Search Results (30)

This paper proposes an enhanced adaptive unscented Kalman filter (SH-AUKF) method based on the Sage–Husa algorithm to address the issue of insufficient estimation accuracy for state parameters and road adhesion coefficients in distributed drive mining dump trucks under complex mining conditions. By integrating a seven-degree-of-freedom vehicle dynamics model with the Dugoff tire model, a collaborative observer is constructed for estimating state parameters and the four-wheel road adhesion coefficient. Through joint simulation verification using Trucksim–Matlab 2025b, it was demonstrated that under sinusoidal steering, step steering, and varying road adhesion coefficients (0.3~0.7), the root mean square error (RMSE) of longitudinal vehicle speed, slip angle, and yaw rate estimation using SH-AUKF was significantly reduced compared to the traditional UKF. Additionally, the estimation error of the four-wheel road adhesion coefficient was decreased by 8~26%. This has significant application value for improving the automation level of mining transportation. Full article

Lightweight high-entropy alloys are primarily designed to overcome the strength-to-density ratio limitations of conventional counterparts and often consist of elements with drastically different melting temperature and vapor pressure. Their chemistry, therefore, imposes challenges on alloy synthesis, particularly through liquid metal engineering routes, since elements with high vapor pressure (e.g., Mg, Zn, Li) vaporize before the higher-melting-point ingredients (e.g., Cu, V, Ni) are fully molten, resulting in volatile element loss. To overcome this challenge, a novel pressure-assisted induction melting (PAIM) process was developed and the proprietary furnace for its implementation was designed and built. The system allows precision melting of up to 10 cm³ of an alloy at temperatures up to 1700 °C while addressing the partial pressure requirements during the melting progress. The chamber is prepared using rough vacuum and re-filled with inert gas such as argon with the operating pressure range from about 10⁻⁴ MPa up to maximum of 1.6 MPa (233 psi). The alloy chemical composition can be modified in situ by feeding solid additives at specific melting stages through the isolated airlock without disrupting the pressure conditions within the chamber. The viability of the concept was verified by synthesis of two lightweight non-equimolar high-entropy alloys: Mg-rich Mg₅₀(MnAlZnCu)₅₀ and Al-rich Al₃₅Mg₃₀Si₁₃Zn₁₀Y₇Ca₅. The experiments showed that sequential multi-step melting procedures, designed based on inputs from FactSage computational analysis, when combined with PAIM synthesis, allowed manufacturing fully dense and chemically homogenous complex alloy compositions with optimal volumes for materials discovery research. Full article

During continuous navigation of the mother ship, an autonomous underwater vehicle (AUV) can be recovered through an underwater hangar, and the accurate localization of the AUV relative to the mother ship is a key step in the recovery process. To address the AUV localization problem, an n-shaped hydrophone array is designed based on the spatial configuration of the underwater hangar. Since underwater acoustic signals are susceptible to multipath propagation, co-channel interference, and other transmission impairments, the signals received by the array often exhibit coherence. Accordingly, a far-field sound source localization method based on the n-shaped array is proposed. The proposed algorithm first applies spatial smoothing to the x-axis and y-axis subarrays and jointly constructs a received data vector, followed by eigenvalue decomposition of the corresponding covariance matrix. The Multiple Signal Classification (MUSIC) algorithm is then employed to obtain coarse estimates of the source angles. These coarse estimates are subsequently used as initial values for the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, which performs refined optimization of the angular parameters in a continuous parameter space, thereby effectively improving estimation accuracy. Furthermore, the proposed algorithm is extended from far-field scenarios to near-field localization. Simulation results demonstrate that the proposed method achieves good parameter estimation performance. Full article

The extraction of high-purity sodium tungstate from complex wolframite concentrates presents significant challenges due to the limitations of conventional processing methods, which are often energy-intensive and generate substantial secondary waste. In this study, we propose a novel phase-regulated alkali fusion approach for the one-step production of high-purity Na₂WO₄. Using phase-diagram calculations with FactSage in the Na-Fe-Mn-Si-O system, SiO₂ was introduced to regulate slag formation, promoting immiscibility between the silicate slag and Na₂WO₄ melt. This resulted in a clear stratification of the phases at 1000 °C, enabling spontaneous separation of the Na₂WO₄-rich salt phase from the slag. The optimized conditions achieved a sodium tungstate purity of 98.76%, with a tungsten recovery rate of 98.91%. Furthermore, impurity elements such as Fe and Mn were preferentially retained in stable silicate/oxide phases within the slag, contributing to the high purity of the sodium tungstate product. This method offers a simplified and environmentally friendly alternative to traditional hydrometallurgical and pyrometallurgical processes, with significant implications for the efficient utilization of complex tungsten resources. Full article

