Search Results (532)

This study considers the potential of utilizing waste glass powder as a sustainable additive to improve the characteristics of clay subgrade soils. A comprehensive experimental program was designed, wherein a selected clay soil was amended with four distinct contents of glass powder that were finely ground: 0%, 3%, 6%, and 9% by weight. The primary objective was to evaluate the resultant improvements in soil strength and the enhanced interfacial bond between the treated subgrade and an overlying Type B granular subbase layer, which was further reinforced with an SS2 Geogrid. To characterize these effects, a suite of laboratory tests was performed, including the Modified Proctor Test, Atterberg Limits Test, California Bearing Ratio (CBR) test, and a large-scale direct shear test. A specially made large-scale instrument for direct shear was employed for the interface testing. The results demonstrate a clear positive correlation between the proportion of glass powder and the improvement in geotechnical properties. The most significant enhancement was observed at the 9% inclusion rate, which yielded a 6.6% increase in the maximum dry density and a substantial 49% improvement in the CBR value. Concurrently, this optimal mix design resulted in a 14% reduction in optimum moisture content, alongside notable decreases in the swelling and plasticity indices by 33% and 39%, respectively, confirming the efficacy of glass powder in stabilizing the clay subgrade. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. Full article

Software effort estimation (SEE) serves as a cornerstone of effective software project management, and case-based reasoning (CBR) stands out as one of the most extensively adopted approaches within this domain. Nevertheless, CBR-based SEE models are still plagued by two critical challenges: conventional case retrieval mechanisms lack the ability to differentiate the relative importance of various features, and data scarcity remains a persistent bottleneck. Both issues significantly compromise the estimation accuracy and interpretability of the models. To address these limitations, we propose a SHAP–Mixup synergistic framework that enhances both feature-aware similarity learning and data distribution modeling. Specifically, we introduce (1) a stability-aware SHAP-weighted similarity metric that integrates both the magnitude and variance of feature contributions to improve retrieval robustness, and (2) a density-aware Mixup augmentation strategy that generates synthetic samples guided by local data manifold structure rather than random interpolation. Experimental results on seven benchmark datasets demonstrate that the proposed method reduces MAE and MSE by up to 20.2% on average compared to baseline CBR models, while consistently improving Pred(0.25). Furthermore, by enhancing model interpretability, the proposed method equips project managers with actionable insights into the key drivers of software effort, thereby facilitating more informed and efficient resource allocation. Building on these findings, this study provides a novel and effective pathway for developing SEE models that are more accurate, robust, and transparent. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

Background/Objectives: The prognostic and predictive role of BMI in patients with HR+/HER2− MBC remains controversial, particularly in the era of CDK4/6 inhibitors. This study aimed to evaluate the association between baseline BMI and clinical outcomes in patients treated with palbociclib in a real-world setting. Methods: We conducted a multicenter retrospective observational cohort study including 326 patients with HR+/HER2− MBC treated with palbociclib in combination with endocrine therapy across six oncology centers in Romania. Only patients who received palbociclib for at least three months were included. Patients were stratified according to BMI into <25 kg/m² and ≥25 kg/m² groups. PFS and OS were the primary endpoints, while ORR and CBR were secondary endpoints. Results: Among the 326 patients, 66.56% were classified as overweight or obese (BMI ≥ 25 kg/m²). Median PFS was 23.66 months in the BMI < 25 group and 26.78 months in the BMI ≥ 25 group, with no statistically significant difference (HR 0.86; 95% CI 0.62–1.20; p = 0.373). Median OS was not reached in the BMI < 25 group and was 43.73 months in the BMI ≥ 25 group, also without a significant difference (HR 0.82; 95% CI 0.52–1.30; p = 0.397). ORR (29.07% vs. 28.89%) and CBR (90.70% vs. 88.33%) were comparable between BMI groups. In multivariate analysis, liver metastases and brain metastases were independently associated with worse outcomes, whereas BMI was not an independent prognostic factor. Conclusions: In this selected real-world cohort of patients with HR+/HER2− MBC who tolerated at least three months of palbociclib, baseline BMI was not associated with treatment response, PFS, or OS. While clinically informative, these results should not be interpreted as definitive evidence that body weight has no influence on palbociclib efficacy, given the methodological constraints of the analysis. BMI alone may be insufficient to capture the complex interplay between body composition and treatment outcomes, highlighting the need for more refined biomarkers of body composition in this setting. Full article

