Search Results (1,984)

In this study, a Z-scheme Ho₂InSbO₇/Ag₃PO₄ (HAO) heterojunction photocatalyst was successfully fabricated for the first time by ultrasound-assisted solvothermal method. The structural features, compositional components and morphological characteristics of the synthesized materials were thoroughly characterized by a series of techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectrum, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. A comprehensive array of analytical techniques, including ultraviolet-visible diffuse reflectance absorption spectra, photoluminescence spectroscopy, time-resolved photoluminescence spectroscopy, photocurrent testing, electrochemical impedance spectroscopy, electron paramagnetic resonance, and ultraviolet photoelectron spectroscopy, was employed to systematically investigate the optical, chemical, and photoelectronic properties of the materials. Using oxytetracycline (OTC), a representative tetracycline antibiotic, as the target substrate, the photocatalytic activity of the HAO composite was assessed under visible light irradiation. Comparative analyses demonstrated that the photocatalytic degradation capability of the HAO composite surpassed those of its individual components. Notably, during the degradation process, the application of the HAO composite resulted in an impressive removal efficiency of 99.89% for OTC within a span of 95 min, along with a total organic carbon mineralization rate of 98.35%. This outstanding photocatalytic performance could be ascribed to the efficient Z-scheme electron-hole separation system occurring between Ho₂InSbO₇ and Ag₃PO₄. Moreover, the adaptability and stability of the HAO heterojunction were thoroughly validated. Through experiments involving the capture of reactive species and electron paramagnetic resonance analysis, the active species generated by HAO were identified as hydroxyl radicals (•OH), superoxide anions (•O₂⁻), and holes (h⁺). This identification provides valuable insights into the mechanisms and pathways associated with the photodegradation of OTC. In conclusion, this research not only elucidates the potential of HAO as an efficient Z-scheme heterojunction photocatalyst but also marks a significant contribution to the advancement of sustainable remediation strategies for OTC contamination. Full article

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

Al-Mg-Si (6XXX) series aluminum alloys are widely applied in aerospace and transportation industries. However, exploring how varying compositions affect alloy properties and deformation mechanisms is often time-consuming and labor-intensive due to the complexity of the multicomponent composition space and the diversity of processing and heat treatments. This study, inspired by the Materials Genome Initiative, employs high-throughput experimentation—specifically the kinetic diffusion multiple (KDM) method—to systematically investigate how the pop-in effect, indentation size effect (ISE), and creep behavior vary with the composition of Al-Mg-Si alloys at room temperature. To this end, a 6016/Al-3Si/Al-1.2Mg/Al KDM material was designed and fabricated. After diffusion annealing at 530 °C for 72 h, two junction areas were formed with compositional and microstructural gradients extending over more than one thousand micrometers. Subsequent solution treatment (530 °C for 30 min) and artificial aging (185 °C for 20 min) were applied to simulate industrial processing conditions. Comprehensive characterization using electron probe microanalysis (EPMA), nanoindentation with continuous stiffness measurement (CSM), and nanoindentation creep tests across these gradient regions revealed key insights. The results show that increasing Mg and Si content progressively suppresses the pop-in effect. When the alloy composition exceeds 1.0 wt.%, the pop-in events are nearly eliminated due to strong interactions between solute atoms and mobile dislocations. In addition, adjustments in the ISE enabled rapid evaluation of the strengthening contributions from Mg and Si in the microscale compositional array, demonstrating that the optimum strengthening occurs when the Mg-to-Si atomic ratio is approximately 1 under a fixed total alloy content. Furthermore, analysis of the creep stress exponent and activation volume indicated that dislocation motion is the dominant creep mechanism. Overall, this enhanced KDM method proves to be an effective conceptual tool for accelerating the study of composition–deformation relationships in Al-Mg-Si alloys. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

