Search Results (1,942)

Clay minerals and other flocculants are used to mitigate the effects of some species that produce harmful algal blooms due to their physical and chemical characteristics. In this study, we applied calcium bentonite clay (Bca) and zeolite (Ze) to flocculate and remove cells of the dinoflagellate Gymnodinium catenatum (Graham), a producer of paralyzing toxins. The flocculants were characterized by scanning electron microscopy (SEM) in combination with an energy-dispersive X-ray spectroscopy (EDS) microanalysis system. During experiments, Bca and Ze were suspended in distilled water, deionized water, and seawater at concentrations of 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, and 4.0 gL⁻¹. The percentage of removal efficiency (RE%) of biomass indicators of G. catenatum was calculated. The cell number and concentration of chlorophyll a and peridinin were analyzed using high-performance liquid chromatography (HPLC-UV and HPLC-DAD). The external effects on cells of G. catenatum were recorded. As a result, the maximum RE% of Bca was 79% with respect to the total number of cells, chlorophyll a was 69% and peridinin of 73%. The RE% of Ze was less than 40%. In the matrix of sedimented Bca, malformation of cells was observed, inhibiting their swimming, as well as death and rupture of cells with temporary cyst formation after 72 h. We conclude that Bca, suspended in deionized and distilled water, was more efficient in flocculating cells of G. catenatum. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

The Permian Xiashihezi Formation in the Ordos Basin is a typical tight sandstone gas reservoir, which is characterized by low porosity and strong heterogeneity. Diagenesis plays a crucial role in controlling reservoir quality. However, the multiple phases and types of diagenetic processes throughout geological history make the compaction mechanisms highly complex. This study employed a high-temperature and high-pressure diagenesis simulation system to conduct geological simulation experiments. Typical reservoir samples from the 2nd Member of the Permian Xiashihezi Formation were selected for these simulations. The experiments replicated the diagenetic evolution of the reservoirs under various temperature, pressure, and fluid conditions, successfully reproducing the diagenetic sequences. The diagenetic sequence included early-stage porosity reduction through compaction, early carbonate cementation, quartz overgrowth, chlorite rim formation, feldspar dissolution, and late-stage illite and quartz cementation. Mechanical compaction is the primary factor reducing reservoir porosity, exhibiting a distinct four-stage porosity reduction pattern: (1) continuous burial stage (>4000 m); (2) stagnation stage of burial (3900 m–4100 m); (3) the secondary continuous burial stage (>5000 m); (4) tectonic uplift stage (3600 m). The experiments confirmed that the formation of various authigenic minerals is strictly controlled by temperature, pressure, and fluid chemistry. Chlorite rims formed in an alkaline environment enriched with Fe²⁺ and Mg²⁺ (simulated temperatures of 280–295 °C), effectively inhibiting quartz overgrowth. Illite appeared at higher temperatures (>300 °C) in platy or fibrous forms. Feldspar dissolution was noticeable upon injection of acidic fluids (simulated organic acids), providing material for authigenic quartz and kaolinite. The key mineral composition significantly impacts reservoir diagenesis. The dissolution released Mg²⁺ and Fe²⁺ ions, crucial for forming early chlorite rims in the overlying sandstones, confirming the importance of inter-strata interactions in “source-facies coupling.” Through physical simulation methods, this study deepened the understanding of the diagenetic evolution and compaction mechanisms of tight sandstones. This provides significant experimental evidence and theoretical support for predicting “sweet spot” reservoirs in the area. Full article

Rejuvenating aged asphalt is critical for sustainable road construction and resource utilization. This paper systematically reviews the current research on rejuvenators, focusing on their classification and the micro-, and macro-properties of rejuvenated asphalt. Rejuvenators are categorized into mineral oil-based, bio-based, and compound types. Each type offers distinct advantages in recovering the performance of aged asphalt. Mineral oil-based rejuvenators primarily enhance low-temperature cracking resistance through physical dilution, while bio-based rejuvenators demonstrate superior environmental sustainability and stability. Compound rejuvenators, particularly those incorporating reactive compounds, show the best results in repairing degraded polymer modifiers and improving both low- and high-temperature properties of aged, modified asphalt. Atomic Force Microscopy (AFM), Fluorescence Microscopy (FM), and Scanning Electron Microscopy (SEM) have been applied to analyze the micro-properties of rejuvenated asphalt. These techniques have revealed that rejuvenators can restore the microstructure of aged asphalt by dispersing agglomerated asphaltenes and promoting molecular mobility. Functional groups and molecular weight changes, characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Gel Permeation Chromatography (GPC), indicate that rejuvenators effectively reduce oxidation products and molecular weight of aged asphalt, restoring its physicochemical properties. Macro-property evaluations show that rejuvenators significantly improve penetration, ductility, and fatigue resistance. Finally, this review identifies the key characteristics and challenges associated with rejuvenator applications and provides an outlook on future research directions. Full article

