Abstract: This study examines the microspherical high-calcium fly ash (HCFA) and the high-strength binder material based on it by method of scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM-EDS). The composition of 568 individual microspheres of the initial HCFA was determined and presented as ternary diagrams CaO–Al2O3–SiO2 and CaO–FeO–SiO2. The binder specimens have a compressive strength of 24–90 MPa at a curing time of 3–300 days. Their strength is close to that of CEM I 42.5N cement specimens with a curing time of up to 28 days, but exceeds it with a curing time of up to 300 days. The SEM-EDS method showed that the predominant composition of hydration products is concentrated in the high-calcium region of the CaO–Al2O3–SiO2 diagram with a CaO content of 60–80%. The SiO2 content in them is 15-30%, and their composition includes 1–15% Al2O3 and 5–14% FeO. The SEM-EDS method allowed us to understand the transformation of calcium silicate glass microspheres into C-S-H gel, which is the main component of the strengthening matrix. The results contribute to the data for development of models for predicting the effect of HCFA on the properties of composite binders.

Abstract: This review aims to provide comprehensive and practical information on the once-nearly-forgotten but now resurgent roles and trends of autologous transplantation in leukemias. We seek to categorize when it is necessary as a first-line treatment (plasma cell leukemia) and to identify well-defined patient subgroups (such as certain types with intermediate prognosis in AML, APL second remission, etc.) in which autologous transplantation might be comparably or even slightly more effective than allogeneic transplantation, not only in frail patients. In some leukemias, such as CLL, autologous transplantation still does not play a role. Attempts to achieve anti-leukaemic effects in autologous settings have proven largely ineffective, but new approaches might be promising. Newer cell therapies (such as CAR-T) are significantly more effective, and the same applies to in vitro graft purging. However, this area has been investigated relatively recently in an innovative manner, using specific graft pretreatments that may also stimulate anti-leukemic immune responses in autologous cases.

Abstract: This study presents the first comprehensive molecular characterization of Escherichia coli producing extended-spectrum beta-lactamases (ESBL-Ec) in surface waters in Albania, focusing on the Shkumbini river. Antimicrobial resistance (AMR) in aquatic ecosystems poses a significant threat to public health, yet data from Albania remain scarce. Thirty water samples were collected from six locations near Elbasan between September 2022 and February 2024. Following the WHO Tricycle protocol, 52 ESBL-Ec isolates were recovered and characterized for antimicrobial susceptibility, biofilm formation, resistance genotypes and clonal relatedness via pulsed-field gel electrophoresis (PFGE). ESBL-Ec was detected in 80% of the samples analyzed, with 94.2% of the isolates classified as multidrug-resistant (MDR). High resistance frequencies were observed for ampicillin (98.1%) and cefotaxime (86.5%), while 7.7% of the isolates displayed colistin resistance associated with the mcr-3 gene. The bla_CTX-M-1 genotype was the most prevalent (57.7%), and almost half of the isolates harbored multiple ESBL genes. Phylogroup A (46.2%) predominated, followed by the high-risk extraintestinal lineages B2 (23.1%) and D (11.5%). PFGE revealed high genetic heterogeneity, with 51 distinct pulsotypes indicating multiple sources of contamination, such as untreated municipal, agricultural and industrial waste. Additionally, 55.8% of the isolates were capable of forming biofilms. These results highlight the critical role of the Shkumbini river as a reservoir for highly resistant pathogens and emphasize the urgent need for integrated environmental surveillance and improved wastewater management in Albania.

Abstract: Construction and demolition waste (CDW) is the largest single waste stream in the European Union by weight (~39% of all EU waste), yet the EU’s circular material use rate stood at only 12.2% in 2024 — less than half its 2030 target. Despite two decades of legislative ambition, the 70% recovery target under Directive 2008/98/EC has not been genuinely achieved: apparent compliance by most Member States conceals widespread downcycling and inconsistent reporting. This review identifies five persistent barrier domains — legal, technical, social, behavioural, and economic — with regulatory fragmentation and secondary material devaluation as the most structurally entrenched. A decisive paradigm shift is observed in recent research, from material characterisation towards systemic circularity, digital demolition frameworks, and governance. Emerging technologies — including AI-powered sorting, Building Information Modelling, Digital Twins, and Digital Product Passports — hold transformative potential, while Design for Deconstruction represents a critical upstream strategy the sector has yet to mainstream. The forthcoming EU Circular Economy Act will introduce legally binding obligations for Member States. The 2026 Strait of Hormuz energy crisis has reframed CDW from an environmental concern into a strategic industrial imperative: as virgin material costs surge, secondary CDW materials offer economic and geopolitical advantage. Future research must prioritise collaborative governance, longitudinal data, and scalable digital solutions.

