA) has become a validated technique for treating patients with symptomatic or paroxysmal atrial fibrillation (AF), as recommended by the latest 2024 European society of cardiology (ESC) guidelines, class II level A. The procedure is also recommended for patients with persistent AF without major risk factors for AF recurrence, as an alternative to antiarrhythmic medications class I or III. However, CA carries the risk of AF recurrence in 30–35% of patients, sometimes after the procedure. Multiple factors impact the onset, maintenance, and recurrence of AF after CA, including clinical, biohumoral, echocardiographic, genetic, and lifestyle factors. Beyond traditional predictors, emerging factors such as obstructive sleep apnea syndrome, chronic renal failure, chronic lung disease, physical activity patterns, gut microbiota composition, and epicardial fat thickness significantly influence outcomes. Therefore, optimizing patient’s selection for CA is an important strategy to minimize the risk of AF recurrence. Many echocardiographic parameters emerged as predictors of AF recurrence post-CA, but none stood out as a potential single factor. These factors include traditional markers such as left atrial size by 2D echocardiography, LV ejection fraction, LV diastolic function parameters as well as myocardial deformation addressed by the recently developed speckle tracking analysis. Additionally, the duration and type of AF represent fundamental risk factors, with longstanding persistent AF showing significantly higher recurrence rates compared to paroxysmal forms. Novel biomarkers including MR-proANP, caspase-8, hsa-miR-206, and neurotrophin-3 show promise in enhancing risk prediction capabilities. The aim of this review is to explore the most relevant echocardiographic parameters, including myocardial deformation, that could accurately predict recurrence of AF after CA, while also examining the role of emerging clinical and biochemical predictors in comprehensive patient selection strategies. Full article

Industrial heritage is increasingly becoming an important resource for sustainable urban renewal. With the acceleration of deindustrialization and urban transformation, Adaptive Reuse (AR) is regarded as the core path connecting heritage protection and functional renewal. Balancing the diverse value dimensions of AR has also become a key research focus. However, existing research mostly focuses on financial returns and investment efficiency, ignoring the long-term impact of community space and cultural dimensions on economic feasibility; at the same time, culture is often simplified into a tool for asset appreciation and urban branding, lacking a systematic model that reveals the structural role of culture in economic feasibility. Therefore, this study constructs a multi-attribute decision-making framework that integrates economic performance, community space, and cultural value. Using Guangzhou Guanggang New City as a representative case, the Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), and Grey Relational Analysis (GRA) were employed to screen and rank the highest-priority reuse schemes. The results show that the economic dimension holds the highest overall weight, followed by the community and cultural dimensions. This suggests that economic feasibility remains a key prerequisite for industrial heritage renewal, while cultural and community factors play an important supporting role in achieving long-term sustainability. This study provides a quantifiable assessment path for the adaptive reuse of industrial heritage and offers a basis for decision making in other cities seeking a balance between economic rationality and cultural sustainability. Full article

When a highway overlies a goaf, the cracking and subsidence of the highway subgrade seriously threaten the safe operation and maintenance of highways, including passenger safety. In this study, subgrade subsidence in the operation period of the G0611 Zhangye–Wenchuan Expressway from Biandukou to Menyuan was analyzed. First, the main factors influencing this kind of subsidence were analyzed using theoretical analysis, field investigation, and field detection. Then, an index system for these factors was constructed, composed of one target-layer, five criterion-layer, and seventeen indicator-layer indexes. The ANP and CRITIC methods were used to calculate the subjective and objective weights of each influencing factor index. The combined weights were obtained based on game theory, and the contribution degree of each index was determined. The primary and secondary relationships of the influencing factors of subgrade subsidence were inferred. The research results indicate that the foundation of the analyzed expressway section contains goaf areas, with poor filling performance, failure to fill in layers according to regulations, and poor drainage being the main reasons for subgrade subsidence. Based on the contribution degree of the indicator-layer influencing factors, high-energy-level dynamic compaction can be used to ram goafs so as to ensure the operational safety of the expressway. Full article