Importance: Triple-negative breast cancer (TNBC) is characterized by high tumor mutation burden and frequent programmed cell death ligand 1 (PD-L1) expression, making immune checkpoint inhibitors (ICIs) a promising therapeutic approach. However, randomized trials of chemoimmunotherapy (Chemo-IO) in locally recurrent unresectable or metastatic TNBC have shown inconsistent results, necessitating a clearer understanding of efficacy and patient selection. Objective: The aim of this study was to evaluate the efficacy and safety of chemotherapy combined with immunotherapy vs. chemotherapy alone in patients with locally recurrent unresectable or metastatic triple-negative breast cancer and to identify beneficiary populations to guide optimal treatment selection. Data Sources: PubMed, Embase, and the Cochrane Library were searched from database inception through 23 August 2025. Study Selection: Randomized clinical trials (RCTs) comparing chemotherapy combined with ICIs vs. chemotherapy with placebo or control in patients with locally recurrent unresectable or metastatic TNBC were selected. Data Extraction and Synthesis: Two investigators independently performed data extraction and assessed risk of bias using the Cochrane Risk of Bias 2 tool (RoB 2). Heterogeneity was evaluated using the I² statistic. Data were synthesized using random-effects meta-analysis models to calculate hazard ratios (HRs) for time-to-event outcomes and risk ratios (RRs) for dichotomous outcomes according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Results: Seven RCTs comprising 3485 patients (2085 in the Chemo-IO group, 1400 in the control group) were included. The median age across trials ranged from 52 to 57 years. Chemo-IO significantly improved PFS (HR, 0.82 [95% CI, 0.76–0.89]; p < 0.01) and OS (HR = 0.88; 95% CI: 0.81–0.96; p = 0.004) in the intention-to-treat (ITT) population, with PFS benefit particularly evident in PD-L1-positive patients (HR = 0.68, 95% CI: 0.59–0.79). However, OS improvement in the PD-L1-positive subgroup was not statistically significant. CBR did not differ significantly in the intention-to-treat population (RR, 1.11 [95% CI, 0.99–1.25]; p =  0.08) but was higher in PD-L1-positive patients (RR, 1.15 [95% CI, 1.01–1.31]; p = 0.04). Safety analyses revealed no significant differences in overall AE (RR, 1.01 [95% CI, 0.99–1.02]; p = 0.35), TEAE (RR, 1.01 [95% CI, 0.99–1.03]; p = 0.19), or grade ≥ 3 TEAE (RR, 1.00; [95% CI, 0.93–1.07]; p =  0.98). However, serious AE (RR, 1.32 [95% CI, 1.11–1.57]; p = 0.001) and irAE (RR, 1.86 [95% CI, 1.41–2.45]; p <  0.01) were more frequent with Chemo-IO. Conclusions and Relevance: Chemotherapy combined with immunotherapy significantly improved PFS and OS in patients with locally recurrent unresectable or metastatic TNBC, without substantially increasing chemotherapy-related toxicities. However, the OS benefit in PD-L1-positive patients was not statistically significant, and the combined regimen was associated with higher rates of serious and immune-related adverse events. These findings support the use of Chemo-IO as a treatment option, highlighting the importance of PD-L1 status and careful monitoring of immune-mediated toxicities in clinical practice. Full article

