Search Results (17,218)

Deep-seated landslide complexes are widespread in soft-rock hill-country landscapes, yet their regional morphometric organisation and controlling factors remain insufficiently quantified. This study uses high-resolution (1 m) airborne LiDAR-derived terrain data integrated with geological and drainage-network datasets to investigate landslide complexes in the eastern Tararua District, New Zealand. A relative, unit-based morphometric framework is applied to compare terrain derivatives (including slope, aspect, and multi-scale relative relief) between mapped landslides and their host geological units. To isolate intrinsic lithological controls from geomorphic influences, the analysis is restricted to landslides occurring entirely within a single geological unit. The results indicate that lithology exerts first-order control on landslide morphometry, while fluvial incision and valley confinement regulate landslide initiation and persistence. Landslides are preferentially associated with low- to mid-order channels, indicating strong hillslope–channel coupling within a young, actively uplifting landscape. A conceptual threshold framework is proposed, showing that landslides develop where lithological susceptibility and relief amplification jointly exceed stability thresholds. By integrating geological information with LiDAR-based morphometric analysis, this study provides a transferable framework for distinguishing instability regimes and improving understanding of sediment dynamics and landscape evolution in soft-rock terrains. Full article

For the first time, red LED irradiation was applied at pilot scale in the table olive industry to evaluate its influence on Negrinha de Freixo cultivar natural fermentation. Physicochemical parameters, microbial dynamics, and sensory attributes were evaluated between 60 and 95 days, with two irradiation periods (60–70 and 85–95 days). Three conditions were examined: control-static, pumping-brine recirculation, and LED-brine recirculation + red light exposure. Color or texture was not affected. The lowest pH values were consistently observed in the LED-treated samples. Total phenolic compounds in olives showed a slight decrease from 60 to day 95; however, significant differences were only detected between the pumping treatment and the other two conditions. At the end of the first LED irradiation period, a growth of lactic acid bacteria and aerobic mesophilic bacteria was observed in the order of log 1.0 CFU/mL in the brine, and the yeast count (log 1.4 CFU/g) and LAB (log 1.2 CFU/g) in the olives relative to the control, while the second irradiation period did not show a significant effect. Sensory analysis revealed that LED- irradiated olives exhibited the highest hardness (5.6) values, whereas control samples presented the highest perception of putrid defect. Overall, the results demonstrate that red LED photostimulation may be promising for application in the table olive industry. Full article

The persistence of azo dyes in industrial effluents poses significant environmental risks; therefore, there is a need to develop effective adsorbents. This study investigates the efficiency of a Cu₂O/CuO nanocomposite as an adsorbent for the removal of a model dye, methyl orange (MO), from aqueous solutions. The material was characterized by XRD, SEM and BET analyses, revealing a dominant Cu₂O phase (96 wt%) with CuO fractions, and an average particle size of ~18 nm paired with a specific surface area of 19.54 m² g⁻¹. FTIR and TOC assays revealed the adsorption and degradation of MO by action of the nanocomposite. Operational parameters such as adsorbent dosage, initial dye concentration, pH, and the point of zero charge (PZC) were investigated. Under the optimized conditions, the nanocomposite achieved a dye removal efficiency of 97.0%. The kinetic results showed a strong correlation with the pseudo-second-order model. Furthermore, isotherm analysis revealed that the adsorption process is best described by the Langmuir–Freundlich model, demonstrating an outstanding maximum theoretical adsorption capacity (q_max) of 254.76 mg g⁻¹, which closely aligns with the experimental value (249.48 mg g⁻¹). The findings demonstrated that the synthesized Cu₂O/CuO nanocomposite acts as an efficient and promising adsorbent for the remediation of dye-contaminated waters. Full article

