Search Results (1,804)

Date palm (Phoenix dactylifera L.) by-products from bioethanol production represent an underutilized resource rich in bioactive molecules. This study aims to their valorization through characterization of polysaccharides and phenolic compounds from the Medjool variety, both before and after yeast fermentation for bioethanol production. Three sequential types of by-products were analyzed—Ext1 (post hot-extraction), Ext2 (post fermentation), and Ext3 (post distillation)—and compared with Dat-Me. High Performance Liquid Chromatograp-Diode Array Detector-Mass Spectrometry (HPLC-DAD-MS) analysis allowed identifying 22 phenolic compounds, primarily cinnamic acid derivatives and glycosylated flavones such as luteolin and chrysoeriol. Fermentation increased total phenolic content from dry weight, while leading to an improved polysaccharide recovery (i.e., from 14.2% to 42.1% dry weight). Two polysaccharide fractions (F1 and F2) were isolated and characterized by ¹H-NMR and Dynamic Light Scattering (DLS). F1 is a pectic polysaccharide, with a galacturonic acid content ranging from 24.2% (Ext3) to 52.2% (Dat-Me), a degree of methylation (DM) between 34.4 and 50.6%, and a degree of acetylation (DA) of 23.6–42.2%. F2 consists of a non-pectic polysaccharide, characterized by a low galacturonic acid content (5.6–6.8%) and a DM of 12.6–47.1%, but it is highly acetylated, with a DA ranging from 90.1 to 93.3%. DLS analysis confirmed fermentation-induced depolymerization, with molecular weights ranging from 6.6 × 10⁴ to 8.5 × 10⁵ KDa for both the fractions. Overall, Medjool date by-products obtained after bioethanol production represent a sustainable source of high-value phenolic antioxidants and polysaccharides with different structures suitable for future applications in food, pharmaceutical, and cosmetic formulations. Full article

We report, for the first time, waimirite-(Y) in Egypt. This is only the third reported occurrence of this mineral in the world. This observation arose during our study of the rare earth element (REE) mineralization associated with the Neoproterozoic rare-metal granite intrusion in Wadi Abu Rusheid in the Eastern Desert of Egypt. The principal lanthanide and yttrium (Y) hosts in the area are waimirite-(Y) and bastnäsite-(Ce) in leucogranite and bastnäsite-(Y) in adjacent metasedimentary country rock. The leucogranite is a strongly fractionated, metaluminous to weakly peraluminous (A/CNK = 0.98–1.03), medium- to high-K calk-alkaline I-type granite. The metasediments are composed of upper greenschist to lower amphibolite-grade biotite schists with variable amounts of amphibole, graphite, and garnet. Leucogranite contains accessory Li-bearing mica, garnet, zircon, fluorite, and columbite in addition to the REE minerals. It is enriched by three orders of magnitude relative to primitive mantle in Li, Rb, Th, Ta, Nb, Pb, U, and Sn; relative to these highly enriched elements the concentrations of Sr, Ba, Ga, Zr, Hf, and Y are notably low. The REE patterns of most samples show strong enrichment in heavy relative to light REE but occasional samples have light REE-enriched patterns controlled by accessory REE minerals, and all display strong negative Eu anomalies (Eu/Eu* ≤ 0.05). The whole-rock chemistry of the metasedimentary units are different; relative to average upper continental crust they show enrichments of one to two orders of magnitude in Li, Rb, Pb, Sn, Cs, and sometimes Cr and Zn. The REE patterns of the metasedimentary units are nearly flat, with some samples showing negative Eu anomalies. Waimirite-(Y), nominally YF₃, also contains several weight percent each of Yb, Dy, and Er. The empirical formula (based on one cation) is (Y_0.55Ce_0.02Pr_0.01Nd_0.02Sm_0.02Gd_0.02Dy_0.05Er_0.04Yb_0.05Th_0.05Ca_0.16Pb_0.01)_∑1.00(F_2.48O_0.52)_∑3.00. Bastnäsite-(Ce) in leucogranite samples, nominally Ce(CO₃)F, also has several weight percent each of Nd₂O₃ and La₂O₃. The REE host in metasedimentary rocks is bastnäsite-(Y), nominally Y(CO₃)F, but also rich in Nd₂O₃ (11–19 wt.%) and La₂O₃ (4–14 wt.%). It is intimately associated with fluorophlogopite. The geochemical, mineralogical, and textural evidence indicates that waimirite-(Y) and bastnäsite-(Ce) in leucogranite crystallized from granite-derived F- and CO₂-bearing hydrothermal fluids, whereas the source of Y for growth of the bastnäsite-(Y) in the metasedimentary rocks is unclear; the large negative Ce anomaly in bastnäsite-(Y) suggests an oxidizing supergene setting. Despite their proximity, if there is a genetic connection between the mineralization in the granite and in its country rocks, the relationship is not evident from elemental patterns or host mineralogy. Full article