Abstract: Accurate monitoring of soil salinization in arid oasis regions is crucial for agricultural sustainability and ecological security. However, existing deep learning-based approaches often suffer from insufficient utilization of multi-scale information and inadequate modelling of feature interactions, limiting their accuracy in retrieving complex salinity patterns. To address these limitations, this study proposes a scale-attention optimized hybrid deep learning model that integrates multi-scale 1D convolutional neural networks (1D-CNN), bidirectional gated recurrent units (Bi-GRU), and Transformer mechanisms. The model employs a multi-scale feature extraction module to capture remote sensing signals across different scales, a scale attention mechanism to adaptively weight the most informative features, and a Bi-GRU-Transformer module to explore complex sequential and global feature relationships. The proposed framework is applied to the oasis irrigation zone in Weili County, Xinjiang, using hyperspectral data from the ZY-1E satellite, topographic indices, and spectral-derived variables. Experimental results demonstrate that our method achieves a coefficient of determination (R²) of 0.952 and a root mean square error (RMSE) of 0.867 g·kg⁻¹ on the test set, outperforming conventional 1D-CNN, GRU-Transformer, and other benchmark models with improvements of 2.8% in R² and 18.9% in RMSE.

Abstract: Objectives: To descriptively evaluate the feasibility and clinical utility of TowardPi BO (4K ultra HD microscope integrated with a 400 kHz swept-source intraoperative optical coherence tomography (SS-iOCT) system) in managing various ophthalmic surgical conditions in a real-world setting. Methods: We analyzed surgical videos and data from 123 consecutive cases that underwent elective surgery with the assistance of this SS-iOCT system at Beijing Tongren Hospital between September 2, 2025, and February 10, 2026. Surgical cases were included based on specific diagnoses for which the SS-iOCT was found to be demonstrably useful. All videos were reviewed, and the utility of iOCT was discussed. Results: A total of 72 surgical cases were included, comprising 7 intraocular lens implantations with ciliary sulcus fixation, 19 macular holes, 3 cases of macular hole retinal detachment (MHRD), 4 cases of macular schisis with or without foveal detachment (MSRD), 12 cases of submacular hemorrhage, 20 cases of rhegmatogenous retinal detachment (RRD), and 7 intraocular mass lesions. The 400 kHz SS-iOCT significantly aided in surgical visualization, guided real-time decision-making, and prompted modifications in surgical techniques. Conclusions: This study presents the first report on 400 kHz SS-iOCT application in intraocular tumors. From routine surgical teaching to complex case management, SS-iOCT enhances surgical precision and facilitates real-time decision-making, ultimately contributing to improved surgical outcomes.

Abstract: The valorization of agro-industrial by-products through sustainable extraction of bio-compounds is a key challenge within circular economy and clean-processing frameworks, as large volumes of tomato and artichoke residues are generated by the food industry. This study evaluated the impact of non-thermal technologies on the recovery of biocompounds from tomato peels and blanched artichoke bracts using single green solvents instead of solvent mixtures. Ultrasound-assisted extraction (sonication), high-pressure processing (pressurization), and dual processing (pressurization + sonication) were compared with conventional extraction. Ethanol was used for lycopene extraction, while water was employed for inulin-type fructans recovery. Lycopene, total phenolic content, antioxidant activity, and inulin-type fructans were quantified. Non-thermal treatments significantly influenced extraction yields (p < 0.05). The dual processing provided the highest lycopene and inulin-type fructans contents (1440.09 ± 0.71 µg/g DW and 5.17 ± 0.51 g/100 g DW, respectively) and enhanced antioxidant activity in tomato peels and blanched artichoke bracts (25.50 ± 0.20% and 66.11 ± 2.03%), as well as phenolic co-extraction (1783.2 ± 215.3 μg GAE/g DW and 27.68 ± 1.29 mg GAE/g DW) outperforming individual technologies and conventional extraction. Compared with the conventional process, dual processing improved the extraction yields of lycopene (20.60 ± 0.44%) and inulin (26.40 ± 13.95%). The findings prove that non-thermal processes, particularly when combined, intensify mass transfer and enable efficient extraction using green solvents, offering a sustainable strategy for recovering bioactive compounds from tomato and artichoke by-products.

