Abstract: In 1992, Hudzik and Landes [Math. Ann.] derived a breakthrough generalization of the triangle inequality for two nonzero elements in normed linear spaces, which was generalized to finitely many nonzero elements independently in 2006 by Dragomir [Bull. Aust. Math. Soc.] and in 2007 by Kato, Saito and Tamura [Math. Inequal. Appl]. We derive a non-Archimedean version of Hudzik-Landes-Dragomir-Kato-Saito-Tamura inequality.

Abstract: U.S. adults with substance use disorders (SUD) underutilize specialty substance use treatment (SUT) and are more likely to use mental health treatment (MHT) than SUT. In this study based on the 2022 and 2023 National Survey on Drug Use and Health (N=19,555, age 18+ with past-year SUD), we examined clinical and other factors associated with four distinct behavioral health service configurations: no treatment, both SUT and MHT, SUT only, and MHT only. We fitted two multinomial and binary logistic regression models to examine associations of treatment use patterns with SUD and mental illness severity as need factors, while controlling for predisposing, enabling, and other need factors. The findings show that 59.3% U.S. adults with SUD did not use SUT or MHT in the past year, 10.7% used both SUT and MHT, 3.7% used SUT only, and 26.4% used MHT only. Severe SUD (RRR=2.77, 95% CI=2.13-3.60), compared with mild SUD, and all levels of mental illness severity (RRR=7.27, 95% CI=5.41-9.79 for serious mental illness) were associated with a higher likelihood of receiving both SUT and MHT. Both moderate and severe SUD, but not mental illness severity, were associated with receiving SUT only. All levels of mental illness severity were also associated with a higher likelihood of receiving MHT only (RRR=7.30, 95% CI=6.17-8.64 for serious mental illness), whereas severe SUD was associated with a lower likelihood of receiving MHT only. The findings were similar in analyses restricted to users of any treatment or SUT. In sum, receipt of both SUT and MHT is concentrated among individuals with the highest clinical severity in both SUD and mental illness. These findings underscore the importance of integrating substance use and mental health services.

Abstract: Bird strike events on rotating jet engine fan blades pose significant risks to aviation safety, yet high-fidelity numerical simulations remain computationally expensive, limiting their use in parametric design studies. This study develops a physics-informed machine learning surrogate framework for predicting bird strike response on rotating Ti-6Al-4V fan blades, systematically comparing Lagrangian (gelatin-based, Mooney–Rivlin) and Smoothed Particle Hydrodynamics (SPH, water-like) formulations. A total of 100 explicit dynamic simulations were conducted in ANSYS LS-DYNA (50 per formulation), varying bird impact velocity and blade angular speed. Random Forest, Support Vector Regression, Polynomial Regression, and XGBoost regression models were trained and evaluated using five-fold cross-validation. Results demonstrate that SPH-based surrogates achieve superior predictive accuracy, with Random Forest yielding R² = 0.9938 for maximum deformation and R² = 0.9962 for total energy dissipation. In contrast, Lagrangian-based stress surrogates exhibited severe performance degradation (R² = 0.24) due to mesh-dependent numerical noise. The trained surrogates achieved computational speed-up factors of 10⁴–10⁵ relative to direct simulation. These findings establish that surrogate model reliability is fundamentally governed by the numerical quality of the training data, providing guidance for integrating machine learning with impact simulation workflows in aero-engine blade design.

Abstract: Conventional concrete faces limitations such as brittleness, low energy absorption, and a significant environmental impact due to its reliance on natural resources. Integrating natural fibers (NF) and recycled coarse aggregate (RCA) into concrete presents a promising avenue for enhancing both performance and sustainability. However, accurately predicting the strength of these innovative concrete mixtures remains challenging. This study investigates the predictive capabilities of two machine learning (ML) models: Classification and Regression Trees (CART) and Stepwise Polynomial Regression (SPR), in forecasting the compressive and splitting tensile strength of NF-reinforced concrete incorporating RCA. Results unequivocally demonstrate the superior predictive accuracy of the CART model. CART exhibited significantly higher R-squared values and lower error metrics (RMSE, MAD, MAPE, MSE) for both compressive and splitting tensile strength. For compressive strength CART achieved R² = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R² = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These findings underscore the significant potential of ML, particularly CART, in optimizing the design of sustainable concrete structures by enabling more precise and efficient strength predictions, ultimately contributing to more sustainable and resilient infrastructure.

