Search Results (2,955)

This study presents a systematic critical review of 91 peer-reviewed publications published between 2000 and 2024, examining the sustainability of Mediterranean tourism destinations through indicator-based frameworks. Using the Scopus database (Elsevier) and PRISMA-based screening, the review coded studies by methodological approach, indicator type, sustainability dimension, stakeholder involvement, and data source. Quantitative and mixed-methods designs dominated the corpus, together accounting for 90.1% of the reviewed studies, while geographical coverage was highly concentrated in Spain (52.7%), Greece (14.3%), and Italy (13.2%), which jointly represented 80.2% of the corpus. The literature also expanded markedly over time, from 8 studies (8.8%) in 2003–2010 to 39 studies (42.9%) in 2021–2024. Dimensional analysis showed strong emphasis on economic and environmental sustainability assessment, addressed in 92.3% and 91.2% of studies respectively, whereas cultural sustainability received attention in only 23.1% of the corpus. These findings highlight persistent problems of geographic imbalance, limited standardisation, and insufficient multidimensional integration in Mediterranean tourism sustainability assessment. In response, the study proposes the Mediterranean Sustainability Assessment Framework (MSAF), an appraisal-oriented framework for evaluating and improving destination-level sustainability indicator systems in terms of dimensional completeness, methodological pluralism, and contextual embeddedness. Full article

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced multi-criteria decision-making (MCDM) methodologies, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Combined Distance-based Assessment (CODAS). Surface roughness (SR), cutting force (CF), carbon emissions (CE), and energy consumption (EC) were systematically evaluated and ranked using the L₁₈ Taguchi Orthogonal Array. Minimum Quantity Lubrication (MQL) and cryogenic CO₂ cooling techniques were used to achieve a superior surface finish and reduce friction at the tool-workpiece interface, thereby minimizing scratches and thermal damage. Desirability evaluation results showed the optimal machining conditions for milling of Al7075 (HMMCs) occurred at a cutting speed (V_c) of 200 m/min, a feed rate (f) of 0.02 mm/rev, and a depth of cut (a_p) of 0.3 mm, proving the potential of integrating MCDM tools with effective cooling strategies. The desirability method favored a balanced compromise, while entropy-weighted TOPSIS/CODAS emphasized energy and carbon-related responses. Improvements of 6% in cutting force, 7% in surface roughness, and a 7% reduction in energy consumption, along with 8% lower carbon emissions, were achieved, demonstrating the effectiveness of hybrid cooling strategies in promoting eco-friendly and resource-efficient processes. Full article

The objective of this study is to mitigate the bottom-out failure and improve the energy absorption of conventional helmet liners during high-energy impacts, thereby reducing the risk of head injuries. To this end, a locally reinforced Primitive-type triply periodic minimal surface (P-TPMS) energy-absorbing liner is proposed for the helmet forehead region, which facilitates progressive energy dissipation through layer-by-layer buckling deformation. A finite element model of a helmet–head coupling was created based on a previously verified high-fidelity head model and subsequently validated against the ECE 22.06 standard drop-test methodology. Three critical design parameters—outer protective layer thickness, triply periodic minimal surface (TPMS) unit cell size, and wall thickness—were optimized employing the Box–Behnken Design (BBD) response surface methodology, resulting in quadratic regression models for the head injury criteria (HIC) and peak linear acceleration (PLA) with good fit ($R^2$ > 0.97). Optimal parameter combinations were established using multi-objective optimization, with protective efficacy carefully assessed from both head dynamic response and biomechanical response perspectives. The ideal P-TPMS liner possesses an outer protective layer thickness of 14.95 mm, a TPMS unit cell size of 12.23 mm, and a wall thickness of 3.93 mm. Compared to the traditional expanded polystyrene (EPS) liner, the optimized P-TPMS liner significantly reduces HIC (by ∼16%) and PLA (by ∼14%) while extending the impact duration. More critically, it transitions both intracranial pressure and brain tissue strain below their respective clinical injury thresholds, substantially lowering the risks of skull fracture and mild traumatic brain injury (mTBI). The P-TPMS construction facilitates continuous energy dissipation during impacts via incremental layer-by-layer buckling deformation, hence extending impact duration and markedly improving helmet protective efficacy. These findings offer theoretical foundations and technical direction for the creation of localized heterogeneous liner designs in advanced high-performance helmets, although the results are limited to frontal flat-anvil impact conditions. Full article