Background/Objectives: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) and chronic obstructive pulmonary disease (COPD) are linked to multi-organ vulnerability involving the lungs, heart, and kidneys. This study aimed to compare the annual changes in pulmonary, cardiac, and renal parameters in patients with SSc-ILD and COPD across three consecutive years, using both individual biomarkers and integrated composite profiles. Methods: This observational longitudinal study included repeated assessments in 2023, 2024, and 2025. Functional, laboratory, and imaging parameters were collected: 6-min walk test (6MWT), SpO₂ (pre-/post-exercise), spirometry/CT lung volumes, gas exchange (pO₂/pCO₂/lactate), echocardiography [left ventricular ejection fraction (LVEF), estimated systolic pulmonary artery pressure (sPAP)], cardiac biomarkers (NT-proBNP, MR-proANP, hsTnT), renal markers [eGFR, creatinine, albuminuria, albumin-to-creatinine ratio (ACR)], heart rate variability (HRV), and renal CT densitometry. All markers were standardized (z-scores, higher values = worse). Subprofiles were generated and aggregated into three integrated profiles (cardiac, renal, pulmonary). Within-group dynamics were analyzed using the Wilcoxon signed-rank test (year-to-year deltas), between-group comparisons with the Mann–Whitney U test, effect sizes via Cliff’s delta, and multiple testing correction with the Benjamini–Hochberg false discovery rate (FDR). Results: Exercise tolerance declined in both groups: by 2025, 6MWT distance decreased by −10 m in SSc-ILD (p = 0.006; q = 0.010) and −20 m in COPD (p = 0.002; q = 0.004); post-exercise SpO₂ fell in both cohorts (both p < 0.001; q < 0.001). MR-proANP remained consistently higher in SSc-ILD across all years (p ≤ 0.005; q ≤ 0.028). sPAP increased in both groups, reaching higher values in COPD by 2025 (p = 0.007; q = 0.033). NT-proBNP and hsTnT increased over time, while eGFR declined, and ACR rose in both cohorts (both p < 0.001; q < 0.001). HRV (HF/total power) decreased by 2025. Composite profiles showed: in 2023, the cardiac profile was worse in SSc-ILD (δ ≈ 0.27; p = 0.011; q = 0.048), but differences diminished by 2025; the renal profile was initially worse in SSc-ILD but later shifted unfavorably in COPD; the pulmonary profile showed no consistent between-group differences. Conclusions: Over three years, patients with SSc-ILD and COPD exhibited concordant deterioration in pulmonary, cardiac, and renal function. Distinct leading markers emerged: desaturation during exercise and neurohormonal activation (MR-proANP) in SSc-ILD, versus reduced 6MWT and higher sPAP in COPD. These findings support the need for integrated monitoring of the cardio–pulmo–renal continuum. Limitations include the observational design, multiple comparisons, and absence of advanced repeated-measures modeling. Full article

This paper proposes an enhanced hierarchical fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Analytic Network Process (ANP) for solving multi-criteria decision-making (MCDM) problems under uncertainty. Conventional fuzzy TOPSIS models often face significant challenges, such as restrictions to specific fuzzy number formats, difficulties in normalization when zero or very small values appear, and limited capacity to capture hierarchical interdependencies among criteria. To address these limitations, we develop a generalized fuzzy geometric mean approach for deriving weights from pairwise comparisons that can accommodate multiple fuzzy number types. Moreover, a novel normalization function is introduced, which ensures mathematically valid outcomes within the [0, 1] interval while avoiding division-by-zero and inconsistency issues. The proposed method is validated through both a numerical building selection problem and a practical supplier selection case study. Comparative analyses against established fuzzy MCDM models demonstrate the improved robustness, flexibility, and accuracy of the approach. Additionally, a sensitivity analysis confirms the stability of results with respect to variations in criteria weights, fuzzy number formats, and normalization techniques. These findings highlight the potential of the proposed fuzzy hierarchical TOPSIS-ANP framework as a reliable and practical decision support tool for complex real-world applications, including supply chain management and resource allocation under uncertainty. Full article

Banana is a globally important food crop. As a large herbaceous plant with a shallow root system, its yield is highly susceptible to drought stress. ANP family genes play crucial roles in plant drought resistance. However, the ANP gene family has not been systematically studied in bananas, and how Piriformospora indica (P. indica) induces its expression remains unclear. A comprehensive identification of the ANP family is thus a necessary foundation for functional studies. In this study, we systematically identified 13 ANP family members in banana for the first time through genome-wide analysis. Using bioinformatics, RT-qPCR and subcellular localization techniques, we characterized their structural features, phylogenetic relationships, and the expression patterns of MaNPK1 under drought stress and P. indica colonization. The results revealed that banana ANP family members are highly evolutionarily conserved. MaNPK1-1 was specifically induced and upregulated by P. indica under drought conditions and subcellular localization showed that it played a role in the nucleus. This research provides theoretical insights into the function of the banana ANP family and its regulatory role in the P. indica mediated drought stress response, offering potential applications for breeding stress resistant banana varieties. Full article