Soil stabilizers using conventional cement and lime binders incur high environmental costs owing to CO₂ emissions associated with their excavation, production, and processing. This has motivated research on low-carbon, waste-derived alternatives. The review shows that: biochar increases unconfined compressive strength (UCS) by 15–40% with a 2–5% dosage through pore filling and particle binding; nanocellulose promotes soil cohesion by 25–60% through fibrous network development and tensile bridging; recycled PET powder at 5–10% increases shear strength by 20–35% promoting mechanical interlocking, increasing stiffness, crack resistance and durability. Biochar provides direct carbon sequestration with a carbon transfer capacity of up to 2.5 tons CO₂-eq/ton. Recycled PET introduces waste valorization, with the potential to divert millions of tons of annual PET waste, while nanocellulose provides indirect carbon savings by avoiding emissions from cement and lime replacement. This review’s objectives are as follows: providing a comprehensive comparison of biochar, nanocellulose, and PET powder as promising non-cement composite stabilizers; identifying optimal dosage ranges and stabilization mechanisms for each material across different soil types; and outlining knowledge gaps and future research directions in sustainable geotechnical practices. The review assessed the individual and synergistic effects of the additives on critical geotechnical properties, including unconfined compressive strength (UCS), California bearing ratio (CBR), resilient resistance, swelling resistance, and the durability of the treated soil. Findings provide actionable guidance for practitioners seeking to reduce construction carbon footprints while maintaining geotechnical performance standards. Research gaps were identified, and future directions for integrating high-performance, low-carbon soil composites into sustainable construction solutions are proposed. Full article

This study investigates the challenging task of predicting the strength of subgrade soils, which serve as the foundation of superstructure systems. Due to the inherent complexity of soil behavior, traditional empirical methods often fall short in providing consistent and reliable estimations. To address this limitation, a fuzzy entropy-based TOPSIS multi-criteria decision-making (MCDM) approach is proposed. Methodologically, the study introduces a novel fuzzy entropy function that extends existing fuzzy entropy formulations and is compared against conventional fuzzy entropy measures. Using the newly proposed $P_m^{-}$ fuzzy entropy (m = 0.5), a soil stabilization quality ranking was obtained and validated against classical fuzzy entropy-based TOPSIS results. It is important to emphasize that the primary objective of the proposed framework is not to provide direct numerical estimates of CBR values, but rather to support the decision-making process by ranking soil options based on multiple criteria under conditions of uncertainty. The robustness of the rankings was further examined using California Bearing Ratio (CBR) data and comprehensive sensitivity analyses to consider uncertainties from expert judgments and laboratory measurements. The proposed approach offers a solution for multi-criteria decision-making processes in uncertain environments, ensuring high rating consistency and adaptability. Full article

Caulobacter segnis strain CBR1 enhances the germination rate and total biomass of Arabidopsis plants. Since bacteriophages are usually present in the rhizosphere, we isolated three additional bacteriophages, designated TMCBR2, TMCBR3, and W2, that can replicate using CBR1 as the host strain. The genome sizes and morphologies of the new phages were similar to those of the previously isolated Kronos phage, and when we compared the nucleotide sequence of these new phage genomes, we found only minor differences among the four phage genomes. Pairwise sequence comparisons indicated that the phage genomes should be grouped into three separate species within Kronosvirus genus. Interestingly, we found that most of the genomic variation occurred among genes of unknown function within a 10 kb region of the 42 kb genomes with little variation in the remaining 32 kb which contains the genes known to be important for phage propagation. Full article

This study aimed to develop new catalysts, based on MoS₂, for the hydrogenation of bromoquinolines without C-Br bond cleavage. The mechanochemical exfoliation of the bulk MoS₂ in the presence of NaCl resulted in the formation of the material (MoS₂-1), consisting of flat plates of size between ca. 40 × 100 and ca. 250 × 400 nm². Similar grinding of MoS₂ in the presence of NH₄Cl produced smaller nanoplates of size between ca. 10 × 30 and ca. 50 × 300 nm² (MoS₂-2). These materials were characterized using powder XRD, TEM, SEM, Raman spectroscopy and XPS. The specific surface area of the MoS₂-1 and MoS₂-2 samples was estimated using the analysis of N₂ adsorption isotherms. Both materials were catalytically active in the hydrogenation of quinoline; 1,2,3,4-tetrahydroquinoline (THQ) was the sole product and its yield grew proportionally to the accessible surface area of the catalyst. The hydrogenation of 5- and 8-bromoquinolines in the presence of MoS₂-1 and MoS₂-2 led to the respective bromo-THQs with almost quantitative yields, while the hydrogenation of 6-bromoquinoline resulted in the formation of the respective 6-bromo-THQ with the yield up to 30%. In the case of 7-bromoquinoline, N-methylated 7-bromo-THQ was formed almost quantitatively. Full article