To date, the grinding behavior of saprolite and lateritic overburden mixtures remains poorly understood. The Bond Work Index (BWI) is the principal indicator used to determine the specific energy consumption during the grinding process. To establish the F₈₀ and P₈₀ values, granulometric distribution models—Rosin–Rammler (RR), Gates–Gaudin–Schuhmann (GGS), and the Swebrec function (SWEF)—were evaluated. The mineral phases of the feed samples were analyzed by X-ray powder diffraction. This study provides evidence that the RR function is the most suitable for simulating the particle size distribution of the feed material, with residual errors below 6.30% and a coefficient of determination (R²) exceeding 97%. After the grinding equilibrium cycle is reached, the SWEF model proves to be the most appropriate, exhibiting residual errors under 3.50% and R² values above 98%. BWI reveals that saprolite is the most difficult ore to grind, with specific energy consumption increasing from 16.38 kWh/t to 25.50 kWh/t as the proportion of saprolite in the mixture rises. This reflects a clear upward trend, as confirmed by a fitted model with an R² of 98.54%. In contrast, the grindability index (Gbp) decreases, indicating that the material becomes increasingly resistant to grinding as the saprolite content increases. This may be attributed to inherent material properties, such as hardness, or to physical phenomena related to fragmentation. The declining Gbp further suggests that greater energy input is required to achieve additional particle size reduction. Overall, the findings demonstrate that saprolite is inherently difficult to grind and behaves according to its own grinding characteristics, regardless of whether it is processed alone or in combination with lateritic overburden. Full article

Background: Bone health in youth is influenced by both modifiable factors, such as nutrition and physical activity, and non-modifiable factors, such as biological maturation and heredity. Understanding how these elements interact to predict body composition may enhance the effectiveness of early interventions. Importantly, the integration of both supervised and unsupervised machine learning models enables a data-driven exploration of complex relationships, allowing for accurate prediction and subgroup discovery. Methods: This cross-sectional study examined 94 male athletes during the developmental period. Anthropometric, performance, and nutritional data were collected, and bone parameters were assessed using dual-energy X-ray absorptiometry (DXA). Three supervised machine learning models (Random Forest, Gradient Boosting, and Support Vector Regression) were trained to predict Total Body-Less Head (TBLH) values. Nested cross-validation assessed model performance. Unsupervised clustering (K-Means) was also applied to identify dietary intake profiles (calcium, protein, vitamin D). SHAP analysis was used for model interpretability. Results: The Random Forest model yielded the best predictive performance (R² = 0.71, RMSE = 0.057). Weight, height, and handgrip strength were the most influential predictors. Clustering analysis revealed two distinct nutritional profiles; however, t-tests showed no significant differences in TBLH or regional BMD between the clusters. Conclusions: Machine learning, both supervised for accurate prediction and unsupervised for nutritional subgroup discovery, provides a robust, interpretable framework for assessing adolescent bone health. While dietary intake clusters did not align with significant differences in bone parameters, this finding underscores the multifactorial nature of skeletal development and highlights areas for further exploration. Full article