Background: Polygenic risk scores for Alzheimer’s disease (AD), organized by gene networks shared between the blood and brain, may provide insights into underlying disease mechanisms common to both tissues. Methods: We derived a blood–brain network-based polygenic risk score (nbPRS) from AD-associated genetic variants for three blood-brain networks, selected by the preservation of blood and brain gene co-expression networks, and AD association. Participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 1109), Framingham Heart Study (FHS, n = 8310), the Religious Orders Study Memory Aging Project (ROSMAP, n = 1215), and Mount Sinai Brain Bank (MSBB, n = 323) were stratified into low- and high-nbPRS subgroups, then profiled using longitudinal and cross-sectional data. We compared the conversion from normal cognition to AD between nbPRS subgroups. Genes differentially expressed among low- and high-nbPRS individuals were profiled with classical neuropathological markers and we investigated potential biologically relevant pathways for the genes significantly expressed in high-risk individuals. Results: Individuals with high nbPRS in three AD-associated networks (M2, M6, M14) demonstrated significant impairment in executive function and memory performance, whereas high-risk individuals in networks M2 and M14 had significantly reduced hippocampal volume. We observed high-risk individuals in M2 and M14 developed AD at twice the rate of low-risk individuals in these networks. HLA genes were differentially expressed with transcriptome-wide significance among low- and high-nbPRS individuals in M14 and associated with neuroinflammatory and tau pathology. Conclusions: Polygenic risk scores derived from blood and brain networks can differentiate individuals with a high risk of AD conversion. Full article

Electrochemical chlorine production is of considerable industrial importance in areas such as water treatment, chemical manufacturing, and disinfection. However, conventional precious metal-based dimensionally stable anodes (DSAs), such as RuO₂- and IrO₂-based systems, are limited by high cost and resource constraints, motivating the development of low-cost alternative catalysts. In this study, Mn₃O₄ electrodes with controllable defect characteristics were fabricated by electrochemical deposition under various processing conditions. The effects of defect modulation and surface modification on the structural, electronic, and electrochemical properties of the electrodes were systematically evaluated. X-ray diffraction analysis confirmed that all deposited films retained a stable tetragonal Mn₃O₄ crystal structure, indicating that the deposition parameters primarily influenced defect states rather than the bulk phase. Mott–Schottky measurements revealed that the Mn₃O₄ electrodes exhibited p-type semiconducting behavior, with charge carrier densities on the order of 10¹⁴ cm⁻³, suggesting that oxygen vacancy-related defect states may contribute to the observed electronic properties of the electrodes. To further enhance anodic performance, Pt was introduced onto the Mn₃O₄ surface via sputtering, resulting in significantly improved charge transfer characteristics. Electrochemical measurements demonstrated that the best performing Pt/Mn₃O₄ electrodes delivered a current density exceeding 100 mA cm⁻² at an applied potential of 1.5 V versus Ag/AgCl. More importantly, defect-enriched Pt/Mn₃O₄ electrodes exhibited markedly enhanced chlorine evolution activity, with the chlorine production rate increasing from approximately 14 µmol cm⁻² to 29 µmol cm⁻², corresponding to an enhancement of about 2.07-fold. Faradaic efficiency analysis further showed that sample (g) and sample (n) achieved chlorine evolution efficiencies of 59.2% and 74.6%, respectively, indicating a higher tendency toward chlorine evolution for the Pt-modified electrodes under the tested conditions. These findings suggest that the synergistic combination of defect engineering and surface modification effectively modulates the electronic structure of Mn₃O₄, providing a viable strategy for improving chlorine evolution performance. Full article

