Search Results (57)

Accumulation of salts in irrigated soils can be detrimental not only to growing crops but also to groundwater quality. Soil salinity should be regularly monitored, and appropriate irrigation at the required leaching rate should be applied to prevent excessive salt accumulation in the root zone, thereby improving soil fertility and crop production. We combined two frequency domain electromagnetic induction (FD-EMI) mono-channel sensors (EM31 and EM38) and operated them at different heights and with different coil orientations to monitor the vertical distribution of soil salinity in a salt-affected irrigated area in Kairouan (central Tunisia). Multiple measurement heights and coil orientations were used to enhance depth sensitivity and thereby improve salinity predictions from this type of proximal sensor. The resulting multi-configuration FD-EMI datasets were used to derive soil salinity information via inverse modeling with a recently developed in-house laterally constrained inversion (LCI) approach. The collected apparent electrical conductivity (ECa) data were inverted to predict the spatial and temporal distribution of soil salinity. The results highlight several findings about the distribution of salinity in relation to different irrigation systems using brackish water, both in the short and long term. The expected transfer of salinity from the surface to deeper layers was systematically observed by our FD-EMI surveys. However, the intensity and spatial distribution of soil salinity varied between different crops, depending on the frequency and amount of drip or sprinkler irrigation. Furthermore, our results show that vertical salinity transfer is also influenced by the wet or dry season. The study provides insights into the effectiveness of combining two different FD-EMI sensors, EM31 and EM38, for monitoring soil salinity in agricultural areas, thereby contributing to the sustainability of irrigated agricultural production. The inversion approach provides a more detailed representation of soil salinity distribution across spatial and temporal scales at different depths, and across irrigation systems, compared to the classical method based on soil samples and laboratory analysis, which is a point-scale measurement. It provides a more extensive assessment of soil conditions at depths up to 4 m with different irrigation systems. For example, the influence of local drip irrigation was imaged, and the history of a non-irrigated plot was evaluated, confirming the potential of this method. Full article

Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article

Managing hydraulic behaviour and water quality in semi-arid, transboundary rivers such as the Talas River in Kazakhstan requires reliable numerical tools for predicting free-surface flow through porous hydraulic structures. This study develops and verifies a two-dimensional computational fluid dynamics (CFD) framework for simulating free-surface water flow through porous media and demonstrates its applicability to a real river reach of the Talas in the Zhambyl region. The model combines the Volume of Fluid (VOF) method with the Darcy–Forchheimer formulation to represent porous resistance, while turbulence is described by the RNG k–$\epsilon$ model, and pressure–velocity coupling is handled by the PISO algorithm. Model verification is conducted against a classic dam-break experiment involving a rectangular porous barrier across a laboratory channel. The simulations successfully reproduce the main experimental observations, including rapid drawdown after gate opening, formation and attenuation of the free-surface wave, localized depression above the porous insert, and the subsequent approach to a quasi-steady state. Time histories of water levels at control points and the spatial progression of the wet front show close agreement with measurements. Using the validated setup, a site-specific two-dimensional domain for the Talas River is constructed to analyse the hydraulic influence of a porous bar. The model quantifies velocity redistribution and energy dissipation across the porous patch and provides physically consistent flow fields suitable for engineering assessments under various discharge conditions. Full article

An attachment of a particle on a bubble is a very complex process due to the surface chemistry of bubbles and particles, and hence it is difficult to describe the bubble–particle attachment mechanism from first principles. This paper focuses on better understanding of the bubble–particle attachment mechanism by predicting induction time from first principles for the glass beads–dodecyl amine hydrochloride (DAH) system. The induction time for the bubble–particle attachment was determined using an optically based attachment timer. The zeta potentials of bubbles and glass particles were measured by the microelectrophoresis method. The contact angle between a bubble and a particle was obtained using atomic force microscopy. In these calculations, the overall disjoining pressure and the overall energy of bubble–particle interactions comprised a sum of the DLVO and non-DLVO contributions. The overall energy of interactions was used to determine the critical film thickness, while the overall disjoining pressure was employed to estimate the wetting film drainage time using the theoretical models. By comparing experimental data and theoretical models for drainage of wetting films, it was found that the attachment of glass particles to air bubbles in the presence of DAH is accelerated due to the mobility of the air–water interface (of the wetting films), which has incorrectly been assumed as rigid (fully immobile) in the classical Stefan–Reynolds theory. Full article

