Search Results (17,376)

The sustainable stabilization of clayey soils has become a critical strategy for improving their mechanical performance while reducing environmental impact. This study compares two distinct stabilization systems applied to the same low-plasticity clay (CL) from Cartagena de Indias, Colombia: (i) lime catalyzed with sodium chloride (NaCl) and (ii) xanthan gum (XG) reinforced with polypropylene fibers (PPF). A series of laboratory tests was performed to evaluate the unconfined compressive strength (q_u) and small-strain stiffness (G_o) of both systems under controlled compaction and curing conditions. The lime–NaCl system demonstrated accelerated early-age strength and stiffness development, reaching q_u values above 2.5 MPa and G_o exceeding 10 GPa after 28 days of curing, mainly attributed to enhanced pozzolanic reactions catalyzed by NaCl. Conversely, the XG–PPF blends exhibited progressive improvements in mechanical performance, achieving notable gains after 90 days due to the polymeric bonding of XG and the fiber–matrix reinforcement that enhanced ductility and post-peak behavior. When normalized through the porosity–binder index, both systems exhibited power-law trends, with the lime–NaCl mixtures displaying higher exponents indicative of cementation-controlled behavior, while the XG–PPF mixtures showed lower exponents consistent with interparticle bonding and network formation. These results highlight the complementary mechanisms of chemical and biopolymeric stabilization, providing insights into the selection of sustainable binders tailored to specific design requirements in tropical clays. This research demonstrated that the implementation of machine learning models enhanced the fitting accuracy of the two soil stabilization methods when compared with traditional mathematical regression models commonly used in geotechnical engineering. Among the tested approaches, the neural network and Gaussian process regression models exhibited the best performance, achieving R² values ranging from 0.917 to 0.980 during the validation stage. Full article

Background/Objectives: This study aimed to determine the potential roles of miR-4295, miR-4720-5p, miR-4773, miR-6831-5p, and miR-7161-5p in colorectal cancer by evaluating their expression levels in matched tumor and adjacent non-tumor tissues from 86 patients. Methods: A total of 172 samples were analyzed, and the associations between miRNA expression levels and clinicopathological characteristics were assessed, along with correlations among the miRNAs. Functional enrichment analyses, including GO and KEGG pathway evaluations, were performed using DIANA-mirPath v.3 to characterize biological processes and signaling pathways associated with the predicted target genes. Results: The results showed that miR-4295 and miR-4720-5p were significantly upregulated in tumor tissues, while miR-4773 and miR-6831-5p were significantly downregulated (p < 0.001). No significant difference in miR-7161-5p expression was observed between tumor and non-tumor tissues (p = 0.877). KEGG analysis indicated that miR-4295, miR-4720-5p, miR-4773, and miR-6831-5p regulate genes involved in the TGF-β, mTOR, ErbB, FoxO, and endocytosis signaling pathways. Conclusions: These findings suggest that miR-4295 and miR-4720-5p may have oncogenic functions, while miR-4773 and miR-6831-5p may have tumor-suppressing functions, and that this relationship may contribute to the development of colorectal cancer. Full article

Insecticide resistance in Aedes aegypti (Linnaeus, 1762), the primary vector of several arboviruses, threatens vector control efficacy and motivates evaluation of current and candidate public health insecticides, such as imidacloprid and broflanilide, and their molecular impacts. Here, we used RNA sequencing (RNA-seq) to characterize the transcriptomic response to one-hour acute exposure to an operational partial-mortality concentration (<50%) of imidacloprid and broflanilide in two Ae. aegypti strains: a field-derived, pyrethroid-resistant population from San Nicolás and a susceptible laboratory strain (New Orleans). Adults were exposed for 1 h to partial-mortality concentration (<50%) doses of each insecticide or acetone control, and differential gene expression and Gene Ontology (GO) enrichment were assessed with DESeq2-based workflows. We detected pronounced baseline transcriptomic differences between strains and extensive activation of gene expression after insecticide exposure, with a strong bias toward up-regulation. A shared transcriptional core involving proteolysis, transmembrane transport, detoxification pathways, and structural remodeling of the cuticle and cytoskeleton was identified across contrasts. Despite these common elements, broflanilide elicited largely conserved early responses between strains, whereas imidacloprid amplified pre-existing divergence and produced marked population-specific transcriptional signatures. These findings suggest greater transcriptional changes in the field-derived strain, particularly in response to imidacloprid, and highlight the importance of integrating population-specific molecular information when designing insecticide rotation schemes and resistance management strategies targeting Ae. aegypti. Full article