Abstract: Housing affordability is commonly analysed through price-to-income ratios and housing cost burdens. However, housing systems also function as mechanisms that distribute financial and stability risks across households through tenure structures and institutional arrangements. This study examines housing affordability as part of a broader process of risk allocation within housing systems. Using survey evidence from Slovenia—a high-homeownership system shaped by post-socialist privatization and a limited rental sector—the analysis investigates how households perceive housing affordability and how these perceptions influence expectations regarding future housing market developments. The results show that lower perceived housing affordability is associated with stronger expectations of future housing price growth. Expectations regarding housing purchase price growth are also strongly linked to expectations of future rent increases, suggesting that households interpret both dynamics as part of broader housing market pressure. Regional differences in perceived housing affordability are marginally significant, indicating spatial variation in housing conditions. Respondents who recently participated in the housing market report significantly different expectations regarding future housing price developments. These findings highlight the importance of understanding housing affordability as a perception-based mechanism embedded within broader housing systems.

Abstract: In this paper, a short-term active power curtailment (ST-APC) strategy for doubly fed induction generator (DFIG) wind farms is proposed to enhance first-swing rotor angle stability under fault disturbances. While wind power is a clean renewable resource that is widely deployed, its large-scale integration heightens concerns about transient stability. After analysing DFIG operating principles, this study advocates for using short-horizon active power control to mitigate the adverse stability impacts of wind farms. Using the Western System Coordinating Council (WSCC) three-machine nine-bus test system, the effectiveness of the ST-APC strategy across diverse operating conditions is verified. Simulation results show that, following a fault, modulating the DFIG’s active output effectively suppresses the first swing, postpones loss of synchronism, and increases the critical clearing time (CCT). The scheme yields notable benefits regarding improvements in overall stability, reductions in the frequency nadir, and acceleration of frequency recovery. Sensitivity analyses further examine the effects of activation time, control duration, and curtailment depth on CCT and offer tuning recommendations. The findings indicate that the proposed strategy is practical and adaptable, making it suitable for power systems with high wind power penetration.

Abstract: End-stage lung disease represents a major challenge in modern medicine. Lung transplantation remains the most effective treatment; however, donor shortage and rejection significantly limit its clinical impact. The engineering of bioartificial lung grafts using patient-derived cells may lead to new therapeutic strategies. Advanced culture conditions enable the formation of functional three-dimensional tissues from lineage-committed cells. Currently, bioartificial grafts capable of gas exchange have been created and transplanted in animal models. Ongoing challenges in tissue engineering include the development of ideal scaffolds and the full maturation of engineered structures to ensure graft longevity after in vivo implantation. With collaborative efforts, the goal is to design patient-derived lung grafts and achieve clinically relevant translational milestones such as airway grafts and disease models.

Abstract: This study comparatively assesses the Municipal Emission Reduction Plans (MERPs) of Spetses, Platanias, and Souli, emphasizing the role of water infrastructures in shaping municipal carbon footprints. Using secondary data extracted from the officially approved MERPs, a descriptive inter-municipal comparative analysis was conducted based on demographic variables and sectoral and total greenhouse gas emissions for the base year 2019 and the reference year 2023. Percentage changes in population and sector-specific emissions were calculated at the municipal level and subsequently compared across the three municipalities. The analysis covers municipal buildings and lighting, transport, waste and wastewater management, water and irrigation distribution systems, while also presenting the emission reduction actions and projected 2030 targets documented in the respective plans. The results reveal substantial inter-municipal variations, driven primarily by water supply models, geomorphology, altitude differences, network extent, and the distribution of service responsibilities. In Platanias and Souli, water supply and irrigation constitute among the most energy-intensive sectors, contributing significantly to total emissions, whereas in Spetses the externalized water supply model alters the emission structure. The findings indicate that water infrastructure can represent a significant emission source and that effective mitigation depends on spatially tailored strategies considering geomorphological, operational, and administrative characteristics.