To study the effects of liquid properties and interface parameters on gas–liquid two-phase flow in porous media. The volume flow model of gas–liquid two-phase flow in porous media was established, and the interface of the two-phase flow was reconstructed by tracing the phase fraction. The microscopic imbibition flow model was established, and the accuracy of the model was verified by comparing the simulation results with the classical capillary imbibition model. The flow characteristics in the fracturing process and backflow process were analyzed. The influence of flow parameters and interface parameters on gas flow was studied using the single-factor variable method. The results show that more than 90% of the flowing channels are invaded by fracturing fluid, and only about 50% of the fluid is displaced in the flowback process. Changes in flow velocity and wetting angle significantly affect Newtonian flow behavior, while variations in surface tension have a pronounced effect on non-Newtonian fluid flow. The relative position of gas breakthrough in porous media is an inherent property of porous media, which does not change with fluid properties and flow parameters. Full article

In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. However, relying solely on spectral indices can lead these models to overlook crucial urban contextual features, making it difficult to distinguish inundated areas from other similar features like shadows or wet roads. To address this, our research explores a novel approach to improve flood segmentation by integrating a row-wise cross attention (CA) module with ML ensemble learning. We apply this method to the analysis of the Brisbane Floods of 2022, utilizing 4-band satellite imagery from PlanetScope and derived spectral indices. Applied as a pre-processing step, the CA module fuses a spectral band index into each band of a peak-flood satellite image using a row-wise operation. This process amplifies subtle differences between floodwater and other urban characteristics while preserving complete landscape information. The CA-fused datasets are then fed into our proposed ensemble model, which is constructed using four classic ML models. A soft voting strategy averages their binary predictions to determine the final classification for each pixel. Our research demonstrates that CA datasets can enhance the sensitivity of individual ML models to floodwater in complex riverine urban areas, generally improving flood mapping accuracy. The experimental results reveal that the ensemble model achieves high accuracy (approaching 100%) on each CA dataset. However, this may be affected by overfitting, which indicates that evaluating the model on additional datasets may lead to reduced accuracy. This study encourages further research to optimize the model and validate its generalizability in various urban contexts. Full article

Background: Melt-based 3D printing technologies are currently extensively evaluated for research purposes as well as for industrial applications. Classical approaches often require intermediates, which can pose a risk to stability and add additional complexity to the process. The Advanced Melt Drop Deposition (AMDD) technology, is a 3D printing process that combines the principles of melt extrusion with pressure-driven ejection, similar to injection molding. This method offers several advantages over traditional melt-based 3D printing techniques, making it particularly suitable for pharmaceutical applications. Objectives: This study evaluates the AMDD printing system for producing solid oral dosage forms, with a primary focus on the thermo-stable polymer polyvinyl alcohol (PVA). The suitability of AMDD technology for creating amorphous solid dispersions (ASDs) is also examined. Finally, the study aims to define the material requirements and limitations of the raw materials used in the process. Methods: The active pharmaceutical ingredients (APIs) indometacin and ketoconazole were used, with PVA 4-88 serving as the carrier polymer. Powders, wet granulates, and pellets were investigated as raw materials and characterized. Dissolution testing and content analyses were performed on the printed dosage forms. Solid-state characterization was conducted using differential scanning calorimetry (DSC) and X-ray diffraction (XRD). Degradation due to thermal and mechanical stress was analyzed using nuclear magnetic resonance spectroscopy (NMR). Results/Conclusions: The results demonstrate that the AMDD 3D printing process is well-suited for producing solid dosage forms. Tablets were successfully printed, meeting mass uniformity standards. Adjusting the infill volume from 30% to 100% effectively controlled the drug release rate of the tablets. Solid-state analysis revealed that the AMDD process can produce amorphous solid dispersions with enhanced solubility compared to their crystalline form. The experiments also demonstrated that powders with a particle size of approximately 200 µm can be directly processed using AMDD technology. Full article

Counter-current spontaneous imbibition of water is a critical oil recovery mechanism. In the laboratory, the Amott test is a commonly used method to assess the efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell experiment estimates ultimate oil recovery, but not the recovery dynamics that hold fundamental information about the imbibition mechanisms. Retention of oil droplets at the outer core surface and initial production delay are the two key artifacts of the classic Amott experiment. This retention, referred to here as the “external-surface oil holdup effect” or simply “oil holdup effect”, often results in stepwise recovery curves that obscure the true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil recovery dynamics. Also for the first time, we used the generalized extreme value (GEV) statistics to describe oil production histories from water-wet heterogeneous limestone cores with finite initial water saturations. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil–rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation. Full article