In this paper, I focus on a major corpus of the earliest Syrian Christian literature, Aphrahat the Persian Sage’s collection of epistles titled Demonstrations (Taḥwyātā; early 4th century), in order to gauge his thoughts on the “sins of reading”, peccata lectionis. First, I present the Aphrahatic corpus as it currently is and has been perceived over time in its Western and Eastern reception history. Then, I briefly consider what importance early Greek and Syriac monastic sources—like the Vita Antonii, the Pseudo-Macarian Homilies, Theodoret of Cyrrhus, Palladius’ Historia Lausiaca, the Ktābā dmasqātā (the Syriac Book of Steps), etc.—attributed to the reading of scripture as a regular part of a monk’s daily practice. It is against this historical backdrop that Aphrahat’s stance on reading scripture can be meaningfully interpreted. Finally, I present and analyze what the earliest-known orthodox Syrian church father, Aphrahat himself, has to say about the reading of scripture and its concurrent threat, the peccatum lectionis. As the Persian Sage was an excellent Biblical scholar, he made abundant references to religious reading practices in his Demonstrations. To his mind, the locus where sin may enter the meditative reading of early Syrian versions of the Bible is the interpretation of the text: misunderstanding it may lead to sin and potentially damnation. However, the wise person should be able to evade this danger, supported by the natural piety and cosmic religion inspired in them by the majesty of creation, which is a true reflection of divine infinity. Full article

The current study focuses on a preliminary evaluation of the use of solid residues produced from the distillation of selected medicinal and aromatic plants (MAP) as fertilizers for alkaline soils. Specifically, the residues of hemp (Cannabis sativa L.), helichrysum (Helichrysum Italicum (Roth) G. Don), lavender (Lavandula angustifolia Mill.), oregano (Origanum vulgare L.), rosemary (Rosmarinus officinalis L.) and sage (Salvia officinalis L.) were added in an alkaline and calcareous soil at the rates of 0 (control), 1, 2, 4 and 8%, in three replications (treatments), and the treated soils were analyzed. The results showed that upon application of the residues, soil electrical conductivity (EC), organic C, total N and the C/N ratio significantly increased, especially at the 4 and 8% rates. The same was found for soil available P, K, B, Cu and Mn. The effects of the residues on soil pH, cation exchange capacity (CEC) and available Zn and Fe were rather inconclusive, whereas soil available N significantly decreased, which was somewhat unexpected. From the different application rates tested, it seems that all residues could improve soil fertility (except N?) when they were applied to soil at rates of 2% and above, without exceeding the 8% rate. The reasons for the latter statement are soil EC and available Mn: the doubling of EC upon application of the residues and the excessive increase in soil available Mn in treatments with 8% residues raise concerns of soil salinization and Mn phytotoxicity risks, respectively. This work provides the first step towards the potential agronomic use of solid residues from MAP distillation in alkaline soils. However, for the establishment of such a perspective, further research is needed in respect to the effect of residues on plant growth and soil properties, by means of at least pot experiments. Based on the results of the current study, the undesirable effect of residues on soil available N should be investigated in depth, since N is the most important essential element for plant growth, and possible risks of micronutrient phytotoxicities should also be studied. In addition, application rates between 2 and 4% should be studied extensively in order to recommend optimum application rates of residues to producers. Full article