Effectively modeling software failure behavior is crucial for reliability assessment and planning of releases. However, many current software reliability growth models assume that failures are independent and fault detection mechanisms are simplified. However, these assumptions may not accurately represent real-world testing environments. This study introduces a novel Nonhomogeneous Poisson Process (NHPP)-based Software Reliability Growth Model (SRGM) that includes dependent failure behavior and exponentially decaying fault detection rates to better reflect the software debugging process. The proposed model was validated using real failure datasets and compared with 17 existing models. The performance of the model was assessed using various goodness-of-fit criteria, such as errors, prediction accuracy, and metrics based on information theory. To provide a more thorough evaluation, a multi-criteria decision-making approach was used to rank the competing models based on their overall performance. Furthermore, a one-at-a-time sensitivity analysis was conducted to examine how the initial values of the parameters affected the model’s behavior. These findings indicate that the sensitivity of the model to this parameter varies depending on the dataset used. The results indicate that the proposed model achieved superior performance across multiple evaluation criteria and consistently obtained the best overall ranking under the integrated multi-criteria framework. In Dataset 1, the proposed model achieved the best performance in most goodness-of-fit criteria, whereas in Dataset 2 it produced the best results across all twelve evaluation criteria. The results show that the proposed model offers improved or competitive performance compared to existing models and provides greater flexibility in capturing complex failure processes within software systems. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Artificial intelligence is reshaping warehouse work through algorithmic task allocation, scanner-based monitoring, KPI feedback, dynamic scheduling, and real-time performance control. Although these systems can improve coordination and operational visibility, they also create governance risks related to fairness, transparency, autonomy, privacy, workload pressure, trust, and employee resistance. This study develops a human-centered decision framework for prioritizing algorithmic management governance packages in third-party logistics (3PL) warehousing. The main contribution is to translate employee-level governance concerns into a scenario-sensitive decision model that helps managers select appropriate governance packages under different operational pressures. The study uses survey data from 380 warehouse employees to examine key psychological and behavioral mechanisms, including procedural fairness, transparency, system/information quality, autonomy, privacy concern, workload, trust, acceptance, and resistance/disengagement. These survey-supported constructs are then converted into six governance criteria: procedural fairness, transparency and contestability clarity, system and information quality, autonomy support, privacy boundary governance, and workload protection. A seven-expert panel evaluates five governance packages under three scenarios: peak season surge, labor shortage/high turnover, and audit pressure/compliance scrutiny. Methodologically, the framework combines Dynamic Multi-Facet Fuzzy Sets to capture membership, non-membership, hesitancy, engagement, and resistance; Bayesian Network weighting to reflect dependencies among governance criteria; and PCA-based ranking optimization to generate scenario-specific and robust rankings. Comparative validation with SAW and TOPSIS is also used to assess ranking consistency. The findings show that effective algorithmic management governance is not a fixed compliance solution. Transparency, workload protection, autonomy support, privacy boundary governance, and procedural fairness become more or less important depending on the operational scenario. A2, which combines transparency, workload protection, and autonomy support, emerges as the strongest robust package. A1 performs best under labor shortage/high turnover, while A3 performs best under audit pressure/compliance scrutiny. These results suggest that 3PL warehouses should adopt adaptive governance routines that combine explainability, contestability, workload safeguards, privacy boundaries, and employee voice mechanisms. The study contributes to the literature on AI in socio-technical systems by showing how human, organizational, and ethical concerns can be embedded into an interpretable decision framework for responsible algorithmic management in logistics work environments. Full article

Soil vulnerability is commonly assessed using environmental indicators; however, the lack of systematic and continuous monitoring often leads to incomplete and fragmented data, particularly in surface coal mining areas affected by potentially toxic element (PTE) contamination. Existing studies mainly focus on impact assessment, with limited emphasis on structured decision-support frameworks for selecting optimal soil protection strategies. This study addresses this gap by proposing an integrated hybrid decision-making framework that combines the Analytic Hierarchy Process (AHP), Sustainability Balanced Scorecard (SBSC), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The main contribution lies in integrating strategic sustainability perspectives (SBSC) with quantitative multi-criteria methods (AHP and TOPSIS), enabling a transparent and consistent evaluation of soil protection strategies across environmental, economic, technical, and social dimensions. The framework was applied to the Kostolac mining and energy complex in Serbia as a representative case study, using data from the State of the Environment Report as the basis for expert evaluation. The results identify risk reduction and environmental effectiveness as the dominant criteria, while the Progressive Strategy (SBSC) achieved the highest ranking. Sensitivity analysis confirmed the robustness of the model. From a policy perspective, the findings support prioritizing sustainability-oriented and risk-reduction strategies in mining regulations and investment planning. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