Abstract: Marine Spatial Planning (MSP) and Terrestrial Spatial Planning (TSP) have traditionally evolved as disconnected systems, limiting the capacity to address coastal dynamics under climate change. This article proposes Integrated Spatial Planning (ISP) as a governance and planning framework that links marine and terrestrial domains through a multi-level zoning structure operating from municipal to international scales. The approach explicitly incorporates climate change adaptation by aligning spatial planning instruments with marine climate drivers, hydrological processes, and environmental dynamics that shape coastal resilience.The methodology is applied to the Region of Murcia, Spain, a Mediterranean coastal system highly exposed to climate variability, sea level rise, and extreme runoff events. Despite the existence of multiple regulatory and strategic instruments, including urban plans, regional spatial law, basin-scale hydrological planning, climate strategies, and coastal management guidelines, planning remains fragmented across land and sea. The case study reveals critical gaps in the integration of climate projections, runoff and sediment dynamics, infrastructure planning, renewable energy deployment, and ecosystem-based adaptation, particularly in sensitive areas such as the Mar Menor lagoon.ISP addresses these challenges by establishing governance mechanisms that connect marine climate models, environmental dynamics, and spatial decision-making across administrative levels. The results demonstrate how ISP can improve coherence between climate adaptation strategies, ecosystem protection, and socio-economic development, offering a transferable framework for climate-informed coastal and marine spatial planning in vulnerable regions.

Abstract: We present a strengthened collider-facing extension of the uploaded Quantum Information Copy Time (QICT) program to compressed Higgsino searches at \( \sqrt{s}=13 \) TeV. The underlying QICT manuscript identifies its theorem-level core as a copy-time definition \( \tau_\text{copy} \), a Liouvillian-squared susceptibility \( \chi^{(2)}_Q \), and a conserved-charge speed-limit bound; the infrared and phenomenological sections are explicitly conditional closures. We therefore do not claim a theorem-level Higgsino prediction. Instead, we construct a fully explicit QICT-to-collider closure map, validate a detector-level surrogate against public CMS and ATLAS compressed-Higgsino reach anchors, perform a quantitative public-contour recast, propagate dominant surrogate uncertainties, and isolate a new branch-transition observable \( B_{\ell t} = N_{\ell t} / (N_{\ell t} + N_{\text{track}}) \). Within this strengthened framework, the residual post-public-limit search prior remains two-branched: an ultra-compressed branch near \( m_{\tilde{\chi}_1^\pm} \approx 200-240 \) GeV with \( \Delta m^\pm \sim 0.35-0.9 \) GeV, and a few-GeV branch near \( m_{\tilde{\chi}_1^\pm} \simeq 150-220 \) GeV with \( \Delta m \simeq 2-6 \) GeV. The manuscript is deliberately modest about logical status, but it is now quantitative, reproducible, and falsifiable.

Abstract: The study continues the authors' previous research on The Impact of CO₂ Laser Treatment on Kevlar^® KM2+ Fibres Fabric Surface Morphology, using direct laser surface texturing of polymers. SEM and Confocal microscopy image analysis of Kevlar^® KM2+ 440D and 600 are performed to analyze the results. In the course of the study, the surface topography of Kevlar^® KM2+ fabric is optimized by adjusting the continuous wave (CW) CO2 laser parameters so that it increases the surface roughness before graphene coating and resistance to yarn pulling out of the fabric without destroying the unique structure of Kevlar® KM2+ fibres. Experimental study measurement data indicate an increase in surface roughness by 50%, and a set of laser parameter variants has been obtained that allows increasing the yarn pulling out force of KM2+ woven fabric from the fabric in the range from 50% to 99%, compared to untreated fabric. The results obtained are potentially applicable for the production of composite materials against projectile fragmentation.

Abstract: Open-vocabulary 3D object detection (OV-3DOD) is crucial for real-world perception, yet existing monocular methods are often limited by predefined categories or heavy reliance on external 2D detectors. In this paper, we propose CLIP-Mono3D, an end-to-end one-stage transformer framework that directly integrates vision-language semantics into monocular 3D detection. By leveraging CLIP-derived semantic priors and grounding object queries in semantically salient regions, our model achieves robust zero-shot generalization to novel categories without requiring auxiliary 2D detectors. Furthermore, we introduce OV-KITTI, a large-scale benchmark extending KITTI with 40 new categories and over 7,000 annotated 3D bounding boxes. Extensive experiments on OV-KITTI, KITTI, and Argoverse demonstrate that CLIP-Mono3D achieves competitive performance in open-vocabulary scenarios.

Abstract: This work investigates the performance improvement of a four-probe ballistic rectifier on bilayer graphene (BLG) through the formation of an energy gap under a perpendicular electric field. For this purpose, exfoliated BLG was deposited on oxidized p+-Si and structured into an asymmetric cross junction with 90 nm wide channels. The junction consists of a straight voltage stem (contacts U,L) and slanted current injectors (contacts 1,2). The differential conductance of the stem, g_UL, as a function of back-gate bias, V_BG, reveals clear indications of energy gap formation and lateral depletion zones at the edges of the channel. The _DC characteristic of the ballistic rectifier, V_UL(I₁₂), shows an increase of the output voltage VUL with increasing V_BG. We attribute this to reduced diffuse scattering at the rough edges when the lateral depletion zones form smooth barriers.