The pursuit of sustainable construction practices has become imperative in the modern era. This paper delves into the research of the properties and application of a specific material called “red clay” from the locality “Crvena Mogila” in Macedonia. A series of laboratory tests were conducted to evaluate the physical, mechanical, and chemical properties of the material. The tested samples show that it is a porous material with low density, high water absorption, and compressive strength in range of 29.85–38.32 MPa. Samples of composite wall blocks were made with partial replacement of natural aggregate with red clay aggregate. Two types of blocks were produced with dimensions of 390 × 190 × 190 mm, with five and six holes. The average compressive strength of the blocks ranges from 3.1 to 4.1 MPa, which depends on net density and the number of holes. Testing showed that these blocks have nearly seven-times-lower thermal conductivity than conventional concrete blocks and nearly twice-lower conductivity than full-fired clay bricks. The general conclusion is that the tested red clay is an economically viable and sustainable material with favourable physical, mechanical, and thermal parameters and can be used as a granular aggregate in the production of composite ceramic blocks. Full article

Geogrids are frequently utilized in engineering for reinforcement; yet, they are vulnerable to construction damage when employed on coarse-grained soil subgrades. In contrast, waste tire grids are more appropriate for subgrade reinforcement owing to their rough surfaces, integrated steel meshes, robust transverse ribs, extended degradation cycles, and superior durability. Based on the limit equilibrium theory, this study developed formulae for calculating the internal and external frictional resistance, as well as the end resistance of waste tires, to ascertain the interface bearing properties and calculation techniques of waste tire grids. Based on this, a mechanical model for the ultimate pull-out resistance of waste-tire-reinforced soil was developed, and its validity was confirmed through a series of pull-out tests on single-sided strips, double-sided strips, and tire grids. The results indicated that the tensile strength of one side of the strip was approximately 43% of that of both sides, and the rough outer surface of the tire significantly enhanced the tensile performance of the strip; under identical normal stress, the tensile strength of the single-sided tire grid was roughly nine times and four times greater than that of the single-sided and double-sided strips, respectively, and the grid structure exhibited superior anti-deformation capabilities compared to the strip structure. The average discrepancy between the calculated values of the established model and the theoretical values was merely 2.38% (maximum error < 5%). Overall, this research offers technical assistance for ensuring the safety of subgrade design and promoting environmental sustainability in engineering, enabling the effective utilization of waste tire grids in sustainable reinforcement applications. Full article

The development of alginate/polyacrylamide hydrogels for various biomedical applications has attracted significant interest, particularly due to their potential use in wound healing and tissue engineering. This study explores the fabrication of these hydrogels via 3D bioprinting with ultraviolet light curing, focusing on how the alginate concentration and curing speed impact their mechanical properties. Rheological testing was employed to examine the viscoelastic behavior of alginate/polyacrylamide hydrogels manufactured using a 3D bioprinting technique. The relaxation behavior and dynamic response of these hydrogels were analyzed under torsional stress, with relaxation curves fitted using a two-term Prony series. Fourier Transform Infrared (FTIR) spectroscopy was also employed to assess biocompatibility and the conversion of acrylamide. This study successfully demonstrated the printability of alginate/polyacrylamide hydrogels with varying alginate contents. The rheological results indicated that 3D bioprinted hydrogels exhibited significantly high stiffness, viscoelasticity, and long relaxation times. The curing speed had a minimal impact on these properties. Additionally, the FTIR analysis confirmed the complete conversion of polyacrylamide, ensuring no harmful effects in biological applications. The study concludes that 3D bioprinting significantly enhances the mechanical properties of alginate/polyacrylamide hydrogels, with the alginate concentration playing a key role in the shear modulus. These hydrogels show promising potential for biocompatible applications such as wound healing dressings. Full article

In this paper, the authors introduce the transmuted geometric integer-valued autoregressive model with periodicity, designed specifically to analyze epidemiological and public health time series data. The model uses a transmuted geometric distribution as a marginal distribution of the process. It also captures varying tail behaviors seen in disease case counts and health data. Key statistical properties of the process, such as conditional mean, conditional variance, etc., are derived, along with estimation techniques like conditional least squares and conditional maximum likelihood. The ability to provide k-step-ahead forecasts makes this approach valuable for identifying disease trends and planning interventions. Monte Carlo simulation studies confirm the accuracy and reliability of the estimation methods. The effectiveness of the proposed model is analyzed using three real-world public health datasets: weekly reported cases of Legionnaires’ disease, syphilis, and dengue fever. Full article