In this study, the sustainability of maize production with farmer’s practice (FP) and with redesigned plant density and fertilizer use were assessed at a household level based on social, agronomic, economic, and environmental principles. Farmers’ preference, household grain (maize) self-sufficiency, gross margin, and nitrogen use efficiency were used as indicators for social, agronomic, economic, and environmental principles, respectively. The results revealed that the preference of redesigned density and redesigned fertilizer use (RDRF) was 95% and 100% in CRV and Jimma, respectively, whereas the preference of FP was 45% and 20% in the respective regions. With all production technologies, farmers in both regions could achieve their family grain self-sufficiency. The grain self-sufficiency in CRV was 2.47 t household⁻¹ year⁻¹ whereas in Jimma, the grain self-sufficiency was 1.77 t household⁻¹ year⁻¹. In CRV, RDRF, redesigned density and current fertilizer (RDCF), and current density redesigned fertilizer (CDRF) were economically viable. However, in Jimma, less than 50% of households profited from RDRF and CDRF. In CRV, maize production using all crop management practices was associated with soil mining, whereas in Jimma, the use of RDRF resulted in 18% environmental sustainability. It is concluded that producing maize with RDRF is sustainable from social, agronomic, and economic perspectives whereas producing maize with FP is sustainable only from the agronomic dimension. Further redesigning maize management practices is of paramount important for achieving economic and environmental sustainability dimensions in CRV and Jimma in Ethiopia. Full article

Background: Previous work from our group demonstrated an association between immunohistochemical detection of Human cytomegalovirus (HCMV) late antigen and poor event-free survival (EFS) in pediatric medulloblastoma. Whole-genome sequencing (WGS) further identified increased abundance of HCMV-aligned reads at the UL88 locus, particularly in Group 3 tumors, a molecular subgroup associated with aggressive clinical behavior and poor prognosis. Methods: We performed an integrated multi-omics analysis of pediatric medulloblastoma using WGS (n = 39) and RNA sequencing (RNA-seq; n = 28) datasets. RNA-seq data were filtered using stringent alignment criteria (MAPQ ≥ 20) and compared with fetal brain (n = 12), adult brain (n = 12), and HCMV-infected cell culture controls (n = 3). Only high-confidence uniquely aligned reads were retained to reduce nonspecific and multi-mapped viral alignments. Sequencing reads were aligned to the HCMV Merlin reference genome (NC_006273.2) using a standardized analytical pipeline. A subset of 28 cases with matched tumor WGS, tumor RNA-seq, and germline WGS data was used for integrated multi-omics analyses. Orthogonal validation analyses were performed in Group 3 tumors using independent genomic and transcriptomic approaches. Exploratory survival analyses were conducted in a combined cohort (n = 84) integrating genomic and immunohistochemical datasets. Results: Recurrent low-level HCMV-aligned molecular signals were identified across medulloblastoma datasets. Reads aligning to UL76, UL88, and UL99 were the most consistently detected HCMV-associated late-gene signals across RNA-seq and WGS datasets. A composite HCMV late-gene signature (UL76–UL88–UL99) showed higher levels in Group 3 tumors than in other molecular subgroups (p < 0.05 in WGS analyses). Orthogonal analyses demonstrated concordant low-level HCMV-associated genomic and transcriptomic signals enriched in tumors with MYC-associated activation and chromosome 17 imbalance. In the combined cohort (n = 84), elevated HCMV-associated signal assessed by immunohistochemistry and genomic profiling was associated with reduced EFS (median 55 vs. 147 months; log-rank p < 0.001). The subgroup classified as HCMV-high Group 3 demonstrated the strongest association with adverse outcome in exploratory multivariable analyses (HR = 6.43, p = 0.002). Conclusions: This study identifies recurrent low-level HCMV-associated genomic and transcriptomic signals across pediatric medulloblastoma datasets, with preferential enrichment in biologically aggressive Group 3 tumors. Although the extremely low abundance of viral-aligned reads precludes definitive evidence of productive viral infection, the reproducible detection of HCMV-associated molecular signatures across independent sequencing platforms supports further investigation into a potential oncomodulatory association in pediatric medulloblastoma. Additional validation using optimized viral detection methodologies, independent cohorts, and mechanistic studies will be necessary to clarify the biological and clinical significance of these findings. Full article