The primary objective of this study is to investigate the effect and mechanism of replacing limestone powder with red mud as a filler on asphalt aging resistance. The microstructure and porosity characteristics of limestone powder, Bayer process red mud, and sintered red mud were analyzed. Asphalt mastics were then prepared using these fillers. The effect of red mud on the aging resistance of asphalt was evaluated by comparing the conventional physical properties, rheological behavior, and functional groups of the asphalt mastics before and after aging. Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography (GPC), and ultraviolet-visible spectroscopy (UV-Vis) were further employed to elucidate the underlying anti-aging mechanisms. The results indicate that the asphalt mastic containing 4% sintered red mud exhibits the strongest resistance to both thermo-oxidative and UV aging. It shows the lowest increments in softening point, viscosity aging index, and complex modulus aging index, with performance comparable to a commercial anti-aging agent. FTIR and GPC analyses reveal that sintered red mud selectively adsorbs light asphalt components, thereby inhibiting their conversion into heavier fractions during thermo-oxidative aging. UV-vis analysis demonstrates that sintered red mud provides effective UV shielding within the asphalt mastic, substantially mitigating UV-induced damage. In summary, the incorporation of 4% sintered red mud can significantly delay both thermo-oxidative and UV aging processes in asphalt mastics, thereby effectively enhancing the aging resistance of asphalt pavement. Full article

Due to climate change, seedling damage caused by drought stress is expected to increase in both afforestation sites and nurseries. Therefore, to ensure stable seedling production under high-temperature conditions and to cultivate seedlings with enhanced drought tolerance through hardening treatments, the development of an effective irrigation system is required. Conventional physiological methods for non-destructive drought detection, such as chlorophyll fluorescence and leaf temperature measurements, require expensive and manual operation, thereby limiting their real-time applicability in forest nurseries. This study evaluated the applicability of using image-based leaf angle measurements for drought stress detection in Quercus acutissima Carruth. seedlings. One-year-old seedlings were grown under two water regimes—well-watered (CT: control) and unwatered (DT: drought)—through Day 8. Statistical analyses (RMANOVA) revealed that changes in the leaf angle parameter PMD–MD (the difference between the previous and current measurement days) showed treatment effects similar to those of the physiological responses ΦNO (quantum yield of non-regulated energy dissipation) and qL (fraction of open PSII reaction centers) to drought on Day 6. Leaf angle reflected drought stress but did not precede physiological changes, indicating its role as a complementary rather than an early indicator. Multiple regression models identified AT (air temperature), SM (soil moisture), Fm′ (maximum fluorescence in the light-adapted state), and VPD (vapor pressure deficit) as the main factors influencing leaf angle variation. Although leaf angle was affected by combined environmental stresses such as high temperature, it was less sensitive to heat stress than physiological responses based on RMANOVA results. These results indicate the potential of image-based leaf angle measurements for drought stress detection. To establish plant-based smart irrigation systems, future studies should validate and refine this approach using larger datasets. Full article