Atmospheric plasma spraying (APS) represents a critical solution for enhancing the durability of agricultural components, such as harrow discs, which are subjected to synergistic wear and corrosion during soil cultivation. This study presents experimental results evaluating the electrochemical corrosion behavior and thermal shock resistance of discs coated via atmospheric plasma thermal spraying. Both metallic and ceramic materials, in powder form, from established manufacturers were used to produce the coatings, and the three types of coatings (two metallic and one ceramic) have the following chemical compositions and trade names: W₂C/WC12Co (Metco71NS), Cr₂O₃-4SiO₂-3TiO (Metco136F) and Co25.5Cr10.5Ni7.5W0.5C (Metco45C-NS). The coatings were analyzed using electron microscopy to evaluate the surfaces following corrosion testing. The ceramic coating based on the Cr₂O₃-4SiO₂-3TiO demonstrated the highest protective efficiency by increasing the charge transfer resistance from 307 Ω/cm² to 2213 Ω/cm² for the ceramic coating. It provided a superior physical barrier, reducing the corrosion current density from 0.140 mA/cm² for unprotected substrate to 0.004 mA/cm², representing an improvement of nearly two orders of magnitude. These findings demonstrate that implementing Cr₂O₃-4SiO₂-3TiO ceramic systems can significantly extend the operational lifespan of soil-engaging components, providing a cost-effective strategy for reducing maintenance intervals and material loss in aggressive agricultural environments. Full article

To address the emergency obstacle-avoidance problem of intelligent vehicles on structured roads, this paper proposes an integrated planning and control method that combines an improved Artificial Potential Field (APF) with fuzzy Model Predictive Control (MPC). Different from a direct APF + MPC combination, the planning layer introduces a braking-distance threshold, an effective obstacle-influence boundary, and sinusoidal shape factors to reshape the obstacle repulsive field and alleviate local-minimum behavior. A seventh-order polynomial smoothing strategy is then adopted to generate a reference path with higher-order continuity. For trajectory tracking, a fuzzy adaptive MPC controller adjusts the prediction horizon and control horizon online according to lateral error, while a fuzzy PID controller regulates longitudinal speed. MATLAB/Simulink and CarSim co-simulation results in single-static, double-static, and double-dynamic obstacle scenarios show that the proposed method can generate smoother trajectories and achieve more stable tracking, thereby improving obstacle-avoidance safety and ride comfort. In the double-static scenario, the peak lateral error is reduced from about 0.7 m to within 0.1 m, while in the double-dynamic scenario the longitudinal speed is maintained within 78–80 km/h instead of dropping to about 67 km/h under the baseline controller. The study provides a practical technical framework for integrated decision-planning-control design in structured-road intelligent vehicles. Full article

For increasing the sustainability of existing building stock, energy renovation programs for existing buildings are being implemented worldwide with the aim of reducing the CO₂e footprint associated with building operation. In countries with high seismicity, the long-term effectiveness of energy renovation programs is called into question, since a strong earthquake can severely affect existing buildings and compromise the sustainability of the implemented works. As a result, the design of energy renovation programs in seismically active countries must explicitly account for seismic risk. Integrated intervention programs were developed, in which energy renovation measures are implemented simultaneously with seismic strengthening interventions. Romania represents a particular case due to the specificity of the intermediate-depth Vrancea seismic source, which strongly affects more than 60% of the national territory, covering over 120,000 km². Consequently, a large existing building stock is susceptible to seismic damage in the event of a major earthquake. This paper proposes the assessment of the specific CO₂e footprint of the Romanian residential building stock for the two types of interventions. The results show that preventive seismic strengthening has the lowest CO₂e footprint when compared to reactive seismic strengthening, the computed values for different scenarios ranging between 6 kg/m² and 45 kg/m² in case of preventive retrofitting and 23 kg/m² to 121 kg/m² in case of reactive retrofitting. Energy renovation leads to midrange values of 27 kg/m² to 58 kg/m². Nevertheless, all calculated values are significantly lower than the specific CO₂e footprint associated with new construction, proving the sustainability of existing building stock rehabilitation techniques. The research presented in this paper can be further extended through the implementation of scenario-based analyses concerning the improvement of the existing building stock through seismic strengthening and energy renovation, considering the occurrence of a major earthquake, in order to determine the optimal solution for the implementation of national programs in relation to the assumed objective of reducing CO₂e emissions at the building stock level. Full article