This paper proposes a mathematical co-design framework for synchronous Boost DC-DC converters and their PI voltage controllers. In contrast to the conventional sequential design approach, where the power stage is sized first and the controller is tuned afterward, the proposed method treats the converter and the controller as a single coupled design problem. A nonlinear averaged model of the synchronous boost converter operating in continuous conduction mode is considered, explicitly incorporating the inductor series resistance, the capacitor equivalent series resistance, and the on-state resistances of the active switches. In addition, a simplified but physically interpretable loss model is included in order to capture inductor copper loss, capacitor ESR loss, semiconductor conduction loss, and switching loss. Based on this formulation, the joint design of the power stage and the PI controller is cast as a constrained multi-objective optimization problem whose decision variables include the inductance, capacitance, switching frequency, and controller gains. The optimization criteria account for output-voltage ripple, settling time, total losses, and current stress, while practical constraints related to duty cycle, current limits, ripple bounds, and closed-loop feasibility are enforced. The proposed framework makes it possible to compute Pareto-efficient designs and to reveal trade-offs that remain hidden under classical decoupled design procedures. Numerical case studies are structured to compare the proposed co-design strategy with a conventional sequential-design baseline. An optional technology-aware extension is also considered, allowing the influence of different semiconductor classes, such as Si, SiC, and GaN, to be assessed through technology-dependent loss and switching-frequency assumptions. The results indicate that the proposed framework provides a mathematically grounded and practically useful basis for integrated converter–controller synthesis in nonideal power electronic systems. Full article

This study analyzed the initial adsorption and activation of CO₂ on bimetallic Cu_nSc nanoclusters, with n = 3–7, using DFT calculations in ORCA with the r²SCAN-3c method. A total of 20 bare clusters and their corresponding Cu_nSc–CO₂ complexes were investigated, considering four structural configurations for each composition. To avoid classification based solely on adsorption energy, a global CO₂ activation index was developed and defined as IACO₂ = z(AG) + z(CTCO₂) + z(Bending) + z(ΔrC–O). In this index, AG = −ΔG_ads, CT_CO2 = −qCO₂, bending corresponds to (180° − ∠O–C–O), and (ΔrC–O) represents the average elongation of the C–O bonds. This descriptor enabled distinguishing complexes that only stabilize CO₂ from those that induce effective geometric and electronic activation. Although 5IV and 3IV exhibited favorable adsorption, with (ΔG_ads) values of −52.978 and −53.494 kcal mol⁻¹, respectively, their molecular activation was low, with nearly linear CO₂ and minimal or unfavorable charge transfer. In contrast, 7III and 7II showed the highest activation, with CT_CO2 values of 1.206 and 1.163, bending values of 69.867° and 68.869°, and C–O elongations of 0.208 and 0.195 Å, respectively. The standardized (IACO₂) ranking identified 7III, 7II, 3III, and 3II as the most relevant systems, with scores of 100.0, 93.8, 88.2, and 86.8, respectively. These results show that CO₂ activation on Cu_nSc nanoclusters should not be assessed solely by (ΔG_ads), but rather by a multi-criteria approach that accounts for stability, charge transfer, and molecular distortion. Full article

This study examines how investment priorities for sustainable rural development are shaped when financial, environmental, social, and institutional criteria are evaluated simultaneously. Using the Analytic Hierarchy Process (AHP), the study assesses six investment alternatives: eco-tourism, agro-tourism, renewable energy, digital tourism, sustainable agriculture, and cultural tourism. The results reveal the dominance of financial performance and risk considerations, which together account for more than two-thirds of total decision weight. Renewable energy emerges as the highest-ranked investment alternative, whereas agro-tourism and sustainable agriculture remain under-prioritized despite their environmental and social benefits. A comparative scenario analysis demonstrates that policy-oriented weighting structures substantially alter investment rankings, increasing the attractiveness of locally embedded and sustainability-oriented activities. The findings suggest a structural divergence between market-driven capital allocation and broader rural development objectives. By integrating sustainable finance and rural development within a multi-criteria decision-making framework, the study provides practical insights for investors and policymakers seeking to align investment decisions with long-term sustainability goals. Full article