This study introduces an electrochemical sensing platform based on bimetallic CoAg/rGO and CuAg/rGO nanocomposites for the detection of 1,3-dinitrobenzene (DNB), a highly toxic nitroaromatic compound commonly encountered in industrial effluents and contaminated water systems. The prepared nanocomposites were characterized using SEM, TEM, AFM, XPS, and electrochemical techniques, providing detailed insight into their structural, morphological, and surface properties relevant to electrochemical sensing. The electrochemical behavior of DNB was investigated in phosphate buffer solutions using cyclic voltammetry under optimized experimental conditions. Both CoAg/rGO and CuAg/rGO electrodes exhibited pronounced electrocatalytic activity towards the reduction in DNB, characterized by well-defined reduction peaks. The developed sensors exhibited good analytical performance, with limits of detection of 2.21 µM and 2.47 µM for the CuAg/rGO and CoAg/rGO electrodes, respectively, both showing linear responses in the concentration range of 5–50 µM. Moreover, a clear response to DNB was obtained in the presence of phenols as interferents as well as in spiked real water samples. The integration of characterization results with electrochemical measurements and validation in real water samples supports process-oriented research in environmental monitoring and electrochemical process control. These results confirm that bimetallic rGO-based nanocomposites represent efficient and cost-effective electrode materials for the electrochemical detection of 1,3-dinitrobenzene. Full article

Enhancing traffic flow stability is a critical approach for achieving energy conservation and emission reduction in road transportation. While existing cooperative car-following strategies for connected and automated vehicles (CAVs) are effective, their heavy reliance on reliable Vehicle-to-Everything (V2X) communication limits practical deployment. This study proposes an energy-optimal car-following model for CAVs, introducing a regulation term based on the predicted optimal speed difference. Rather than directly using predicted kinematic variables, this mechanism adjusts acceleration based on the difference in optimal velocity between predicted and current headways. This leverages the inherent filtering of the optimal velocity function to ensure smooth control. Linear and nonlinear stability analysis confirm the model’s effectiveness in suppressing traffic disturbances and suppression of stop-and-go wave propagation, thereby laying the theoretical foundation for smoother traffic flow and the resulting reductions in energy consumption and emissions. Simulations validate the theoretical findings. Compared to the classical Full Velocity Difference (FVD) model, the proposed model achieves significant reductions in energy consumption (38.82%), CO₂ emissions (39.41%), and NO_x emissions (83.46%). The model also reduces rear-end collision risks, ensuring higher safety. These findings indicate that the proposed ego-vehicle predictive framework provides a communication-independent and practically viable approach for improving the energy efficiency and stability of CAV traffic flow. Full article

High-throughput sequencing generally results in a list of genes. Which functional groups of genes among the DEGs are meaningful underlying factors to the differential biological/biomedical conditions under investigation? The process to find answers to this question can be called biological functional class enrichment analysis (FunCEA). R is a robust platform for FunCEA due to its accessibility by general users and availability of well-developed R packages for enrichment analysis and visualization, as well as for knowledge databases. Bench scientists in biomedical sciences need accessible and easy-to-understand protocols for FunCEA. This R tutorial provides detailed R scripts or command lines for FunCEA, as well as for data processing and visualization of the enrichment results. It keeps bench scientists in mind and provides supportive and apprehensible descriptions of the R scripts for each task (enrichment analysis, enrichment data processing, and visualization). It describes detailed procedures for the two popular FunCEA methods, the so-called over-representation analysis (ORA) and functional class scoring (FCS). The introduced FunCEA here uses three basic knowledge databases: gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome. R codes for various visualizations (dot plot, term-gene network plot, enrichment map plot, ridge plot, and GSEA plot) are presented and annotated. Since all analyses are conducted in R, no commercial software is needed, yet clusterProfiler can directly access the latest KEGG knowledge database. Full article