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece. Full article

Introduction: Direct pulp capping (DPC) aims to preserve the vitality of the dental pulp by placing a protective biocompatible material over the exposed pulp tissue to facilitate healing. There are several calcium-silicate materials that have been designed to promote mineralization and the regulation of inflammation. These have strong potential for the repair and regeneration of dental pulp. Among them, Biodentine (BD) and EndoSequence RRM Putty (ES) have been found to promote in vitro and in vivo mineralization while minimizing some of the limitations of the first-generation calcium-silicate-based materials. Theracal-LC (TLC), a light-cured, resin-modified calcium-silicate material, is a newer product with potential to improve the clinical outcomes of DPC, but existing studies have reported conflicting findings regarding its biocompatibility and ability to support pulpal healing in direct contact with the pulp. A comprehensive assessment of the biocompatibility and pulpal protection provided by these three capping materials has not yet been performed. Aim: We aimed to quantify the inflammatory response, dentin bridge formation, and material adaptation following DPC using three calcium-silicate materials: ES, BD, and TLC. Materials and Methods: DPC was performed on the maxillary first molar of C57BL/6 female mice. Maxilla were collected and processed at 1 and 21 days post-DPC. The early inflammatory response was measured 24 h post-procedure using confocal imaging of anti-Lys6G6C, which indicates the extent of neutrophil and monocyte infiltration. Reparative mineralized bridge formation was assessed at 21 days post-procedure using high-resolution micro-computed tomography (micro-CT) and histology. Lastly, the homogeneity of the capping materials was evaluated by quantifying voids in calcium-silicate restorations using micro-CT. Results: DPC using TLC induced less infiltration of Lys6G6C⁺ cells at 24 h than BD or ES. BD promoted higher volumes of tertiary dentin than TLC, but TLC and ES showed no significant differences in volume. No differences were observed in material adaptation and void spaces among the three capping materials. Conclusions: All three materials under investigation supported pulp healing and maintained marginal integrity. However, TLC induced a lower inflammatory response on day 1 and induced similar levels of tertiary dentin to ES. These observations challenge the common perception that resin-based capping materials are not suitable for direct pulp capping. Our findings underscore the need to balance biological responses with physical properties when selecting pulp capping materials to improve long-term clinical success. Full article

Crops nitrogen supply through the in situ mineralization of soil organic matter is a critical process for plant nutrition. However, accurately estimating the contribution of mineralization remains challenging. The complexity of biological, chemical, and physical processes in the soil, influenced by environmental conditions, makes it difficult to precisely quantify the amount of nitrogen available for crops. In this study, we created a database by collecting results from 121 mineralization monitoring experiments carried out between 2015 and 2021 on different experimental plots across Wallonia, Southern Belgium, and assessed the efficiency of predictive mineralization methods. The most impactful analytical parameters on in situ mineralization (ISM), determined using LIXIM program, appeared to be potentially mineralizable nitrogen (PMN) (r = 0.79). PMN, estimated by anaerobic soil incubation, also allowed the effective consideration of the after-effects of grassland termination and manure inputs. A multiple linear regression (MLR) combining PMN, POxC, pH, TOC:N, and TOC:clay significantly improved the prediction of soil nitrogen mineralization available for crops, achieving r = 0.87 (vs. r = 0.58 for the current method), while reducing dispersion by 41% (RMSE 56.35 → 33.13 kg N·ha⁻¹). The use of a more flexible Bootstrap Forest model (BFM) further enhanced performance, reaching r = 0.92 and a 50.8% reduction in dispersion compared to the current method (RMSE 56.35 → 27.76 kg N·ha⁻¹), i.e., about 16% lower RMSE than the MLR. Those models provided practical and efficient tools to better manage nitrogen resources in temperate agricultural systems. Full article

The current state of the art research, and latest engineering technology application in the synthesis of the aragonite crystalline phase of calcium carbonate is presented here. Aragonite crystalline products are highly valuable in selected industries, such as medical and personal care, and in food additives using MgCl₂ as a chemical inducer. The outcome of this literature review provides the outlook of the available research whitespace opportunity in optimizing the current process parameters and in ensuring that sustainable and economically feasible continuous production of aragonite products could be achieved. One of the major improvements proposed in this study is to investigate the methods of synthesizing aragonite crystalline particles using a continuous mineral carbonation reactor system and optimizing the operating parameters. An experimental design was established to identify all the main effects to maximize aragonite production. The three main effects investigated are the effect of feedstock or reactant concentration, the effect of reaction temperature, and the effect of reaction time towards aragonite yield in the final products synthesized. An optimized operating parameter for maximum aragonite yield at 95% purity was proposed at the reaction temperature T of 90 °C, reaction time t of 10 min, and feedstock ratio Mg-to-Ca of 0.4. Subsequently, the continuous reactor system was designed, operated, and tested for at least 50 h operation, where the lime CaO(s) feed was successfully converted into aragonite products with purity between 75 and 81%. The properties and quality of the aragonite produced were analytically characterized from the following laboratory methods which include the thermalgravimetric analysis, TGA; X-Ray Diffraction, XRD; scanning electron microscopy, SEM; and induction coupled plasma, ICP. TGA mass balance after decomposition suggests that 44% of the mass balance represents the weight of CO₂ sequestered in the aragonite crystalline carbonates. Hence, the aragonite crystalline carbonates can be labeled as a green product which sequesters 0.44 kg of CO₂ per 1 kg of precipitated aragonite products synthesized. Interestingly, SEM microscopy characterization results revealed that the aragonite precipitate has a physical morphology of needle-like shape with a good aspect ratio (length/diameter) AR of between 8.67 micron and 11.35 micron. The properties were found to be suitable for paper making fillers, medical, personal care, and food additive applications. Full article