Abstract: Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking inter-nal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle re-finement. This review synthesizes the current state of the art in harmonic NVH engineer-ing for electric drivetrains, focusing on the interactions between gear geometry, manufac-turing variability, electromechanical coupling, structural transfer, and human sound per-ception. Classical mechanisms of gear-mesh excitation are revisited together with emerg-ing EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The re-view further examines recent progress in predictive and data-driven approaches, includ-ing machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary sys-tem-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review pro-vides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

Abstract: Monkey pox virus has caused two major outbreaks in the past 4 years. Transmission of the virus is via close person-to-person contact, which suggests that the virus is of high virulence and pathogenicity. This work reports evidence gleaned from an in-house MATLAB open reading frame finder applied to the GenBank assembled genome of monkey pox virus. Using the data in the output file of the software that includes gene sequence, gene length, gene molecular weight, amino acid sequence of gene, protein molecular weight, and isoelectric point of protein, two significant results were observed. Firstly, profiling for genes of length more than 60 base pair reveal a total of 255 candidate genes, of which more than 200 are of protein length between 20 to 80 plus amino acids. Secondly, plotting the protein isoelectric point and molecular weight in a theoretical 2D protein gel plot shows that there are clusters of viral proteins of different molecular weight at different pH values from pH 4 to 12. In particular, there is a large cluster of proteins between pH 8 and 10, which suggests that alkaline blood pH may promote monkey pox virus virulence. However, absence of many proteins in the pH range of around 5, points to a potential therapeutic window where modulation and control of blood pH at 5, may provide symptomatic relief for the patient, while awaiting the patient’s immune response to degrade the viral particles in the blood.

Abstract: Massera and Schaffer [\textit{Ann. Math. (2), 1958}] derived a breakthrough upper bound for the Clarkson angle between two nonzero vectors in a normed linear space, which was later improved by Maligranda [\textit{Am. Math. Mon., 2006}]. Pecaric and Rajic [\textit{Math. Inequal. Appl., 2007}] extended Maligranda's inequality to finitely many nonzero vectors. We derive a non-Archimedean version of Massera-Schaffer-Maligranda-Pecaric-Rajic inequality.

Abstract: The digital age has expanded social media and online forums, allowing free expression for nearly 45% of the global population. Yet, it has also fueled online harassment, bullying, and harmful behaviors like hate speech and toxic comments across social networks, messaging apps, and gaming communities. Studies show 65% of parents notice hostile online behavior, and one-third of adolescents in mobile games experience bullying. A substantial volume of abusive content is generated and shared daily, not only on the surface web but also within dark web forums. Creators of abusive comments often employ specific words or coded phrases to evade detection and conceal their intentions. To address these challenges, we propose a hybrid deep learning model that integrates BERT, CNN, and LSTM architectures with a ReLU activation function to detect abusive language across multiple online platforms, including YouTube comments, online forum discussions, and dark web posts. The model demonstrates strong performance on a diverse and imbalanced dataset containing 77,620 abusive and 272,214 non-abusive text samples (ratio 1:3.5), achieving approximately 99% across evaluation metrics such as Precision, Recall, Accuracy, F1-score, and AUC. This approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content even in highly skewed datasets, as encountered in real-world scenarios.

Abstract: Fostering teachers’ professional agency is central to promoting relevant chemistry learning, as it enables future teachers to design accurate, evidence-informed and meaningful learning in a rapidly changing scientific landscape. This article summarises 25 years of evidence-based chemistry teacher education (EBTE) within the LUMA co design ecosystem. Situated in the Department of Chemistry, the Chemistry Teacher Education Unit collaborates closely with scientists and co designs pedagogical content knowledge (PCK) and Technological Pedagogical Content Knowledge (TPASK) courses that are both scientifically up to date and pedagogically effective. Developing strong PCK grounded in chemistry research, chemistry education research and classroom practice, strengthens scientific literacy, enabling teachers to translate often complex chemical ideas into accessible and relevant forms for learners. The EBTE model connects theory and practice, supports cross boundary collaboration and prepares research-oriented designer teachers for the demands of modern science, sustainability, digitalisation and AI. Co design within the co-design-based research (CoDBR) framework enhances teachers’ professional, relational and epistemic agency by enabling research informed development and fostering close collaboration with chemists and societal partners through LUMAlab Gadolin. Agency driven co design within the LUMA ecosystem can build the capacity of student teachers, practising teachers and students to engage confidently with contemporary chemistry, and contribute to a scientifically literate and sustainable future.