Biomass-based adsorbents for methylene blue (MB) currently face critical bottlenecks including raw material homogenization, insufficient adsorption capacity, and an unclear structure–activity relationship. To address these limitations, we prepared porous super activated carbon (SAC) with ultra-high specific surface area via KOH activation, using industrial balsa wood (Ochroma pyramidale) waste from the wind power industry as the precursor. The adsorption behavior and underlying mechanism of the as-prepared SAC towards MB were systematically investigated. The as-prepared SAC has an ultra-high specific surface area of 3833 m²/g, with a well-developed microporous structure matching the molecular size of MB. It exhibited a maximum monolayer MB adsorption capacity of 1037.76 mg/g, superior to similar biomass-based materials. Near-complete removal of high-concentration MB was achieved at an SAC dosage of 0.4 g/L, and the material maintained stable performance across a wide pH range of 4 to 10. The adsorption of MB onto SAC fitted well with the Langmuir isotherm and pseudo-second-order kinetic models, dominated by monolayer physisorption. The outstanding adsorption performance originated from the synergistic contribution of the pore confinement effect, π-π conjugation, electrostatic interaction, and hydrogen bonding. This work provides a new strategy for high-value utilization of balsa wood industrial waste and efficient treatment of dye wastewater. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article

We investigate $\lambda$-constacyclic codes of length n over finite commutative local rings R of characteristic p and order $q^5,$ where $q=p^m$ is an odd prime power, whose Jacobson radical $\mathcal{N}$ satisfies ${\mathcal{N}}^3=0\ne {\mathcal{N}}^2$, under the coprimality condition $\gcd (n,p)=1$. In this setting, exactly two radical types occur, namely $(3,1)$ and $(2,2)$, determined by the dimensions of $\mathcal{N}/{\mathcal{N}}^2$ and ${\mathcal{N}}^2$. For each type, we provide an explicit classification of the underlying rings and analyze the induced radical filtration of the ambient algebra $A_{\lambda }=R\left[X\right]/⟨X^n-\lambda ⟩$. We prove that every $\lambda$-constacyclic code is uniquely determined by its residual component in $A_{\lambda }/\mathrm{J}\left(A_{\lambda }\right)$ together with two torsion components arising from the radical chain $\mathrm{J}\left(A_{\lambda }\right)\supset \mathrm{J}{\left(A_{\lambda }\right)}^2\supset 0$. This residual–torsion decomposition yields explicit generating sets; in particular, every $\lambda$-constacyclic code admits a generating set consisting of at most five elements. Furthermore, we derive exact enumeration formulas for all $\lambda$-constacyclic codes. In the type $(2,2)$ case, the enumeration is governed by linear-algebraic constraints over the Chinese Remainder Theorem residue fields and, in the anisotropic class, depends on quadratic character values determined by the extension degrees. In the type $(3,1)$ case, the enumeration is controlled by the dimension of the radical of the induced symmetric bilinear form on the top radical layer, equivalently by the rank class of the associated canonical matrix. Full article

CuCo-based catalysts are promising candidates for higher alcohol synthesis from syngas, yet their performance is often limited by poor metal dispersion and insufficient Cu-Co synergy. In this work, a series of ordered mesoporous CuCoAl catalysts with varying Cu/Co atomic ratios were synthesized via the evaporation-induced self-assembly (EISA) method. The structural, electronic, and catalytic properties were systematically investigated using N₂ physisorption, XRD, TEM, H₂-TPR, CO-TPD, XPS, and fixed-bed reactor evaluation. The results show that all CuCoAl catalysts prepared by the EISA method possess well-ordered mesoporous structures with high surface areas (up to 235 m²/g) and narrow pore size distributions. The interaction between Cu and Co stabilizes the mesoporous framework, inhibits Cu particle growth, and induces electron transfer from Cu to Co as evidenced by XPS. Among the catalysts tested, Cu₁Co₁Al (Cu/Co = 1:1) exhibits the highest strong CO adsorption capacity (1.54 mmol/g) and surface hydroxyl content (63.29%), achieving a CO conversion of 32.9% with a C₂⁺ alcohol space–time yield of 20.5 mg·gcat⁻¹·h⁻¹. These findings establish clear structure–performance relationships for ordered mesoporous CuCoAl catalysts and provide fundamental guidance for the rational design of efficient catalysts for higher alcohol synthesis. Full article