Tight gas reservoirs, as important unconventional natural gas resources, face low recovery rates due to low porosity, low permeability, and strong heterogeneity. CO₂ Storage with Enhanced Gas Recovery (CSEGR) technology combines CO₂ geological storage with natural gas development, providing both economic and environmental benefits. However, the main controlling factors and influence mechanisms remain unclear. This study utilized the PR-EOS to investigate CH₄, CO₂, and natural gas physical properties, established a numerical simulation model considering CO₂ dissolution and geochemical reactions, and explored the influence of injection scheme, injection rate, production rate, and shut-in condition on CO₂ enhanced recovery and storage effectiveness through orthogonal design. Results show that CO₂ exhibits significant differences in compressibility factor, density, and viscosity compared to natural gas, enabling piston-like displacement. Intermittent injection slightly outperforms continuous injection in recovery enhancement, while continuous injection provides greater CO₂ storage capacity. The ranking of the significance of different influencing factors for enhanced oil recovery is as follows: injection rate > production rate > injection scheme > shut-in condition. For the effect of geological storage of CO₂, it is as follows: injection rate > injection scheme > production rate > shut-in condition. During gas injection, supercritical, ionic, and dissolved CO₂ continuously increase while mineral CO₂ decreases, with storage mechanisms dominated by structural and residual trapping. The study provides scientific basis for optimizing CO₂ flooding strategies in tight gas reservoirs. Full article

This study investigated the Yongning Fortress ruins in Taiyuan through a comprehensive analytical approach employing scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), laser particle size analysis, X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), and ion chromatography (IC). The research focused on elucidating the disturbance mechanisms and environmental impacts induced by the root systems of five representative herbaceous species on rammed earth structures. The results demonstrated distinct, species-specific disturbance patterns. Melica roots created three-dimensional network damage, Artemisia capillaris primarily caused deep root penetration, Fallopia aubertii exhibited coupled physical–chemical effects, Convolvulus arvensis induced shallow horizontal expansion damage, while Cirsium formed a heterogeneous structure characterized by dense taproots and loose lateral roots. Environmental conditions, particularly moisture content, significantly influenced disturbance intensity. All root activities led to common deterioration processes, including particle rounding, gradation degradation, and formation of organic–mineral composites. Notably, vegetation markedly altered soluble salt distribution patterns, with Cirsium increasing total salt content to 3.7 times that of undisturbed rammed earth (0.48%), while sulfate ion concentration (1.16 × 10⁻³) approached hazardous thresholds. The study established a theoretical framework linking plant traits, disturbance mechanisms, and environmental response, and proposed risk-based zoning strategies for preservation. These outcomes provide significant theoretical foundations and practical guidance for the scientific conservation of rammed earth heritage sites. Full article