Abstract: Urban planners need continuous, scalable methods to evaluate pedestrian Level of Service (LOS). Static and locally calibrated approaches fail to capture the dynamic, network-wide, and context-dependent nature of pedestrian activity. While traditional LOS uses fixed density thresholds and data-driven models predict continuous flows, neither supports cross-city analysis due to context-specific assumptions. This study introduces a transferable analytical framework for predicting pedestrian LOS using large scale urban sensor data that captures both recurrent temporal demand patterns and spatial dependencies within street networks. The framework is evaluated using pedestrian sensor data from three cities Melbourne, Dublin, and Zurich, which represent diverse geometries, demand profiles, and sensing infrastructures. Results show strong in-domain Melbourne performance (accuracy 79.7%; Acc±1 99.1%) and effective cross-city generalization. Few-shot fine-tuning with only 5% labeled target-city data recovers 95–99% of in-domain performance, demonstrating practical scalability. KernelSHAP explainability reveals short-term temporal lag features universally dominate predictions, while spatial/contextual factors exhibit city-specific influence tied to local morphology. These findings demonstrate transferable GeoAI methods can support real-time pedestrian congestion monitoring and evidence-based public-space management, offering planners a scalable decision-support tool to enhance walkability, safety, and equitable access to high-quality public spaces in contemporary cities.

Abstract: Background: Hypoxic-ischaemic injury (HI) is a major contributor to neurological deficits following stroke. Understanding the mechanisms of neuronal death or survival in response to oxygen and nutrient deprivation is essential to fully comprehend the pathogenesis of diseases and disorders that are associated with HI. Aim: The aim of this study was to develop a robust in vitro model of ischaemic stroke, as well as serve as a new in vitro tool for initial screening of potential therapeutics and identification of diagnostic markers of brain hypoxic injury. Methods: This study details and validates a comprehensive protocol for modelling HI using differentiated SH-SY5Y neuroblastoma cells (Neuron-Like Cells, NLCs). First, we optimized the differentiation process and confirmed the maturity and purity of NLCs via standard molecular markers. The NLCs exhibited functional excitotoxicity, demonstrating a graded cell death response to N-methyl-D-aspartate (NMDA), validating their functional application. To simulate HI, we initially optimized the oxygen-glucose deprivation (OGD) treatment using graded concentrations of CoCl2 (0.125mM to 2mM) in glucose-free media. NLCs were then subjected to the refined OGD protocol (1mM CoCl2 in glucose-free media) for 3 hours, followed by various periods of reoxygenation (1h, 3h, 6h, 12h, 18h, and 24h). Result: RNA sequencing revealed a distinct temporal transcriptional response to HI. Injury-associated genes, including heat shock proteins and stress markers, were significantly upregulated at 3 hours of reoxygenation, peaked at 6 hours, and declined thereafter, remaining above baseline at 24 hours. Upstream regulator analysis identified IL-1β, TNF-α, and HIF-1α as key drivers during OGD, with additional regulators emerging during reoxygenation. TNF-α and β-oestradiol were consistently identified across time points, while TGF-β1 and NTRK1 became prominent during peak injury and later phases. Analysis of secreted factors showed increased release of inflammatory (TNF-α) and neurotrophic (β-NGF, BDNF, VEGF) mediators with reoxygenation, while maximal cell death occurred at 24 hours. Conclusion: This study identifies a transient, time-dependent transcriptional cascade following hypoxic–ischaemic injury, highlighting a critical window for early neuronal response. The model provides a reproducible platform for studying neuronal injury and recovery, and identifies known (TNF-α, IL-β, and HIF-1α), context-specific (NTRK1 and TGF-β) and novel (β-oestradiol) regulators of the injury response with potential relevance for therapeutic targeting.