Legionella pneumophila can cause a large panel of symptoms besides the classic pneumonia presentation. Here we present a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of Legionnaires’ disease in the same ward. While the first case was easily assumed as nosocomial based on the date of symptom onset, the second case required clear typing results to be assigned either as nosocomial and related to the same environmental source as the first case, or community acquired. To untangle this specific question, we applied core-genome multilocus typing (MLST), whole-genome single nucleotide polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large-scale epidemiological investigations, next-generation sequencing typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of the L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in the environment. Full article

Coughing, a common symptom associated with various respiratory problems, is a crucial indicator for diagnosing and tracking respiratory diseases. Accurate identification and categorization of cough sounds, specially distinguishing between wet and dry coughs, are essential for understanding underlying health conditions. This research focuses on applying the Swin Transformer for classifying wet and dry coughs using short-time Fourier transform (STFT) representations. We conduct a comprehensive evaluation, including a performance comparison with a 2D convolutional neural network (2D CNN) model, and exploration of two distinct image augmentation methods: time mask augmentation and classical image augmentation techniques. Extensive hyperparameter tuning is performed to optimize the Swin Transformer’s performance, considering input size, patch size, embedding size, number of epochs, optimizer type, and regularization technique. Our results demonstrate the Swin Transformer’s superior accuracy, particularly when trained on classically augmented STFT images with optimized settings (320 × 320 input size, RMS optimizer, 8 × 8 patch size, and an embedding size of 128). The approach achieves remarkable testing accuracy (88.37%) and ROC AUC values (94.88%) on the challenging crowdsourced COUGHVID dataset, marking improvements of approximately 2.5% and 11% increases in testing accuracy and ROC AUC values, respectively, compared to previous studies. These findings underscore the efficacy of Swin Transformer architectures in disease detection and healthcare classification problems. Full article

This work presents a dynamic modeling approach for analyzing the behavior of a bi-material cantilever actuator structure, consisting of a strip of filter paper bonded to a strip of tape. The actuator’s response is induced by a mismatch strain generated upon wetting, leading to the bending of the cantilever. The study delves into a comprehensive exploration of the dynamic deflection characteristics of the bilayer structure. It untangles the intricate connections among the saturation, modulus, hygro-expansion strain, and deflection, while uniquely addressing the challenges stemming from fluid–structure coupling. To solve the coupled fluid–solid differential equations, a combined numerical method is employed. This involves the application of the Highly Simplified Marker and Cell (HSMAC) technique for fluid flow analysis and the Finite Difference Method (FDM) for response deflection computation. In terms of the capillary flow model, the Computational Fluid Dynamics (CFD) simulations closely align with the classical Washburn relationship, depicting the wetted front’s evolution over time. Furthermore, the numerical findings demonstrate that heightened saturation levels trigger an increase in hygro-expansion strain, consequently leading to a rapid rise in response deflection until a static equilibrium is achieved. This phenomenon underscores the pivotal interplay among saturation, hygro-expansion strain, and deflection within the system. Additionally, the actuator’s response sensitivity to material characteristics is highlighted. As the mismatch strain evolving from paper hygro-expansion diminishes, a corresponding reduction in the axial strain causes a decrease in response deflection. The dynamic parameter demonstrates that the deflection response of the bilayer actuator diminishes as dynamic pressure decreases, reaching a minimal level beyond which further changes are negligible. This intricate correlation underscores the device’s responsiveness to specific material traits, offering prospects for precise behavior tuning. The dependence of paper modulus on saturation levels is revealed to significantly influence bilayer actuator deflection. With higher saturation content, the modulus decreases, resulting in amplified deflection. Finally, strong concordance is observed among the present fluidically coupled model, the static model, and empirical data—a testament to the accuracy of the numerical formulation and results presented in this study. Full article