The lack of reliable methods for cropland and forest monitoring remains a challenge in the Betsiboka basin and Ankarafantsika National Park (ANP), Madagascar. A key novelty of our study is the comparative analysis of multiple high-resolution datasets for 2017 and 2021 and future projections under five Shared Socioeconomic Pathways (SSPs) from 2020 to 2100 using Google Earth Engine and Python. Results indicate that forest cover has remained below ~9% in the Betsiboka basin and above ~35% in ANP, while cropland stays under 7% in both areas. Inter-dataset agreement showed high overall accuracy (OA = 0.87–0.95), with stronger agreement in ANP (Kappa = 0.68–0.90). FROM-GLC10 and ESA performed best for cropland classification in Betsiboka, while Dynamic World and ESRI were most accurate for forest, particularly in ANP. Projections suggest that by 2100, forest area in Betsiboka may increase by +230% under SSP3 and +300% under SSP5, whereas ANP could see declines up to 39% under SSP1, −2.2% SSP5, and −1.4% SSP3. The predicted minor cropland increase across both regions suggests that forest expansion is unlikely to significantly constrain agricultural land, illustrating the potential for sustainable intensification and agroforestry to address food security challenges. Full article

Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network Process (DANP), an integrated DEMATEL-ANP multi-criteria approach to evaluate and prioritize key resilience-related spatial and planning factors in TOD areas. Rather than using GIS flood modeling, we emphasize empirical indicators derived from local data, including urban density, transit accessibility, historical typhoon flood impacts, infrastructure vulnerability, and demographic exposure. An extensive literature review covers TOD principles, urban resilience theory, and DANP methodology, with a particular emphasis on the Taiwanese context and case studies. Empirical results reveal that specific TOD characteristics indeed enhance typhoon resilience. High-density, mixed-use development around transit can reduce overall exposure to hazards by curbing sprawl into floodplains and enabling efficient evacuations. Using DANP, we find that infrastructure robustness and emergency planning capacity emerge as the most influential factors for resilience in Taipei’s TOD neighborhoods, followed by land use and management and transit accessibility. Weighted rankings of Taipei’s districts suggest that centrally located TOD-intensive districts score higher in resilience metrics, while peripheral districts with flood-prone areas tend to lag. The Discussion explores these findings, considering planning policies—noting that TOD can bolster resilience if coupled with adaptive infrastructure and inclusive planning—and compares them with examples like Singapore’s integrated land use and transit strategy, which dramatically reduced flood risk. The study concludes with policy implications for integrating TOD and climate resilience in urban planning, and contributions of the DANP approach for complex urban resilience evaluations. Full article

Vertical greening (VG) delivers measurable urban ecosystem benefits, yet maintenance is constrained by at-height safety risks, heterogeneous facade geometries, and low labor efficiency. Although unmanned aerial vehicles show promise, most studies optimize isolated modules rather than providing a user-oriented, system-level pathway. This paper proposes a closed-loop, multi-method framework integrating the Decision-Making Trial and Evaluation Laboratory-Analytic Network Process, the Functional Analysis System Technique, and the Theory of Inventive Problem Solving. DEMATEL-ANP models causal interdependencies among requirements and derives prioritized weights,; FAST decomposes functions and localizes conflicts, and TRIZ converts those conflicts into principle-guided structural concepts—establishing a traceable requirements → functions → conflicts → structure pipeline. We illustrate the approach at the prototype level with Rhino–KeyShot visualizations under near-facade constraints, showing how prioritized requirements propagate into candidate UAV architectures. The framework structures the identification and resolution of tightly coupled technical conflicts, supports adaptability in facade-proximal scenarios, and provides a transparent mapping from user needs to structure-level concepts. Claims are restricted to methodological feasibility; comprehensive quantitative field validation remains for future work. The framework offers a reproducible methodological reference for the systematic design and decision-making of intelligent UAV maintenance systems for VG. Full article

In recent years, agriculture has increasingly faced shocks related to climate change, pathogen outbreaks, and geopolitical instability, highlighting the need for sustainable regeneration strategies. This study develops an innovative Multi-Criteria Decision-Making (MCDM) framework that integrates the Delphi method, the Analytic Network Process (ANP), and the Aggregated Decision-Making (ADAM) method—the first application of this combination in the context of agricultural regeneration. The framework was applied to the Apulia region (Italy), heavily affected by the Xylella fastidiosa epidemic, and evaluated alternative crops across 30 economic, environmental, and socio-cultural sub-criteria. Results indicate that carob, walnut, and pistachio outperform other options by combining strong economic viability, climate resilience, and cultural compatibility. To mitigate the risks of monoculture, crop diversification strategies based on high-ranked alternatives are recommended. Sensitivity analysis confirmed the robustness of results, and the framework demonstrates high scalability, offering a transparent tool for policymakers in regions facing similar agricultural crises. Full article