Background: 1,2,3,4,5-pentathiepines (PTEs) are compounds originally identified in marine ascidians and are currently under investigation for their promising pharmacological properties, particularly as potential antineoplastic agents. Objectives: In this study, we investigated the antineoplastic properties of a series of ten indolizine-based PTEs, comprising eight previously reported compounds and two newly synthesized derivatives. Methods: These compounds were evaluated against a panel of human cancer cell lines of diverse tissue origins, as well as, for the first time, on non-cancerous CR9 fibroblasts to assess their cytotoxic selectivity. In addition, their effects were tested on 3D spheroid models, providing preliminary insights into their potential in vivo efficacy. Initial screening focused on cell viability, followed by a more detailed characterization of the most active compounds in terms of their ability to induce apoptosis, necrosis, cell cycle arrest, and reactive oxygen species (ROS) generation. The anti-migratory activity of PTEs and a newly adapted assay to confirm sulfur species release in the cells were also performed for the first time. Results and Conclusions: Our findings reveal that four PTEs bearing hydrophilic, hydrogen-bonding functional groups, particularly the two inspired by natural analogs, exhibited the most potent anticancer activity. Full article

Interpreting complex clinical time series is vital for patient safety and care, as it is both essential for supporting accurate clinical assessment and fundamental to building clinician trust and promoting effective clinical action. In complex time series analysis, decomposing a signal into meaningful underlying components is often a crucial means for achieving interpretability. This process is known as time series disentanglement. While deep learning models excel in predictive performance in this domain, their inherent complexity poses a major challenge to interpretability. Furthermore, existing time series disentanglement methods, including traditional trend or seasonality decomposition techniques, struggle to adequately separate clinically crucial specific components: static patient characteristics, condition trend, and acute events. Thus, a key technical challenge remains: developing an interpretable method capable of effectively disentangling these specific components in complex clinical time series. To address this challenge, we propose CoTD-VAE, a novel variational autoencoder framework for interpretable component disentanglement. CoTD-VAE incorporates temporal constraints tailored to the properties of static, trend, and event components, such as leveraging a Trend Smoothness Loss to capture gradual changes and an Event Sparsity Loss to identify potential acute events. These designs help the model effectively decompose time series into dedicated latent representations. We evaluate CoTD-VAE on critical care (MIMIC-IV) and human activity recognition (UCI HAR) datasets. Results demonstrate successful component disentanglement and promising performance enhancement in downstream tasks. Ablation studies further confirm the crucial role of our proposed temporal constraints. CoTD-VAE offers a promising interpretable framework for analyzing complex time series in critical applications like healthcare. Full article

The purpose of this study was to demonstrate the feasibility of using spheroidal cellulose powders with different particle sizes (2 and 7 µm) in face creams and to evaluate their effect on selected physicochemical and performance properties of these products. A series of prototypes of facial creams with spheroidal cellulose were prepared. The following tests were carried out: stability, dynamic viscosity, texture analysis, degree of skin hydration, and evaluation of sensory appeal by consumers. It was observed that none of the creams showed instability over time. The addition of powdered spheroidal cellulose was found to increase dynamic viscosity and hardness and reduce the adhesion strength of the tested emulsions to the base face cream. A positive effect of the presence of polymeric raw materials on the level of skin hydration was observed. The most favorable results were obtained for the E4 cream prototype containing spheroidal powders of both 2 and 7 µm particle size at a weight ratio of 2.5 to 2.5. In addition, according to the members of the sensory panel, the E4 face cream was best evaluated and showed sensory benefits. The study concluded that spheroidal cellulose powders are a promising biodegradable alternative to microplastics in cosmetics. Full article