Abstract: The rapid growth of electromobility is increasing pressure on the adequacy of charging infrastructure deployed along major transport corridors. This study presents a simulation-based framework for assessing the operational performance of electric vehicle charging infrastructure along the S19 Rzeszów–Barwinek section, a 90 km corridor forming part of the TEN-T and Via Carpathia networks. The methodology combines microscopic traffic simulation in PTV Vissim with probabilistic charging-demand modeling for passenger cars and heavy-duty vehicles, enabling the analysis of infrastructure utilization, queue formation, and unmet charging demand under realistic corridor conditions. Three electric vehicle penetration scenarios were examined: 10%, 25%, and 45% of the traffic stream. The results show that the charging system remains stable under the 10% scenario, begins to experience local overload and recurring congestion at 25%, and reaches structural insufficiency at 45%, where utilization exceeds 100% and unmet demand rises markedly. A key finding is that heavy-duty electric vehicles constitute the dominant operational bottleneck due to longer charging times, higher energy requirements, and the limited number of dedicated charging points. An additional expansion variant indicates that increasing the number of heavy-duty charging points can substantially improve system performance and restore a safer utilization range. The study demonstrates that minimum regulatory compliance should be treated as a baseline rather than a sufficient planning target and that dynamic, scenario-based simulation offers an effective decision-support tool for the adaptive development of corridor charging infrastructure.

Abstract: This paper examines the evolving role of central banks in supporting the transition to a low-carbon economy within the framework of sustainable development objectives. While central banks are not directly responsible for climate policy, climate-related physical and transition risks increasingly affect their core mandates, including price stability, financial stability, and the resilience of the banking system. The study highlights the growing relevance of integrating Sustainable Development Goals (SDGs) into central banks’ analytical frameworks as a means of linking macroeconomic and financial dynamics with environ-mental and social transformations. Drawing on key institutional sources, including Eurostat’s SDG monitoring reports, NGFS Phase IV climate scenarios, and ECB and ESRB analyses, the paper explores how climate risks can be quantified and incorporated into monetary policy and financial stability assessments. It emphasizes the role of standardized climate scenarios and stress testing in evaluating both transition and physical risks, as well as the uneven distribution of these risks across sectors and regions. Furthermore, the paper discusses the ECB’s “Climate and nature 2024–2025” plan as a concrete step toward operationalizing climate considerations in monetary policy, supervision, and portfolio management. By combining SDG indicators with climate scenarios and stress test results, the research identifies potential synergies and trade-offs between sustainability objectives and central bank mandates. The findings contribute to a conceptual and empirical framework for assessing how central banks can support the green transition while maintaining macroeconomic and financial stability.

Abstract: Candida albicans is an opportunistic fungal pathogen of clinical relevance, and plant-derived antifungal agents have attracted interest because of rising resistance to conventional drugs. This study evaluated the in vitro antifungal activity of Mespilodaphne quixos (Lam.) Rohwer essential oil (EO) against C. albicans, modelled its concentration-dependent response using a one-factor response surface methodology (RSM) design, and investigated the interactions of its constituents with selected fungal targets by molecular docking. Freshly collected leaves were subjected to steam distillation, and the EO was characterised by GC/MS. Antifungal activity was determined using the Kirby–Bauer disc diffusion method. A one-factor RSM design was applied to model inhibition halo diameter as a function of EO concentration. Besides, 22 identified compounds were docked against 14-α-demethylase, Δ(14)-sterol reductase, and exo-β-(1,3)-glucanase. The EO was mainly composed of (E)-cinnamaldehyde (47.2%), caryophyllene (10.8%), and α-humulene (5.37%). The EO reached an inhibitory capacity of 87.3% relative to ketoconazole. The quadratic model showed good predictive performance. Molecular docking revealed favourable affinities for several sesquiterpenes: α-copaene showed the best interaction profile against 14-α-demethylase and Δ(14)-sterol reductase, whereas α-guaiene and spathulenol performed best against exo-β-(1,3)-glucanase. These findings provide preliminary in vitro and in silico evidence supporting the antifungal activity of M. quixos EO.