This study presents research on the production of activated biocarbons derived from herbal waste. Sage stems were chemically activated with two activating agents of different chemical natures—H₃PO₄ and K₂CO₃—and subjected to two thermal treatment methods: conventional and microwave heating. The effect of the activating agent type and heating method on the basic physicochemical properties of the resulting activated biocarbons was investigated. These properties included surface morphology, elemental composition, ash content, pH of aqueous extracts, the content and nature of surface functional groups, points of zero charge, and isoelectric points, as well as the type of porous structure formed. In addition, the potential of the prepared carbonaceous materials as adsorbents of model organic (represented by Triton X-100 and methylene blue) and inorganic (represented by iodine) pollutants was assessed. The influence of the initial adsorbate concentration (5–150 (dye) and 10–800 mg/dm³ (surfactant)), temperature (20–40 °C), and pH (2–10) of the system on the efficiency of contaminant removal from aqueous solutions was evaluated. The adsorption kinetics were also investigated to better understand the rate and mechanism of contaminant uptake by the prepared activated biocarbons. The results showed that materials activated with orthophosphoric acid exhibited a significantly higher sorption capacity for all tested adsorbates compared to their potassium carbonate-activated counterparts. Microwave heating was found to be more effective in promoting the formation of a well-developed specific surface area (471–1151 m²/g) and porous structure (mean pore size 2.17–3.84 nm), which directly enhanced the sorption capacity of both organic and inorganic contaminants. The maximum adsorption capacities for iodine, methylene blue, and Triton X-100 reached the levels of 927.0, 298.4, and 644.3 mg/g, respectively, on the surface of the H₃PO₄-activated sample obtained by microwave heating. It was confirmed that the heating method used during the activation step plays a key role in determining the physicochemical properties and sorption efficiency of activated biocarbons. Full article

Current methods for public opinion propagation prediction struggle to jointly model temporal dynamics, structural complexity, and dynamic node influence in evolving social networks. To overcome these limitations, this paper proposes a public opinion dissemination prediction model based on the integration of dynamic time-weighted Rényi entropy (DTWRE) and graph neural networks. By incorporating a time-weighted mechanism, the model devises two tiers of Rényi entropy metrics—local node entropy and global time-step entropy—to effectively quantify the uncertainty and complexity of network topology at different time points. Simultaneously, by integrating DTWRE features with high-dimensional node embeddings generated by Node2Vec and utilizing GraphSAGE to construct a spatiotemporal fusion modeling framework, the model achieves precise prediction of link formation and key node identification in public opinion dissemination. The model was validated on multiple public opinion datasets, and the results indicate that, compared to baseline methods, it exhibits significant advantages in several evaluation metrics such as AUC, thereby fully demonstrating the effectiveness of the dynamic time-weighted mechanism in capturing the temporal evolution of public opinion dissemination and the dynamic changes in network structure. Full article

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes. Full article

Removing polychlorinated biphenyls (PCBs) from the environment is an important process for the protection of biota. This work examines three different approaches to the degradation of such contaminants. The first involves the use of iron bionanoparticles (Fe-BNPs) prepared through green synthesis from selected plant matrices. The second approach entails the use of the bacteria Stenotrophomonas maltophilia (SM) and Ochrobactrum anthropi (OA) isolated from a PCB-contaminated area, Strážsky canal, located in the Slovak republic, which receives efflux of canal from Chemko Strážske plant, a former producer of PCB mixtures. The third approach combines these two methods, employing a sequential hybrid two-step application of Fe-BNPs from the plant matrix followed by the application of bacterial strains. Fe-BNPs are intended to be an eco-friendly alternative to synthetic nanoscale zero-valent iron (nZVI), which is commonly used in many environmental applications. This work also addresses the optimization parameters for using nZVI in PCB degradation, including the pH of the reaction, oxygen requirements, and dosage of nZVI. Pure standards of polyphenols (gallic acid, GA) and flavonoids (quercetin, Q) were tested to produce Fe-BNPs using green synthesis at different concentrations (0.1, 0.3, 0.5, 0.8, and 1 g.L⁻¹) and were subsequently applied to the PCB degradation experiments. This step monitored the minimum content of bioactive substances needed for the synthesis of Fe-BNPs and their degradation effects. Experimental analysis indicated that among the selected approaches, sequential nanobiodegradation appears to be the most effective for PCB degradation, specifically the combination of Fe-BNPs from sage and bacteria SM (75% degradation of PCBs) and Fe-BNPs from GA (0.3 g.L⁻¹) with bacteria OA (92% degradation of PCBs). Full article