This work investigates the fabrication and performance of axially compressed isotropic epoxy-bonded NdFeB-type magnets produced from melt-spun powders with partial substitution of (Nd,Pr) by (La,Ce). Four alloy compositions were synthesized and processed into bonded magnets using two powder-to-binder weight ratios (95:5 and 96.5:3.5). Structural analysis confirms that all substituted alloys retain the tetragonal Nd₂Fe₁₄B phase (up to ~95 wt%) even at high substitution levels, while the lattice parameters decrease slightly with increasing (La,Ce) content. Microscopy analysis confirms a homogeneous distribution of the binder phase around the powder particles, demonstrating uniform binder–powder integration. Thermal analysis reveals composition-dependent Curie temperatures and enhanced crystallization onset in highly substituted powders. Magnetic measurements on both powders and bonded magnets show that increasing substitution leads to a gradual reduction in remanence, coercivity, and energy product, though all samples maintain strong hard-magnetic behavior. Increasing the powder fraction to 96.5 wt.% significantly improves all magnetic parameters due to higher magnetic-phase density and enhanced interparticle coupling, yielding bonded magnets with densities up to ~80% of the theoretical value. The resulting magnets achieve competitive performance, uniform field distribution and isotropic magnetization with (BH)max values about 65 kJ/m³, a coercivity around 660 kA/m, and superior thermal stability compared with commercial bonded NdFeB magnets. Overall, partial substitution with light rare-earth elements (La,Ce) provides a cost-effective route to high-density bonded NdFeB magnets that combine strong magnetic performance, enhanced thermal stability, and suitability for lightweight, complex-shaped industrial applications. Surprisingly, the coefficients of the temperature variation of coercivity and (BH)_max are much better compared to the commercial NdFeB bonded magnets. Full article

Mitigating climate change necessitates a thorough understanding of soil organic carbon (SOC) decomposition and its response to warming. The overuse of synthetic fertilizers can alter SOC composition and affect carbon cycling, potentially changing the temperature sensitivity (Q₁₀) of SOC decomposition. This study evaluated the Q₁₀ of SOC decomposition after long-term (37-year) excessive fertilization in a loess soil. Four treatments were compared: control (no nutrient input, CK); recommended rates of synthetic nitrogen (N) and phosphorus (P) fertilizers (CF_r); excessive rates of N and P fertilizers (CF_h); and CF_h plus organic manure (MCF_h). The Q₁₀ of SOC decomposition was investigated via an incubation experiment at temperatures of 15 °C, 25 °C and 35 °C within 63 days. Compared with CF_r, long-term CF_h and MCF_h significantly increased SOC contents by 14% and 67%, and this increase was driven primarily by rises in mineral-associated organic carbon (MOC) of 28% and 62% and particulate organic carbon (POC) of 32% and 79%, respectively, under CF_h and MCF_h. While CF_h and MCF_h did not change the SOC composition, they increased the proportions of fine POC (fPOC) to SOC by 10% and 91%, and the ratio of light POC to SOC by 78% and 143%, respectively. Q₁₀ values ranged from 2.18 to 3.00 across all treatments, with a mean of 2.64. Both CF_h and MCF_h drastically enhanced the Q₁₀ values by 38% and 25% compared with CF_r at 15–25 °C. However, MCF_h significantly decreased the Q₁₀ value by 31% relative to CF_r at 25–35 °C. Partial least squares path modeling showed that soil physicochemical properties and labile carbon fractions differed significantly among treatments, with physical properties regulating labile carbon fractions. Fertilization significantly increased the Q₁₀ value at 15–25 °C owing to increased proportion of labile carbon fractions and decreased labile carbon content. Our results suggest that SOC gains from continuous addition of synthetic fertilizers are vulnerable to loss under warming. However, this loss could be alleviated by incorporation of organic manure. Thus, integration of organic manure into nutrient management practices could be an efficient way to counteract warming-induced SOC decomposition. Full article

Gluten proteins are key components in wheat flour that determine the formation of dough and the quality of flour-based products. Upon hydration and mixing, gluten proteins undergo complex structural transformations to form a gluten network, exhibiting a hierarchical multi-scale structure spanning molecular, aggregate, and network scales. Due to the extreme complexity of gluten proteins, accurately characterizing their multi-scale structures remains challenging, requiring the combined application of multiple techniques, which are still relatively limited and thus warrant further exploration. Therefore, this review presents the principles, operational details, and result presentations of current techniques at different structural scales, including electrophoresis, high-performance liquid chromatography, proteomics, Fourier transform infrared spectroscopy, and Fourier transform Raman spectroscopy at the molecular scale; size-exclusion chromatography, asymmetrical flow field-flow fractionation, dynamic light scattering, multi-angle light scattering, differential refractive index, and ultraviolet absorbance at the aggregate scale; and confocal laser scanning microscopy, scanning electron microscopy, confocal Raman microscopy, and two-photon excitation microscopy at the network scale, among others. It further compares the advantages and disadvantages of similar techniques, facilitating their scenario-based selective utilization. Finally, it outlines the ongoing challenges and future perspectives for the development and application of techniques for the multi-scale structural characterization of gluten proteins. Full article