Abstract: Historically, the study of oral fungal species was limited by the inability to cultivate most of them. However, advances in metagenomic techniques have enabled the direct identification of microbial genomes from human samples, markedly broadening our understanding of the oral mycobiome. This literature review aims to analyze the available scientific evidence on the composition and dynamics of the oral mycobiome, as well as its influence on the development of local pathological conditions. The oral mycobiome is highly diverse, with emphasis on genus Candida, followed by Malassezia, Aspergillus, Saccharomyces, Cladosporium, Trichosporon and Geotrichum. Candida albicans remains the most frequently identified species in both health and diseases state. However, individuals with oral candidiasis present a higher detection of Candida dubliniensis, Candida parapsilosis, Pichia kudriavzevii, Antrodiella micra and Cladosporium sphaerospermum. In dental caries, C. albicans and C. dubliniensis are associated with advanced lesions, whereas Malassezia and Rhodotorula may exert protective effects against cariogenic bacteria. In periodontitis, an increase in yeast-bacteria interactions is observed. Additionally, C. albicans has been implicated in oral carcinogenesis through multiple mechanisms. These findings highlight the need for a deeper understanding of the oral mycobiome to enable early detection of oral diseases and the development of therapeutic approaches.

Abstract: This study examines the dynamic relationship between deposits, credit and sales across Ecuador’s provinces over the period 2019-2025 using a Panel VAR model estimated by two-step GMM. Sales declared to the Internal Revenue Service are employed as a high-frequency administrative indicator of provincial economic activity. The results are consistent with a predominantly supply-leading structure, in which deposits and credit exhibit predictive capacity over provincial sales, with no robust evidence in the reverse direction. The speed of transmission differs between the two financial channels. De-posits affect sales with a one-period lag, whereas credit does so with two, suggesting that liquidity is channelled toward commercial activity more immediately than credit financing. During the pandemic period, an increase in deposits, a contraction in credit, and a decline in sales are observed. The study provides subnational evidence for a dol-larized Latin American economy and covers a recent period marked by an extraordinary shock. The findings indicate that the relevance of financial intermediation for territorial economic activity depends not only on the direction of the linkage but also on the dif-ferentiated speed of its components.

Abstract: Diabetes mellitus is a complex metabolic disease characterised by chronic hypoglycemia, which results from the insulin secretion, insulin resistance or both. In recent times, evidence has proven the significant role of epigenetic modulations, particularly DNA methylation and histone modifications, in the progression and long-term persistence of diabetes. These modifications influence the gene expression associated with insulin signaling, glucose metabolism, and β-cell function and inflammatory pathways, which result in the contribution of metabolic dysfunction. Plant-derived polyphenols like curcumin and rutin exhibit antioxidant, anti-inflammatory and antidiabetic properties. Moreover, these compounds have remarkable potential to modulate the epigenetic mechanisms that ultimately lead to beneficial changes in gene expression. This review highlights the epigenetic mechanisms through which curcumin and rutin exert their therapeutic potential in diabetes mellitus, identifying the challenges in ongoing research and future scope in this field.

Abstract: Spintronic crossbar arrays are emerging as a powerful hardware platform for energy-efficient computing. Unlike conventional digital processors that shuttle data between memory and processing units, these arrays perform computation directly where data is stored, a concept known as in-memory computing. This report explains, from the ground up, what spintronic crossbars are, how they operate, and the different types currently available or under development. We cover both binary (single-level) and analog (multi-level) devices, their input/output characteristics, and the physical principles that make them uniquely suited for matrix operations. The figures illustrate the architecture, switching mechanisms, and the transition from binary to multi-level behavior. This foundation is essential for understanding advanced applications such as parallel photovoltaic MPPT and neuromorphic computing.