This article presents the results of a comprehensive investigation into geopolymer composites synthesized from fly ash, incorporating ground asphalt derived from reclaimed road pavement and quartz sand. The primary objective of this study was to elucidate the influence of mixture composition on the mechanical, physical, and microstructural characteristics of the developed materials. The innovative aspect of this research lies in the integration of two distinct filler types—mineral (quartz sand) and organic-mineral (milled asphalt)—within a single geopolymer matrix, while preserving key performance parameters required for engineering applications, including compressive and flexural strength, density, water absorption, and abrasion resistance. The experimental methodology encompassed the characterization of the raw materials by X-ray diffraction (XRD), chemical composition analysis via X-ray fluorescence (XRF), and assessment of particle size distribution. Additionally, the produced geopolymer materials underwent density determination, compressive and flexural strength measurements, abrasion testing, and mass water absorption evaluation. The chemical composition was further examined using XRF, and the surface morphology of the specimens was analyzed by scanning electron microscopy (SEM). The findings demonstrate that the incorporation of quartz sand enhances the density and mechanical strength of the composites, whereas the addition of recycled asphalt, despite causing a modest reduction in mechanical performance at elevated dosages, augments water resistance. Moreover, ternary composite material provide an optimal compromise between mechanical strength and durability under humid conditions. Overall, the results substantiate the feasibility of utilizing asphalt waste for the fabrication of functional and sustainable geopolymer materials suitable for construction applications. Full article

Reliable information on the chemical and physical makeup of mine tailings is critical in meeting environmental and regulatory requirements, as well as identifying whether contained elements, including critical minerals, might be economically recovered in future to meet growing demands. Detailed mineralogical characterization, supported by chemical assays and automated mineralogy (MLA) data on different size fractions, underpins a case study of flotation tailings from the processing plant at the Carrapateena mine, South Australia. The study provides valuable insights into the deportment of minor and critical elements, including rare earth elements (REEs), along with uranium (U). REE-minerals are represented by major phosphates (monazite and florencite) and subordinate REE-fluorocarbonates (bastnäsite and synchysite). More than half the REE-minerals are concentrated in the finest size fraction (−10 μm). REEs in coarser fractions are largely locked in gangue, such that economic recovery is unlikely to be viable. MLA data shows that the main REE-minerals all display specific associations with gangue, which change with particle size. Quartz and hematite are the most common associations, followed by sericite. Synchysite shows a strong affiliation to carbonates. The contents of other critical elements (e.g., tungsten, molybdenum, cobalt) are low and for the most part occur within other common minerals as submicron-sized inclusions or in the lattice, rather than discrete minerals. Nevertheless, analysis of mine tailings from a large mining–processing operation provides an opportunity to observe intergrowth and replacement relationships in a composite sample representing different ore types from across the deposit. U-bearing species are brannerite (associated with rutile and chlorite), coffinite (in quartz), and uraninite (in hematite). Understanding the ore mineralogy of the Carrapateena deposit and how the ore has evolved in response to overprinting events is advanced by observation of ore textures, including between hematite and rutile, rutile and brannerite, zircon and xenotime, and the U-carbonate minerals rutherfordine and wyartite, the latter two replacing pre-existing U-minerals (uraninite, coffinite, and brannerite). The results of this study are fundamental inputs into future studies evaluating the technical and economic viability of potentially recovering value metals at Carrapateena. They can also guide efforts in understanding the distributions of valuable metals in analogous tailings from elsewhere. Lastly, the study demonstrates the utility of geometallurgical data on process materials to assist in geological interpretation. Full article

Depression, anxiety, and accompanying sleep disorders are common mood disorders worldwide, significantly affecting individuals’ physical and mental well-being as well as their quality of life. Coffee is a widely consumed beverage rich in bioactive compounds, including caffeine, polyphenols, tannins, proteins, and minerals, and studies suggest that coffee and its bioactive constituents demonstrate potential benefits for mental health. However, the specific mechanism by which coffee regulates depression, anxiety and sleep disorders is still unclear, and there is a lack of systematic research in this regard. This study searched four databases (PubMed, Embase, Web of Science, and Google Scholar) to summarize the effects of coffee and its active ingredients on depression, anxiety, and sleep disorders and their mechanisms. Keywords included “coffee”, “active ingredients”, “depression”, “anxiety”, “Sleep disorders “and “mechanisms”. This review summarizes 27 animal studies, 11 clinical studies, and 6 epidemiological studies on this topic. The results showed that moderate caffeine intake may improve mood and cognitive performance, while excessive intake may be associated with anxiety, insomnia, and post-traumatic stress disorder (PTSD). Other compounds, such as polyphenols, may contribute to mental health through antioxidant, anti-inflammatory, and neuroprotective mechanisms. Future research is needed to clarify effective dosages, assess long-term safety, improve coffee processing methods, and explore the development of coffee-based functional foods. Full article