The present study investigates different design strategies to produce non-wettable micropatterned surfaces. In addition to the classical method of measuring the contact angle, the non-wettability is also discussed by means of the immersion test. Inspired by non-wettable structures found in nature, the effects of features such as reentrant cavities, micropillars, and overhanging layers are studied. We show that a densely populated array of small diameter cavities exhibits superior non-wettability, with 65% of the cavities remaining intact after 24 h of full immersion in water. In addition, it is suggested that the wetting transition time is influenced by the length of the overhanging layer as well as by the number of columns within the cavity. Our findings indicate a non-wetting performance that is three times longer than previously reported in the literature for a small, densely populated design with cavities as small as 10 μm in diameter. Such properties are particularly beneficial for neural implants as they may reduce the interface between the body fluid and the solid state, thereby minimiing the inflammatory response following implantation injury. In order to assess the effectiveness of this approach in reducing the immune response induced by neural implants, further in vitro and in vivo studies will be essential. Full article

In the present study, zeolite X (FANaX) was synthesized from coal fly ash (FA) by a two-step high-temperature method. In order to follow the effect of different contaminants in the starting coal ash, zeolite X was also synthesized from pure chemicals according to a classical recipe (NaX). Iron was loaded on this reference zeolite with the amount which was contained in the coal FA. The final catalytic samples were obtained by wet impregnation of Pt nanoparticles on both types of zeolite crystals. The most active samples in the benzene oxidation were the platinum-modified ones and, among them, the Pt-impregnated FA zeolite (Pt FANaX). The comparison of the catalytic activity of Pt FANaX with the reference PtFe NaX zeolite showed a temperature difference of 10 °C in favor of Pt FANaX at 50% benzene conversion. From these results, it can be concluded that FA zeolites are a good, cheaper and environmentally friendly alternative to traditional zeolites, synthesized from pure chemicals, which can be applied in the preparation of catalysts for the purification of gaseous mixtures from harmful organic compounds. Full article

An understanding of the proper application of metalworking fluids (MWFs) is necessary for their implementation in efficient production processes. In addition, the knowledge of the process-related aspect of chip transport and the macroscopic cooling effect, the characteristics and properties of lubricant film formation, and the cooling conditions in the secondary shear zone on the chip surface, i.e., in the direct vicinity of the material separation, represent a combined fundamental scientific issue within production engineering. The aim is to transfer methods from the field of tribology of machine elements, which have already led to a considerable gain in knowledge in this discipline, to machining and to couple them with already established approaches to machining. In the case of roller bearings, the contact pressure is in the range as the pressure in the contact zone between the cutting insert and chip. Due to this, established methods might be transferred to the cutting process. In addition to classical pin-on-plate and pin-on-ring friction investigations, film thickness measurements were carried out and compared to machining tests. The coefficient of friction determined in the planing test rig is 0.48 for dry cutting, while it is 0.47 for wet cutting. These two values are much larger than the CoF with MWFs measured on the two tribometers. It is shown that the boundary friction of MWF especially influences the machining process. Thus, additives in MWF might have a high significance in machining. Full article

The formation of oxalates in soils and rocks under conditions of cryoarid climate, permafrost and taiga vegetation was studied. Whewellite and weddellite were found in four areas associated with the mining industry: on the kimberlite deposit of the Daldyn territory, in the lower reaches of the Markha River of the Central Yakut Plain, and on the coastal outcrop of the Allah-Yun Sellah-Khotun ore cluster. Whewellite was found in the upper organic horizon of Skeletic Cryosol (Thixotropic) (sample 151) and as a film on the surface of plant remains of Humic Fluvisols (sample 1663). Weddellite was found as an extensive encrustation on the surface of the soil and vegetation cover of Stagnic Cryosols Reductaquic (sample 984) and on a siltstone outcrop (sample KM-6-21). Calcium oxalates were identified by X-ray phase analysis, photographs of the samples were taken on a polarizing microscope, and the crystal morphology was studied on a scanning electron microscope. To determine the chemical composition of soils and rocks, the classical wet-chemical method was used; the physical properties of the studied samples were studied using a pH meter, the photoelectric colorimetric method, and a synchrotron thermal analysis device. The source of calcium for the formation of salts is the parent layers of the studied soils, represented by carbonate and carbonate clastic rocks, which cause neutral and slightly alkaline environments. High humidity, which is provided by the seasonal thawing of the permafrost, has a key role in the formation of the studied oxalates in Yakutia with a sharply continental cryoarid climate. Based on the studies, it was found that the first two samples are the products of lichen activity, and the third and fourth are at the stage of initial soil formation by micromycetes. In addition, the formation of these oxalates, in our opinion, is the result of the protective function of vegetation, in the first two cases, with a sharp increase in the load on lichens under technogenic impact, and in the second and third cases, when favorable conditions arise for initial soil formation, but under conditions of toxic content of heavy metals and arsenic. Full article