Abstract: Wearable smart inhalers represent a transformative approach to chronic respiratory management in advanced lung cancer, integrating sensor‑based monitoring, real‑time connectivity, and patient‑centric feedback loops. These devices track inhalation technique, dosing frequency, and timing, then transmit encrypted data to cloud‑based platforms for analysis by clinicians and AI‑driven algorithms. For advanced‑stage lung cancer patients, this enables continuous surveillance of bronchospasm, dyspnoea, and medication adherence outside the intensive care setting, thereby reducing uncontrolled exacerbations and unplanned hospitalizations. Smart inhalers also support tele‑follow‑ups and personalized adjustment of bronchodilator or palliative regimens based on individual patterns of use and symptom burden. When embedded within an end‑to‑end care pathway from intensive care discharge to home care these wearables facilitate seamless transitions, improve self‑management, and empower multidisciplinary teams with objective, longitudinal respiratory data. This article explores design principles, clinical integration, and emerging digital‑health frameworks that position wearable smart inhalers as a cornerstone of modern, technology‑driven chronic respiratory support in advanced lung cancer.

Abstract: Background: Work disability in fibromyalgia is only partially explained by symptom severity, suggesting a relevant contribution of cognitive–behavioral mechanisms. Objective: This study aimed to determine whether kinesiophobia is associated with fibromyalgia impact and work-related disability, and to assess whether pain catastrophizing mediates these relationships within a hierarchical biopsychosocial framework. Methods: This cross-sectional study included 2,096 women with fibromyalgia recruited through a nationwide online survey. Participants completed validated instruments assessing fibromyalgia impact (FIQ), pain catastrophizing (PCS), depressive symptoms (PHQ-9), central sensitization (CSI), and kinesiophobia (Tampa Scale). Pain-related work disability was defined using the Graded Chronic Pain Scale–Revised (GCPS-R). Hierarchical logistic regression models identified factors independently associated with work disability. Mediation was tested using bootstrapped analyses (5,000 resamples). Results: Kinesiophobia demonstrated a robust independent association with work disability (OR 1.03; 95% CI 1.02–1.05) after adjustment for sociodemographic factors, clinical pain phenotype, systemic burden, pain severity, psychocognitive load, and medication burden. Other relevant contributors included pain severity (OR 1.96; 95% CI 1.70–2.27), psychocognitive burden (OR 1.35; 95% CI 1.15–1.58), use of benzodiazepines (OR 1.74; 95% CI 1.33–2.28), and opioid use (OR 1.29; 95% CI 1.06–1.56). Mediation analysis indicated a significant indirect effect of kinesiophobia on work disability through pain catastrophizing (β = 0.131; 95% CI 0.078–0.188). Conclusion: Kinesiophobia is a proximal determinant of work disability in fibromyalgia, exerting direct and cognitively mediated effects through pain catastrophizing, reinforcing the fear-avoidance framework and the need for psychologically informed rehabilitation.

Abstract: Existing research on quality gain-loss functions predominantly focuses on single variables or separable quality characteristics, overlooking the correlations among multiple quality attributes and the complexity of spatiotemporal factors. This paper employs the Matérn kernel to construct spatiotemporal interaction terms, incorporates Kalman filtering and smoothing algorithms to enhance computational efficiency, and establishes joint gain-loss weights using the signal-to-noise ratio method. Consequently, a multivariate multidimensional quality gain-loss function model based on the Non-Separable Gaussian Process (NSGP) is developed. The NSGP model is applied to simulation cases and dam concrete production scenarios. Comparative optimization with machine learning methods such as Gaussian processes and linear regression validates the robustness of the NSGP model. Crucially, it eliminates the computational requirement for determining covariance separability, thereby reducing computational costs. This provides robust case support for quality management in hydraulic concrete construction.

Abstract: Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for AIS-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. In the proposed framework, TCN is used to extract local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism is introduced to adaptively emphasize informative representations, and BiLSTM is employed to model bidirectional contextual dependencies in vessel motion sequences. On this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method outperforms several benchmark models in terms of mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and coefficient of determination (R2). The results demonstrate that the proposed framework can effectively improve vessel speed prediction accuracy and provide practical support for proactive maritime supervision and nearshore safety management.