Background/Objectives: Orthodontic treatment induces controlled mechanical forces that alter the periodontal environment, including changes in oral microbiota composition and activation of local inflammatory pathways. Despite the widespread and growing use of orthodontic appliances across all age groups, the magnitude, timing, and multi-domain biological impact of these changes have not been comprehensively quantified in a single systematic synthesis. This systematic review and meta-analysis aimed to synthesize the available evidence on periodontal clinical parameters, oral microbiota composition, and local inflammatory biomarkers associated with orthodontic treatment using fixed appliances and clear aligners, and to provide a structured, GRADE-rated evidence base for clinical practice. Methods: A systematic review and meta-analysis was conducted in accordance with PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched from inception to March 2026. Prospective cohort studies, longitudinal clinical studies, and randomized controlled trials evaluating periodontal parameters, oral microbiota, and inflammatory biomarkers during orthodontic treatment were included. Quantitative synthesis was performed using mean differences or standardized mean differences with 95% confidence intervals, primarily assessing within-group (pre–post) changes. Results: Eighteen studies (n = 812 patients; follow-up 3–12 months) met inclusion criteria. Fixed orthodontic appliances were consistently associated with transient increases in plaque index (MD 0.45, 95% CI 0.32–0.58; I² = 62%), gingival index (MD 0.38, 95% CI 0.25–0.51; I² = 55%), and bleeding on probing (MD 15.2%, 95% CI 10.1–20.3%; I² = 48%), particularly during early treatment phases. Microbiological analyses demonstrated within-group shifts toward increased prevalence of periodontopathogenic species (Streptococcus mutans OR 2.45, 95% CI 1.89–3.18; Porphyromonas spp. OR 2.14, 95% CI 1.67–2.75) in patients treated with fixed appliances. Local inflammatory responses were characterized by elevated IL-1β (MD 1.2, 95% CI 0.8–1.6) and IL-6 (MD 0.9, 95% CI 0.6–1.2) in gingival crevicular fluid. Certainty of evidence was rated moderate for plaque and gingival indices and low for microbiological and inflammatory outcomes (GRADE). Conclusions: Orthodontic treatment—particularly with fixed appliances—is associated with transient, reversible deterioration of periodontal indices, shifts toward a more dysbiotic oral microbiome, and elevation of local inflammatory mediators in gingival crevicular fluid during active treatment phases. These changes are manageable through structured preventive protocols and regular periodontal monitoring. Future prospective studies with concurrent control groups and standardized multi-domain outcome measures are needed to better define the magnitude and reversibility of these biological responses. PROSPERO: CRD420261336117. Full article

Introduction: Lung cancer, the leading cause of cancer-related mortality worldwide, is a heterogeneous malignancy comprising distinct histological and molecular subtypes, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases and adenocarcinoma (ADC) representing the most prevalent histotype. An emerging pathological feature of NSCLC, spread through air spaces (STAS)—defined as the extension of tumor cells into the lung parenchyma beyond the main tumor margin—has been associated with worse disease-free and overall survival and has been proposed as a possible predictor of recurrence to guide surgical extent. Concurrently, recent comprehensive genomic profiling of early-stage NSCLC has highlighted the need to interpret multi-omics data and their relationship with pathological variables, including IASLC histological subtypes, to better personalize treatment strategies. In this context, we investigated the overall distribution of STAS and its association with tumor mutational profiles and IASLC histological subtypes in a large real-world cohort of lung adenocarcinoma patients from the LANTERN project. Materials and Methods: In a prospective, multicenter observational study (March 2023–December 2024), 271 NSCLC patients were enrolled, and clinicopathological, immunohistochemical, and genomic data were collected; comprehensive genomic profiling was performed using the TruSight Oncology 500 assay to analyze 523 cancer-related genes, tumor mutational burden (TMB), and microsatellite instability; and STAS was assessed according to IASLC criteria. Adenocarcinoma accounted for roughly 90% of the cases, with a median age of 69 years and a predominance of stage IV disease (49.5%). STAS was evaluable in 162 cases and was detected in 17.9% of tumors. Results: STAS-positive tumors showed a higher trend towards locally advanced and advanced disease; no differences were observed in sex, age, smoking status, tumor mutational burden, or PD-L1 expression. Additionally, STAS-positive tumors showed a higher association with micropapillary, mucinous, and papillary patterns, whereas the acinar pattern was more frequent in STAS-negative tumors. The most frequently mutated genes were TP53, KRAS, EGFR, and STK11, with no significant differences between groups; ROS1 alterations were absent in STAS-negative tumors but detected more frequently in STAS-positive cases. Conclusions: Overall, these findings indicate that STAS positivity is associated with high-risk histological subtypes and advanced disease, suggesting its importance as a marker of tumor aggressiveness and emphasizing the need for its systematic evaluation in lung adenocarcinoma to better guide surgical planning and patient risk assessment. Full article