Glass is a highly recyclable material that provides substantial environmental benefits, including savings in raw materials and energy as well as a reduction in CO₂ emissions. To ensure the production of high-quality secondary raw materials, container glass from municipal waste separate collection must be accurately separated by color in recycling plants, where only minimal color mixing is tolerated. Color sorting is therefore a key step in glass recycling, as it directly affects both the quality and the market value of recycled cullet. Given the increasingly stringent color quality requirements for recycled glass and the high fraction of cullet used in container glass, advanced technological solutions are needed to improve sorting accuracy. In this study, a visible–near-infrared (VIS-NIR: 400–1000 nm) hyperspectral imaging (HSI) approach integrated with machine learning (ML) is proposed for the automated classification of post-consumer glass fragments from bottles and jars into five color categories: brown, dark green, light green, half-white and white. A hierarchical Partial Least Squares-Discriminant Analysis (PLS-DA) model combined with an object-based analysis strategy was developed to optimize color recognition. The proposed system achieved sensitivity and specificity values between 0.910 and 1.000, demonstrating excellent robustness and predictive capability. Validation on independent datasets confirmed the model’s reliability, with all color glass fragments correctly classified at the object level. The results highlight the potential of HSI-ML systems to enhance color sorting accuracy and process efficiency in recycling plants, contributing to improved material recovery and the advancement of sustainable, circular glass production. Full article

Table grapes are commonly consumed fresh, and their market value is largely determined by ripeness, grape color, mineral composition, and variety-specific aroma. This study integrated physicochemical ripening indicators (°Brix%, pH, titratable acidity, maturity index), CIELAB color parameters measured on the outer skin and inner sections, multi-element mineral profiling following microwave-assisted digestion (ICP-MS), and volatile organic compound (VOC) profile by HS-SPME/GC-MS to characterize five table grape varieties (Thompson Seedless, Isabella, Mevlana, Pepita Alfonso, and Red Globe). Significant differences in ripeness were found among the varieties (p < 0.01). Isabella had the highest soluble solids content (22.91 °Brix%), while Pepita Alfonso had the highest maturity index (79.89) and the lowest titratable acidity (0.22%). Color measurements also showed significant differences among the varieties (p < 0.01). Thompson Seedless exhibited the highest peel lightness/yellowness and chroma values, while Pepita Alfonso and Red Globe had a darker, lower chroma profile. Color index values differed between the peel and the inner cross-section depending on the variety (p < 0.01). Mineral composition was found to be variety-specific (p < 0.01). The dominant macroelements among the samples were K, P, and Mg, and statistically significant differences were also determined in trace elements (p < 0.01). A total of 42 volatile organic compounds (VOCs) were identified. Aldehydes were dominant in the volatile fraction (39.07–64.96%), nonanal contributed a significant percentage, and terpenoids (floral aroma note) were found in the highest percentage in the Isabella variety (28.87%). PCA applied to the integrated physicochemical, color, and mineral dataset enabled the clear discrimination of the five table grape cultivars. Pepita Alfonso was positioned toward positive PC2, and Red Globe occupied the opposite segment. Thompson Seedless and Isabella were separated mainly along PC1, while Mevlana showed an intermediate profile. SIMCA class-distance results confirmed the visual separation. All pairwise interclass distances were above the decision threshold (ICD > 3), ranging from 62,922 (Red Globe–Mevlana) to 806,425 (Isabella–Pepita Alfonso). Findings indicated robust cultivar-level classification for authenticity and quality control purposes. Overall, the integrated multi-domain approach is considered to provide a solid foundation for variety differentiation and quality-oriented harvesting and market management. Full article