Background/Objectives: Diabetes is classified into type 1 and type 2 diabetes. Type 1 diabetes is an autoimmune disease that develops in young people. While several factors are known to worsen type 1 diabetes, the effects of blue light remain unclear. This study aimed to explore this literature gap. Methods: In this study, we examined the effects of blue light exposure on diabetes using streptozotocin-induced type 1 diabetic mice. Furthermore, we used go91phox^-/- mice to investigate the cause of blue light-induced diabetes exacerbation. Results: Blue light exposure exacerbated type 1 diabetes and activated the gp91phox/reactive oxygen species (ROS)/complement component 1/wingless-type MMTV integration site family, member 5A (Wnt5a)/α-catenin or peroxisome proliferator-activated receptor γ pathway in the liver and the gp91phox/ROS/DKK1/Wnt3a/α-catenin pathway in the pancreas, resulting in decreased β-catenin expression. These results indicated that blue light exacerbates type 1 diabetes by activating Wnt5a in the liver and decreasing Wnt3a in the pancreas. The use of gp91phox^-/- was shown to cancel the worsening of diabetic symptoms caused by blue light. Conclusions: These results suggest that type 1 diabetes worsens with blue light and that this is due to the activation of gp91phox by blue light. Full article

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are two processes that occur during the operation of the cathodic electrode in Zn–Air batteries, which enable the integration of alternative energy sources into electrical energy distribution systems. Transition metal oxides, such as perovskites based on LaNiO₃, are promising electrocatalysts for the ORR and OER in alkaline medium due to their versatile structure, allowing for the substitution of certain atoms with dopants, which enhances the catalytic activity for both reactions. This work reports an electrochemical study of the catalytic activity toward ORR and OER of perovskite catalysts (LaNiO3 doped with transition metals (Fe, Mn, or Pd)) in the presence of carbon-based materials as supports (multiwalled carbon nanotubes (MWCNT), graphene oxide nanosheets (GO), and graphitic carbon (C)). The results revealed interesting catalytic properties in both reactions, particularly La(Ni_0.9Pd_0.1)O₃/MWCNT, which showed an ORR activation potential of 0.87 V vs. RHE, comparable to that of the commercial Pt/C catalyst (0.99 V vs. RHE), while the overpotential for OER was lower than that of the Pt/C catalyst (1.68 V vs. RHE for La(Ni_0.9Pd_0.1)O₃/MWCNT and 1.79 V vs. RHE for the commercial Pt/C). Full article

ywqN encodes a protein with an unassigned function that shares partial 3D homology with B. subtilis YhdA, Pseudomonas putida ChrR, and Escherichia coli YieF, which are NADP(H)/FMN-dependent oxidoreductases that catalyze the reduction of diverse chemical pollutants, including Cr(VI). Here, we report that a recombinant His₆-YwqN protein displays marginal chromate reductase activity but is capable of reducing synthetic azo dyes. Remarkably, His₆-YwqN exhibits a potent quinone reductase activity, catalyzing the reduction of menadione (MD) and 1,4-naphthoquinone (NQ). The individual and combined roles of YwqN and YhdA in protecting B. subtilis from ROS-promoting agents were further tested. Sensitization to the oxidizing agent H₂O₂ required the simultaneous loss of both YwqN and YhdA. In contrast, strains deficient in ywqN, either alone or in combination with yhdA, exhibited similar but higher susceptibilities to the superoxide-generating agent MD compared with the WT strain. These results indicate that YwqN and YhdA contribute to protection against the deleterious effects of ROS in B. subtilis. Further results revealed that while YwqN, but not YhdA, prevented MD-induced mutagenesis, both proteins synergistically prevented Rif^R mutations induced by H₂O₂. Furthermore, overexpression of YwqN suppressed the hypermutagenesis phenotype of a B. subtilis strain deficient in the prevention/repair oxidized guanine (GO) system, which is prone to accumulate 8-oxoGs. In summary, YwqN counteracts the cytotoxic and genotoxic effects promoted by ROS in B. subtilis and represents a potential tool for the remediation of soils and effluents contaminated with carcinogenic azo dyes. Full article

Water pollution from industrial dyes is a critical challenge due to the resistance of these types of compounds to degradation and potentially harmful effects on living organisms and human health. In this study, the electrochemical degradation of methylene blue (MB) was investigated using ink-based copper foam electrodes with reduced graphene oxide (rGO), antimony trioxide (Sb₂O₃), and rGO/Sb₂O₃ composites. The materials used to synthesize the electrodes were characterized by X-ray diffraction (XRD), which showed the successful synthesis of GO, rGO, and the Sb₂O₃-rGO composite. Additionally, the synthesized electrodes were examined using SEM. The MB degradation was studied using kinetic behavior and removal efficiency at pH levels from 3 through 6, monitored using UV-Vis spectroscopy. The electrocatalytic degradation was studied using sodium sulfate as the electrolyte across a pH range of 3 to 8. All electrodes investigated were determined to follow first-order kinetics. The Sb₂O₃-rGO composite showed the highest rate constants of MB degradation at pH 7 and 8, with rate constants of 0.0160 and 0.0159 min⁻¹, respectively. At the same time, the rGO ink-based electrode worked fastest at pH 3 and pH 4 with rate constants of 0.0178 and 0.0158 min⁻¹, respectively. The Sb₂O₃ also works best at pH 3 and 4 with rate constants of 0.0151 and 0.0152 min⁻¹. SEM analysis shows the composite electrode was more resilient to degradation than other materials. Full article