A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor’s integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network’s local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra’s Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra’s URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity. Full article

Silicone monomers are the basic raw materials for the preparation of silicone materials. The secondary dust generated during the preparation of silicone monomer by the Rochow–Müller method is a fine particulate waste with high silicon content. In this paper, the physical and chemical properties of silicon powder after pretreatment were analyzed, and an experimental study was conducted on the use of silicon dust in the preparation of Si₃N₄, a nitrogen enhancer for steelmaking, by direct nitriding method in order to achieve the resourceful use of this silicon dust. Furthermore, the thermodynamics and kinetics of the nitriding process at high temperatures were analysed using FactSage 8.1 software and thermogravimetric experiments. The results indicate that after holding at a temperature range of 1300~1500 °C for 3 h, the optimal nitriding effect occurs at 1350 °C, with a weight gain rate of 26.57%. The nitridation of silicon dust is divided into two stages. The first stage is the chemical reaction control step. The apparent activation energy is 2.36 × 10⁵ kJ·mol⁻¹. The second stage is the diffusion control step. The silicon dust growth process is mainly controlled by vapor–liquid–solid (VLS) and vapor–solid (VS) mechanisms. Full article

The aim of the study was to examine the effect of ultrasonic maceration (U) on the extraction of carnosic acid (CA) and its derivative—carnosol (C)—directly from sage into fish oil, compared to homogenization-assisted maceration (H). It was shown that the ultrasonic maceration process (U) allowed for obtaining a macerate enriched in carnosic acid (CA) and carnosol (C), also containing rosmarinic acid (RA), total polyphenols, and plant pigments, and showing antioxidant properties (DPPH test). There was no unequivocal difference in the efficiency of extracting ingredients from sage into the oil macerate between U and H, with the use of ultrasound in most cases resulting in a greater extraction of C and less extraction of pigments from sage into the macerate than in H. The highest simultaneous contents of CA (147.5 mg/100 g) and C (42.7 mg/100 g) in the macerate were obtained after 60 min of maceration U when using a higher power (320 W). The amount of determined compounds also depended on the concentration of methanol (methanol; 70% methanol) used for the analysis. The maceration U is a simple, safe, “green method” of obtaining active substances, with a reduced number of steps, enabling an interesting application form of CA and C, e.g., for food or cosmetics. Full article

Clary Sage extracts are of industrial interest: in particular, sclareol shows a strong pharmaceutical potential. Supercritical fluid extraction was used to recover compounds of interest from a Salvia sclarea L. waxy n-hexane extract (“concrete”), using semi-continuous fractionation and a multi-step extraction strategy. Multi-step extraction experiments were carried out in two phases: the first one operated at 90 bar and 50 °C; the second one at 100 bar and 40 °C. GC-MS traces showed that during the first extraction step, only lighter compounds (e.g., monoterpenes, sesquiterpenes, and derivatives) were collected, whereas, in the second step, only sclareol and related compounds were recovered. By adjusting operating conditions (temperature and pressure), selective extraction of different families of compounds was accomplished, with no further need for post-processing of the products. Moreover, using two separators in series, the compounds of interest were fractionated from paraffins and, by changing the operating conditions, the extraction yield increased from about 6.0% to 9.3% w/w as CO₂ density increased. Full article

Young people are stepping forward and engaging in or leading programs promoting climate action and sustainability. To optimize program outcomes, it is important to understand the nature of these programs, as well as their successes and enablers. Consequently, a scoping review was conducted across six databases, Taylor and Francis, Medline, Web of Science, Scopus, Sage and Wiley, to examine existing programs that promote climate change action amongst young people aged 12 to 25 years. The review sought to determine what is known about these programs and their outcomes by documenting what elements contribute to successful behavior changes in young people. Forty-eight articles were included in the review, with almost half of the studies from the United States. Eight elements recurred throughout the reviewed journal articles including intersecting external and internal factors contributing to reported behavior change. External factors included the social environment, place, knowledge, leadership and goal setting development that fostered internal factors that included, self-efficacy, identity, agency and action competence, and systems thinking. Learning from these programs to improve design and ensure sustainable outcomes is key to improving the capabilities of young people to continue responding to the climate challenge. Full article