The selection of an appropriate technological process is essential to achieve optimal results in manufacturing companies. This affects quality, efficiency and competitiveness. In the modern industry, multi-criteria decision-making (MCDM) methods are increasingly used to evaluate, optimize and solve various manufacturing challenges. In this review article, existing methodologies and patents related to optimization and decision making are investigated. The main characteristics and applications of the methods are outlined. The purpose of this article is to provide a systematic review and evaluation of the main MCDM methods used in industrial practice, including through an analysis of relevant methodologies and patents. The methodology involves a structured literature and patent review, focusing on applications of widely used MCDM techniques such as the AHP (analytic hierarchy process), ANP (analytic network process), FUCOM (full consistency method), TOPSIS (technique for order preference by similarity to ideal solution), and VIKOR (višekriterijumsko kompromisno rangiranje). The analysis outlines each method’s strengths, limitations and areas of applicability. Special attention is given to the potential of the FUCOM for process evaluation in manufacturing. The findings are intended to guide researchers and practitioners in selecting appropriate decision-making tools based on specific industrial contexts and objectives. In conclusion, from the comparative analysis made, the methodologies reveal their advantages and disadvantages as well as limitations that arise in their application. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

Background: Abnormal activation of Angiotensin II (Ang II) serves as a primary trigger for myocardial hypertrophy and cardiac injury. Isoquercitrin (IQ) and Quercetin (Que) possess anti-inflammatory and anti-apoptotic properties, but their protective effects against Ang II-induced cardiac injury remain unclear. This study aimed to investigate the mechanisms and therapeutic efficacy of IQ and Que in heart failure. Methods: Cytotoxic effects of IQ and Que on Ang II-induced H9c2 rat cardiomyocyte apoptosis models were assessed in vitro using the CCK-8 assay. Reactive Oxygen Species (ROS) generation and apoptotic fluorescence levels were measured. WB analysis examined protein expression in inflammatory and apoptotic pathways. In vivo heart failure model was established in mice, with cardioprotective effects of IQ and Que evaluated via echocardiography. Molecular docking was employed to analyze ligand–target interactions. Results: IQ outperformed Que in promoting cell viability and decreasing ROS. IQ exhibited a more potent inhibitory effect on apoptosis through regulating Bax, Caspase-3, CytoC, and Bcl-2 and demonstrated superior suppression of cardiac inflammation by inhibiting phosphorylation of ERK, JNK, and P38. Compared with Que, IQ more effectively attenuated Ang II-induced cardiac injury by ameliorating reductions in EF% and FS%, suppressing ST-segment elevation, and significantly reducing serum levels of CK-MB, LDH, ANP, BNP, and FFA in a heart failure model. Molecular docking verified stronger binding affinity of IQ for key targets. Conclusions: IQ demonstrates superior cardioprotection over Que by regulating MAPK signaling and mitochondrial apoptosis pathways, supporting its potential as a therapeutic candidate for heart failure. Full article

Historic buildings make up a significant proportion of the existing building stock. Most are characterised by poor technical condition and high energy demand. In Poland, many historic buildings are still in use today, but it is also common to find these buildings subjected to adaptive reuse. Adaptive reuse, often combined with modernisation, is problematic, especially in terms of finding a use that is optimal in the light of use-specific decision criteria. In previous studies, the authors used and developed the potential for the modelling and structural analysis of decision-making problems for the selection of new uses for historic buildings. In this paper, we present a test of this methodology on a Polish historic building. To further the application of our approach in sustainability-focused contexts, we performed the analysis using criteria focused on environmental and energy performance, in addition to other established criteria. In our study, the highest ranking use was a kindergarten, which scored 18% higher than the second-ranked alternative and over 90% higher than the lowest-ranked alternative. Full article

Aiming at the problems that the random probability characteristics of large-scale source and load resources lead to the ineffectiveness of deterministic planning methods, the standard grid structure is difficult to adapt to the demands of diversified scenarios. This paper proposes a grid-based scenario delineation method for distribution networks based on fuzzy comprehensive evaluation and SNN-DPC (density peak clustering based on shared-nearest-neighbors). First, analyze the response characteristics of various types of flexible resources, and establish a multi-dimensional comprehensive assessment index system that integrates operational characteristics and structural features. Second, the comprehensive weights of each index in the index layer are calculated based on the DEMATEL-ANP method and the CRITIC method, and the assessment value of the intermediate layer is calculated by the fuzzy comprehensive evaluation method. Finally, the assessment value of the intermediate layer is clustered based on the improved SNN-DPC algorithm, so as to classify the distribution grid scenarios. The results indicate that the proposed method can effectively and accurately classify distribution network scenarios. Full article