Dynamic Spectrum Access (DSA) has emerged as a viable solution to address spectrum scarcity in shared-spectrum networks. In response, the FCC established the Citizens Broadband Radio Service (CBRS) to manage and facilitate shared use of the federal and non-federal spectrum in a three-tiered access and authorization framework. However, due to the open nature of spectrum access and the usually limited coverage of the monitoring infrastructure, enforcing access rights in a shared-spectrum network becomes a daunting challenge. In this paper, we stipulate the use of crowdsourcing as a viable approach to engaging volunteers in spectrum monitoring in order to enforce spectrum access rights robustly and reliably. The success of this approach, however, hinges strongly on ensuring that spectrum access enforcement is carried out by reliable and trustworthy volunteers within the monitored area. To this end, a hybrid spectrum monitoring framework is proposed, which relies on opportunistically recruiting volunteers to augment the otherwise limited infrastructure of trusted devices. Although a volunteer’s participation has the potential to enhance monitoring significantly, their mobility may become problematic in ensuring reliable coverage of the monitored spectrum area. To ensure continued monitoring, inspite of volunteer mobility, deep learning-based models are used to predict the likelihood that a volunteer will be available within the monitoring area. Three models, namely LSTM, GRU, and Transformer, are explored to assess their feasibility and viability to predict a volunteer’s availability likelihood over an extended time interval, in a given spectrum monitoring area. Recurrent Neural Networks (RNNs) such as GRU and LSTM are effective for tasks involving sequential data, where both spatial and temporal patterns matter, which is the focus of volunteer availability prediction in spectrum monitoring. Transformers, on the other hand, excel at handling long range dependencies and contextual understanding. Furthermore, their parallel processing capabilities allows faster training and inference compared to RNN-based models like GRU and LSTM. A simulation-based study is developed to assess the performance of these models, and carry out a comparative analysis of their ability to predict volunteers’ availability to monitor the spectrum reliably. To this end, a real-world trace dataset of volunteers’ location, collected over five years, is used. The simulation results show that the three models achieve high prediction accuracy of volunteers’ availability, ranging from 0.82 to 0.92. The results also show that a GRU-based model outperforms LSTM and Transformer-based models, in terms of accuracy, Root Mean Square Error (RMSE), geodesic distance, and execution time. Full article

Bundle recommendation has gained significant attention, but it faces two key challenges: sparse interaction data and complex UB, UI, and BI relations. Recent work uses multi-view contrastive learning, yet current frameworks rely on fixed-weight fusion that ignores view-specific importance and suffers from gradient suppression on sparse data. We propose TriadCBR, a tri-view adaptive contrastive learning architecture for bundle recommendation. It uses a simplified GCN to learn view-specific representations and a Mixture-of-Experts (MoE) module to generate personalized fusion weights, addressing the limitations of fixed-weight fusion. TriadCBR further incorporates a fine-grained contrastive module integrating InfoNCE, DCL, and Barlow Twins. This combination effectively mitigates gradient vanishing from invalid negatives and minimizes cross-view feature redundancy. To handle data sparsity, we design a Difficulty-Aware BPR (DA-BPR) with curriculum augmentation to dynamically refine the ranking trajectory. Extensive experiments on Youshu, iFashion, and NetEase demonstrate that TriadCBR achieves statistically significant improvements, boosting Recall and NDCG by an average of 3.61%, with 9 of 12 metric–dataset combinations reaching statistical significance, over state-of-the-art baselines, validating the robustness of its dynamic fusion and adaptive optimization. Full article