A one-pot green combustion route was developed for the synthesis of ashy single-crystalline NiO nanoparticles using date molasses as a biogenic fuel and complexing medium. The obtained DM–NiO showed phase-pure cubic NiO with an average crystallite size of about 18 nm, a mesoporous texture with a BET surface area of 68.9 m² g⁻¹, a pore volume of 0.59 cm³ g⁻¹, an average pore diameter of 17.6 nm, and a mean particle size of 43.6 ± 8.13 nm. Optical characterization revealed defect-mediated light absorption with an energy gap of 3.11 eV, supporting solar-light-driven activity. In the photocatalytic degradation of pyronin Y, the catalyst exhibited strong pH dependence, reaching its best H₂O₂-free performance at pH 11 with a pseudo-first-order rate constant of 0.0072 min⁻¹, nearly six times higher than that at pH 3. The introduction of H₂O₂ markedly intensified the process, and at 9 mM H₂O₂, the rate constant increased to 0.048 min⁻¹, representing more than a sixfold enhancement over photocatalysis alone, while complete disappearance of the main visible absorption band was achieved within 38 min under solar illumination. Radical trapping experiments identified photogenerated holes and hydroxyl radicals as the dominant oxidative species. The catalyst also retained high activity over four successive cycles, with degradation efficiencies decreasing only slightly from 91.8% to 85.7%. These results demonstrate that date-molasses-assisted combustion synthesis provides a sustainable route to defect-active mesoporous NiO with highly enhanced solar photo-Fenton-like performance for dye-contaminated wastewater treatment. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article

This article develops a boundary-adaptive nonparametric methodology for estimating the geometric conditional quantiles of a multivariate response when the conditioning covariate is supported on the simplex—an important case, as it is the natural domain of compositional data. The statistical difficulty addressed here is twofold. First, geometric conditional quantiles for multivariate responses must be defined and estimated through a genuinely directional and convex framework rather than through any scalar ordering. Second, when the covariate is compositional or otherwise simplex-constrained, conventional symmetric kernel procedures suffer from intrinsic support mismatch and severe boundary distortion, thereby compromising both estimation accuracy and inferential validity near faces and edges of the simplex. The method proposed in this paper is designed precisely to overcome this combined obstacle. Our main innovation consists in embedding the spatial quantile formalism of Chaudhuri within a Dirichlet–Kernel smoothing scheme whose shape parameters depend deterministically on the evaluation point. This produces a convex M-estimator that respects the simplex geometry exactly, automatically adapts its local shape to the position of the target point, and removes the need for artificial boundary corrections. To the best of our knowledge, this is the first contribution to provide a complete asymptotic treatment of geometric conditional quantile estimation under simplex-supported covariates with location-adaptive asymmetric kernels. We establish a Bahadur-type linear representation with an explicit negligible remainder, from which we derive refined asymptotic bias and variance expansions. The variance analysis reveals a distinctive geometric phenomenon: each coordinate direction approaching the simplex boundary induces an additional $b^{-1/2}$ inflation factor, so that the variance at a face of codimension $\left|\mathcal{J}\right|$ scales as $n^{-1}{b}^{-(s+|\mathcal{J}\left|\right)/2}$. We further obtain the asymptotic mean squared error, an explicit optimal bandwidth rate, asymptotic normality under the nonstandard normalization $n^{1/2}{b}^{-s/4}$, and consistent plug-in covariance estimators yielding valid confidence ellipsoids. Numerical experiments and a real-data illustration based on the GEMAS data confirm the practical merit of the approach, especially in boundary regions where classical methods are known to deteriorate. Full article