Abstract: Introduction: Propofol is one of the most commonly used intravenous anesthetics worldwide and is considered safe for all age groups. However, there have been reports that propofol can induce severe atrioventricular block in humans, and several studies have shown that propofol hinders or prevents the inducibility of arrhythmias during electrophysiological studies (EPS) and radiofrequency (RF) ablation. Objectives: Verify whether propofol prevents or hinders the inducibility of arrhythmias during EPS and RF ablation procedures in children with Wolff-Parkinson-White Syndrome. Methods:We conducted a retrospective observational cohort study including 45 pediatric patients aged 0–18 years. Patients were identified through a review and analysis of a database of individuals with Wolff–Parkinson–White syndrome who were referred for electrophysiological study and/or radiofrequency ablation at the Electrophysiology Laboratory of the Institute of Cardiology (IC/FUC) in Porto Alegre over the past five years (2019-2024). Patients with prior ablation, structural heart disease, or ongoing antiarrhythmic therapy were excluded. The patients were divided into two groups and designated as group S (who received sedation) or group G (who received general anesthesia). Sedation (group S) was performed with midazolam (0.08–0.2 mg/kg), fentanyl (0.1–0.2 μcg/kg), and propofol 50–60 µg/kg/min in continuous infusion. General anesthesia (group G), in turn, was performed with sevoflurane at an average dose of 2% (1 MAC according to age). Results: From 4,874 invasive electrophysiology procedures performed during the study period, 45 involved pediatric patients with WPW. The sedation group (n=29) had significantly older patients (14.6±2.5 vs 10.3±2.8 years, p<0.001) with higher weight (65.9±16.3 vs 41.2±7.8 kg, p<0.001) compared to the general anesthesia group (n=16). Arrhythmia was successfully induced in 15/29 (51.7%) patients in the sedation group compared to 13/16 (81.2%) in the general anesthesia group (p=0.062, Fisher's exact test). Although this difference did not reach statistical significance, it represents a clinically relevant 29.5% lower induction rate in the sedation group. Post-hoc power analysis revealed the study was underpowered (49.8%), suggesting a possible Type II error. Analysis of the "procedure room time" revealed a longer duration in the general anesthesia group (97.8±36.7 vs 67.8±24.4 minutes), and this difference was statistically significant (p = 0.002). Conclusions: This study compared propofol-based sedation with sevoflurane-based general anesthesia in pediatric WPW patients. While sedation with propofol did not show a statistically significant reduction in arrhythmia inducibility, there was a concerning trend toward lower induction rates (29.5% difference) that may be clinically relevant. The study's limited statistical power (49.8%) suggests these findings should be interpreted cautiously, and larger prospective studies are needed to definitively establish whether propofol affects arrhythmia inducibility in this population. Propofol remains a viable option for these procedures, but clinicians should be aware of the potential for reduced inducibility, particularly in cases where arrhythmia induction is critical for diagnosis and treatment.

Abstract: The global food supply chain (FSC) wastes nearly one-third of all food produced—over 2 billion tonnes annually—highlighting the need for technologies to reduce food loss and waste (FLW). Simultaneously, existing solutions are often evaluated in isolation, limiting cross-comparison and informed decision-making. This research develops an explainable decision support system (XDSS) that combines the Best–Worst Method (BWM) and Stochastic Multi-criteria Acceptability Analysis for Group Decision-Making (SMAA-2), providing probabilistic rankings that incorporate preference uncertainty. The framework assesses 100 technology-based strategies for reducing FLW across five criteria: geographic fit, product category, FSC stage, stakeholder role, and technology used. Each scenario undergoes 50,000 Monte Carlo simulations with a fixed seed of 12345 to enable reproducibility. Trade-offs are formalised through penalty functions and weight vectors, while hit-and-run sampling explores feasible weight regions. Example user queries demonstrate how qualitative preferences translate into rank-acceptability profiles: Query 1's maximum rank-1 acceptability is 62%, and Query 2's is 74%. The XDSS provides transparent, robust, and context-sensitive recommendations that support evidence-based technology adoption by SMEs and local authorities. By enabling reproducible and explainable prioritisation, the system advances UN’s Sustainable Development Goal (SDG) 12.3, which aims to reduce FLW along the FSC.