Enzyme-based stabilizers have been used in soil stabilization in the construction industry for many years as alternatives to traditional stabilizers and have produced successful results. These novel stabilizers gained popularity over standard stabilizers such as cement and lime due to their non-toxic and non-hazardous properties. Although enzymatic stabilizers perform very well for a variety of soil types under different environmental conditions, their effectiveness under varying temperatures has not been sufficiently investigated. This is more important in countries where there are significant fluctuations in temperature that are further exacerbated by the impact of climate change. As enzymatic products are organic and biodegradable, their susceptibility to temperature variations must be well understood. In this study, the efficiency of a commercially available enzymatic stabilizer was investigated. Tests were carried out to assess the influence of temperatures ranging from 4 °C to 80 °C on the geotechnical properties of enzymatically stabilized soil. The investigation was conducted into the compaction characteristics, index properties, compressive strength, and CBR of the stabilized soil. The results indicate that the stabilizing effect of the enzyme remains largely unchanged up to approximately 40 °C. The outcome of the study enables practitioners to use more sustainable stabilizers to treat problematic soils in regions where temperature fluctuations are within the tested range. Full article

Phenothiazine is a heteroaromatic molecule capable of various noncovalent interactions, including halogen bonding and π-stacked association. Despite its broad use in functional materials and pharmaceutical ingredients, a systematic comparison of these interaction modes has been lacking. Here, we report a combined experimental and computational study of intermolecular interactions of phenothiazine with a prototypical halogen-bond (HaB) donor (tetrabromomethane), planar π-electron acceptors (tetracyanopyrazine and tetrafluoro-p-benzoquinone), and multifunctional species capable of both interaction types (iodo- and bromo-3,5-dinitrobenzenes). X-ray structural analysis revealed that CBr₄ forms exclusively C–Br···π halogen bonds with the aromatic rings of phenothiazine, whereas all π-acceptors yield alternating donor–acceptor stacks characterized by multiple short contacts indicative of multicenter interactions. Notably, co-crystals of iodo- and bromodinitrobenzenes with phenothiazine display only π-stacked architectures. Density-functional calculations showed that isolated HaB complexes involving N, S, or π sites of phenothiazine possess comparable binding energies (≈−3 kcal mol⁻¹), whereas π-stacked complexes are substantially stronger (≈−9–12 kcal mol⁻¹). QTAIM, NCI, NBO, and energy-decomposition analyses indicated that while amounts of charge transfer in halogen-bonded and π-stacked complexes are comparable, the enhanced stability of the latter originates primarily from a large dispersion contribution. These results rationalize the solid-state preference for π-stacking over halogen bonding in systems where both motifs are accessible and clarify the hierarchy and physical origin of noncovalent interactions involving phenothiazine, providing guidance for the design of supramolecular assemblies and functional materials based on this versatile electron donor. Full article

Expansive or soft soils cause significant geotechnical issues for foundations and subgrades because they show swell–shrink behaviour under wet and dry conditions. These volume changes can result in cracking, heaving, uneven settlement, and structural or pavement damage, ultimately increasing maintenance and repair costs. While traditional Portland cement and lime stabilisers effectively enhance soil strength and reduce swell–shrink behaviour, the cement production process is responsible for only approximately 7%–8% of global CO₂ emissions, prompting a transition toward sustainable alternatives. This comprehensive review consolidates recent advancements in soil stabilisation using industrial by-products, such as fly ash, ground granulated blast furnace slag (GGBS), steel slag, cement kiln dust, silica fume, bottom ash, red mud, waste foundry sand, brick dust, calcium carbide residue, water treatment sludge, etc. These materials leverage pozzolanic and latent hydraulic properties to form C-A-H, C-S-H, and N-A-S-H gels, thereby densifying the soil microstructure, improving CBR (%), UCS, and reducing plasticity and swelling potential. Optimisation studies indicate that industrial waste stabilisers often match or exceed conventional binder performance, GGBS-steel slag combinations yielding 105% higher UCS than ordinary Portland cement, and silica fume enhances cement-stabilised soils by 22% at reduced dosages. However, inherent compositional variability, long-term durability concerns including sulfate attack and freeze–thaw degradation, and the absence of standardised design guidelines restrict large-scale implementation. This review integrates mechanistic, microstructural, and sustainability insights, highlighting the need for durability research, standardised methods, and large-scale field validation to advance industrial waste-based stabilisation within circular construction practices in geotechnical engineering. Full article