To mitigate the threat posed by accumulated gob water to underlying coal seams during multi-seam mining, this study investigates the mechanism of water inrush induced by repeated mining and its control through roof cutting pressure relief. The 31110 panel of the Holowan Coal Mine is taken as an engineering case, where the 3⁻¹ coal seam is threatened by gob water from the overlying 2⁻² coal seam. The mechanisms of interlayer rock mass damage accumulation, fracture interconnection, and water-conducting channel formation were systematically analyzed using a combination of theoretical analysis, numerical simulation, and field tests. The results indicate that the superimposed mining-induced failure zones of the upper and lower coal seams significantly exceed the interlayer spacing of 46.5 m. This condition promotes through-going damage of the interlayer strata and facilitates the downward migration of gob water. Without roof cutting, the main roof fractures toward the solid coal side of the 31110 auxiliary headgate, resulting in full connectivity of the overburden plastic zones and the formation of a continuous water-conducting channel. Roof cutting pressure relief, achieved by pre-inducing artificial weak planes, effectively guides roof fracturing toward the gob side, alleviates stress concentration on the solid coal side, and suppresses the expansion of interlayer damage. When the roof cutting height exceeds 35 m, plastic connectivity between the water-resisting coal pillar and the underlying mining-induced damage zone is interrupted, preserving the integrity of the key aquiclude. Field application of directional hydraulic fracturing roof cutting confirms the formation of continuous weakened fracture planes and controlled roof caving along the designed trajectory. The overburden caving angle increases from 70° to approximately 90°, effectively blocking water-conducting pathways and eliminating the risk of gob water inrush. These findings not only deepen the understanding of water inrush mechanisms under repeated mining disturbances but also establish a proactive fracture-regulation framework for gob water hazard control, providing broadly applicable design criteria and technical references for safe and efficient multi-seam mining in water-threatened coalfields. Full article

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) is a proposed dual-satellite mission to observe Ultra-High-Energy Cosmic Rays (UHECRs), increase the statistics at the highest energies, and observe Very-High-Energy Neutrinos (VHENs) following multi-messenger alerts of astrophysical transient events, such as gamma-ray bursts and gravitational wave events, throughout the universe. POEMMA–Balloon with radio (PBR) is a small-scale version of the POEMMA design, adapted to be flown as a payload on one of NASA’s suborbital Super Pressure Balloons (SPBs) circling over the Southern Ocean for more than 20 days after a launch from Wanaka, New Zealand. The main science objectives of PBR are: (1) to observe UHECRs via the fluorescence technique from suborbital space; (2) to observe horizontal high-altitude air showers (HAHAs) with energies above the cosmic ray knee (E > 3PeV) using optical and radio detection for the first time; and (3) to follow astrophysical event alerts in the search of VHENs. The PBR instrument consists of a 1.1 m aperture Schmidt telescope similar to the POEMMA design, with two cameras on its focal surface: a Fluorescence Camera (FC) and a Cherenkov Camera (CC). In addition, PBR has a Radio Instrument (RI) optimized for detecting EASs (covering the 60–660 Mhz range). The FC observes UHECR-induced EASs in the ultraviolet (UV) spectrum using an array of 9216-pixel Multi-Anode Photo-Multiplier Tubes (MAPMTs) imaged every 1 $\mathsf{\mu }$s. The CC uses a 2048-pixel Silicon Photo-Multiplier (SiPM) imager to observe cosmic-ray-induced HAHAs and search for neutrino-induced upward-going EASs. The CC covers a spectral range of 320–900 nm, with an integration time of 10 ns. This contribution provides an overview of PBR instruments and their current status. Full article