Vegetation physiological processes are critical regulators of terrestrial carbon–water cycles and local microclimate dynamics, with photosynthetically active radiation (PAR, 400–700 nm) serving as a primary driving force. However, most vegetation–climate process models simplify the fraction of PAR in global solar radiation as a constant 50%, potentially introducing diurnal simulation biases that propagate into cumulative annual errors in vegetation carbon–water coupling estimates. To address this limitation, we first evaluated the performance of three empirical models for simulating the dynamic PAR fraction and integrated the most accurate model into the Vegetation Microclimate Process (VMcP) model, and further used typical meteorological year (TMY) data of Beijing, Shanghai and Shenzhen as input to compare the differences in vegetation carbon–water processes before and after the improvement. The results show that the diurnal variation range of PAR fraction in global solar radiation is between 39% and 58%. The existing models that neglect the dynamic changes in PAR may overestimate vegetation transpiration cooling and photosynthetic carbon sequestration by 2.3% and 3.5%, respectively. Meanwhile, Shenzhen (64.3 W/m²; 1.59 g/m²·d), characterized by favorable light and thermal conditions, is more prone to large errors compared with Shanghai (47.6 W/m²; 1.21 g/m²·d) and Beijing (39.5 W/m²; 0.93 g/m²·d). This study provides a novel tool for the accurate assessment of vegetation-mediated microclimate improvement, and offers a new perspective for nature-based climate solutions. Full article

Amphiphilic copolymers based on polystyrene and polyglycidol combine the chemical inertness of polystyrene with the biocompatibility of polyglycidol, making them attractive materials for polymeric micelles. While comb-like architectures have been explored to control micellization behavior and biological response, a direct comparison between comb-like and coil-comb topologies in polystyrene–polyglycidol copolymers at identical polyglycidol content remains insufficiently investigated. In this work, amphiphilic comb-like and coil-comb polystyrene–polyglycidol copolymers were synthesized via copper-catalyzed azide–alkyne click chemistry by grafting a monoalkyne-terminated polyglycidol precursor onto azide-functionalized random and block styrene copolymers. The copolymers were characterized by size exclusion chromatography and nuclear magnetic resonance. Polymeric micelles were prepared by nanoprecipitation, and their self-assembly in aqueous solution was investigated by critical micelle concentration determination, dynamic and electrophoretic light scattering, and atomic force microscopy. Both copolymers formed stable aqueous dispersions and exhibited comparable critical micelle concentrations. At identical polyglycidol content, the random copolymer formed a uniform, monomodal micellar population, whereas the block-based coil-comb architecture led to bimodal size distributions, indicating the coexistence of two distinct micellar populations. The investigated systems showed low cytotoxicity and did not induce significant oxidative stress within the studied concentration range. On isolated rat brain sub-cellular fractions (synaptosomes, mitochondria and microsomes), administered alone, the comb-like and coil-comb polystyrene-polyglycidol copolymers did not reveal statistically significant neurotoxic effects. The results demonstrate that macromolecular architecture plays a key role in governing micellar organization and in vitro biological response in polystyrene–polyglycidol copolymers, highlighting their potential as architecture-controlled polymer-based nanocarriers. Full article