In previous studies, fire detection models that only differentiate between fire and smoke are presented. However, a high false detection rate occurs because of confusion between smoke and fog. In this study, a novel method is proposed to effectively distinguish between smoke and fog. A custom dataset is introduced for detecting fire, smoke, and fog, which offers a novel labeling technique to reduce the misclassification of smoke and fog. A new architecture is proposed that is aimed at enhancing the detection performance. The latest You Only Look Once version 11 (YOLOv11) is used to establish a performance baseline. Further research then focuses on improving this benchmark. To ensure accurate texture differentiation, the dataset is designed to exclude overlapping ground-truth boxes, enabling the trained model to determine object boundaries independently, which is a design approach not found in previous studies. The model design aims to maximize performance and cost efficiency. The performance is improved by adding an image pyramid layer to the existing model to improve large-object detection. Cost efficiency is improved by designing a new module, C5Go, to mitigate the additional computational load introduced by the added pyramid level. Comparative experiments on the proposed custom fire dataset demonstrate that the proposed model improves the detection performance while keeping the additional computational overhead modest. The experimental results show that the proposed model achieves a 12.86% improvement in smoke detection performance compared with YOLOv11 and attains an overall mean average precision (mAP)@50 score of 0.906, reflecting superior performance. The contributions of this work are as follows: (i) we design a three-class dataset comprising fire, smoke, and fog so as not to cause false detections; (ii) we propose a new model structure to improve performance; and (iii) we verify that the proposed method indeed improves performance through the experimental results. Full article

The long-run sustainability of pay-as-you-go pension systems crucially depends on the dynamic balance between social-security contributions paid by the working population and benefits paid to retirees. In Italy, the National Social Security Institute (INPS) manages the core of the public system, whose financial equilibrium is increasingly challenged by demographic aging, labor market fragility, and macroeconomic shocks. In this paper, in line with the aims of the Special Issue “Signal Processing and Machine Learning in Real-Life Processes”, we reinterpret the Italian pension system as a complex stochastic signal-processing problem. Using the most recent data published in the Annuario Statistico Italiano 2024 highlighting by ISTAT—with a focus on Protection and Social Security—we construct a set of time series describing contributions, benefits, coverage ratios and pension amounts, both at the national and territorial level. On this basis, we compare classical time-series models and a recurrent neural network with Long Short-Term Memory (LSTM) architecture for multi-step forecasting of the main aggregates. The signal-processing perspective allows us to disentangle trend, cyclical and shock components, while machine learning provides flexible nonlinear forecasting tools capable of capturing structural breaks such as the COVID-19 crisis. Our empirical results suggest that (i) pension expenditure remains high and persistent as a share of GDP; (ii) the contribution coverage ratio improved in 2022 but remains below the pre-pandemic level; and (iii) regional heterogeneity in the per-capita pension deficit is substantial and stable over time, with persistent imbalances in Southern regions and Islands. Finally, we perform a scenario analysis combining LSTM-based forecasts with demographic and labor market hypotheses, and we quantify the impact of alternative policy measures on the future pension deficit signal. The proposed framework, which integrates permutation-based inference, signal decomposition and deep learning, provides a reproducible template for the real-time monitoring of pension sustainability using official open data. Full article

Static honeypot deployment and one-shot attack-path analysis often become ineffective against adaptive adversaries because fixed decoy layouts are easy to fingerprint and risk estimates quickly go stale. This paper presents a unified, mathematically grounded TrapManager framework that couples graph representation learning with budget-constrained combinatorial optimization for dynamic cyber deception. We model attacker progression on vulnerability-based attack graphs and learn context-aware node embeddings using a Graph Attention Network (GAT) that fuses vulnerability-driven risk signals (e.g., CVSS-derived node scores) with structural features. The learned representations are used to estimate edge plausibility and rank candidate source–target routes at the path level. Given limited resources, we formulate pointTrap placement as a Mixed-Integer Programming (MIP) problem that maximizes the expected interception of high-risk paths while penalizing deployment cost under explicit budget constraints, including mandatory coverage of the top-ranked critical paths. To enable online adaptiveness, a pointTrap-triggered, event-driven feedback mechanism locally amplifies risk around alerted regions, updates path weights without retraining the GAT, and re-solves the MIP for rapid redeployment. Experiments on MulVAL-generated benchmark attack graphs and cross-domain transfer settings demonstrate fast convergence, strong discrimination between attack and non-attack edges, and early interception within a small number of hops even with minimal decoy budgets. Overall, the proposed framework provides a scalable and resource-efficient approach to closed-loop attack-path defense by integrating attention-based learning and integer optimization. Full article