In this work, the kinetics of the redistribution of oils, resins, and asphaltenes in high-viscosity oil from the Karazhanbas field (Republic of Kazakhstan) were investigated. This was achieved with an ultrasonic treatment (22 kHz, 50 W) in the presence of a zeolite catalyst (1.0 wt%). The parameters of the technological process were established as a temperature range from 30 to 70 °C and an exposure time of 3 to 11 min. This allowed us to increase the oil content by 14.8% and decrease the concentration of resins by 12.2% and asphaltenes by 2.6%. Conversion schemes (“oils ↔ resins” and “resins ↔ asphaltenes”) were developed, which made it possible to determine the main direction of the reaction processes. The most rapid process is the conversion of resins to oils (k₂ = 0.1148–0.1860 min⁻¹). The process of the cracking of asphaltenes with the formation of resins (k₄ = 0.1023–0.1413 min⁻¹) ranks second in rates. Condensation reactions, including the transition of oils to resins (k₁ = 0.0175–0.0252 min⁻¹) and resins to asphaltenes (k₃ = 0.0139–0.0194 min⁻¹), occur significantly more slowly. The calculated activation energies (7.0–10.4 kJ/mol) show that the cavitation treatment of high-viscosity oil in the presence of a catalyst effectuates the processing of heavy oil with minimal energy consumption. A group composition analysis of the light and middle oil fractions demonstrated an increase in paraffinic, naphthenic, benzenic, and olefinic hydrocarbons, with a simultaneous decrease in naphthalenes and heteroatomic compounds. The results obtained confirm the effectiveness of ultrasonic–catalytic treatment for the structural cracking of high-viscosity oil and the formation of lighter hydrocarbon fractions. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Nucleation remains one of the least understood steps during protein crystallization, although it strongly impacts product quality attributes, including total crystal numbers, final crystal size distributions, and thus downstream processing. In this work, the nucleation behavior of Lactobacillus brevis alcohol dehydrogenase (LbADH) wild type (WT) and five mutants (Q207D, Q126H, K32A, D54F, and T102E) is investigated in a stirred 7 mL crystallizer monitored by in situ multi-angle dynamic light scattering (MADLS). Nucleation was studied with highly pure homotetrameric LbADHs by establishing a crystallization, lyophilization, and re-solubilization protocol combined with size exclusion chromatography (SEC) and size exclusion high-performance liquid chromatography (SE-HPLC), yielding tetramer purities above 94% and removing low molecular weight impurities. During stirred batch crystallizations initiated by the addition of polyethyleneglycol 550 monomethyl ether (PEG 550 MME), SEC and SE-HPLC revealed decreasing tetramer peak areas but essentially constant peak apex positions, indicating that no long-lasting oligomeric intermediates accumulate at detectable levels. Time-resolved MADLS measurements using a custom-made flow-through cuvette in a bypass to the stirred crystallizer uncovered transient cluster populations. All protein variants exhibited an initial tetramer peak, followed by the formation of larger aggregates and a rapid rise in signal above a hydrodynamic diameter of 1000 nm, coinciding with the onset of macroscopic turbidity. A simple mesoscale nucleation model was formulated, yielding end-of-nucleation times, crystallized fractions, critical soluble concentrations, and apparent nucleation rate constants. The crystal contact mutations modulate both the timing and magnitude of the nucleation burst (rapid build-up of nuclei/cluster populations). The mutant Q207D showed strongly attenuated nucleation compared to the WT, whereas the other mutants (K32A, D54F, and particularly T102E) display markedly accelerated nucleation at nearly invariant critical concentrations. The combined workflow demonstrates how in situ MADLS, together with a tailored kinetic description, can provide mechanistic insight into protein nucleation in stirred batch crystallizers. Full article

Content/Background: Breast cancer is one of the most fatal cancers among women around the globe. The chances of surviving this cancer increase with early tumor detection, which is necessary for effective treatment. Traditional diagnostic techniques are ineffective and time-consuming, and they yield results that may be accurate or inaccurate. Therefore, our primary objective is to determine how a machine learning model can reduce diagnostic errors and provide accurate results. Objective: The main objective of this project is to build an ML-based classification model that can help doctors detect breast cancer early and more accurately. This project also aims to provide an interactive interface for easy access in healthcare settings. Materials/Methods: For this study, twelve machine learning classification algorithms are implemented and tested: Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, XGBOOST, Naive Bayes, AdaBoosting, Light GBM, CatBoost, and the Artificial Neural Network (ANN). This study used the Wisconsin Breast Cancer Dataset (WBCD) from the UCI ML Repository. It contains 569 patient samples and 30 features. This dataset has the following features: Radius, Texture, Area, Perimeter, Smoothness, Compactness, Concavity, and Fractional Dimension. The target variable is diagnosis, which is categorized as malignant vs. benign. Results: The fifteen models were analyzed, evaluated, and compared using five performance metrics: Accuracy, Precision, Recall, F1-Score, and AUC-ROC. Among the evaluated models, CatBoost, LoGR, and AdaBoost outperformed the others, with an Accuracy of 97.%, Precision of 97%, Recall of 97%, and AUC-ROC score of 99%. The AUC-ROC is nearly 99%, and the model has a high ability to differentiate between malignant and benign tumors. Full article