Diverse Behaviors Research Articles

Nuclear morphological characterisation of lobular carcinoma variants: a morphometric study.

Lobular carcinoma (LC) of the breast exhibits diverse morphology and clinical behaviour. The pleomorphic variant (pLC) displays distinct cytonuclear features and aggressiveness compared to the classic variant (cLC). However, diagnosing pLC remains subjective. This study aims to refine LC's cytonuclear features, focusing on pLC. Whole slide images of 59 LCs, including both in situ (LCIS) and invasive (ILC) lesions, were analysed. Nuclear measurements, including nuclear size and variability, were scored using QuPath imageanalysis software. For comparison, selected features were scored in normal cells (n = 10) and pleomorphism score-matched invasive breast carcinoma (IBC) of NST type (n = 33). Additional visual assessment of the pleomorphic ILC (pILC) cohort (n = 90) was conducted for cytomorphological features characterisation. pILC demonstrated larger nuclear area and higher nuclear variability with abundance of cytoplasm than cILC. Compared to lymphocytes, pILC demonstrated a median area ranging from 2.7 to 4.7 times larger. Cut-off values for differentiating pILC from other ILC subtypes included median nuclear area > 48.2 μm2 and interquartile range (IQR) > 19.4, nuclear perimeter median > 25.2 μm and IQR > 5.3 and maximum diameter > 9.1 μm and IQR > 2.2. Multivariable logistic regression confirmed these parameters as independent predictors of pILC, with the maximum diameter being the most significant (P < 0.001). Visual assessment recognised two pILC subtypes: apocrine and non-apocrine. Apocrine variant showed nuclear roundness, pale vesicular chromatin patterns and prominent nucleoli, while non-apocrine variant exhibited greater nuclear size and shape variation. Objective nuclear measurements, combined with cytoplasmic and architectural features, provide a robust framework for diagnosing LC subtypes, improving diagnostic accuracy and reproducibility.

New Insights into Red and White Quinoa Protein Isolates: Nutritional, Functional, Thermal Properties

Quinoa (Chenopodium quinoa Willd.) seeds, renowned for their nutritional richness and balanced amino acid profile, offer promising potential as food ingredients. This study focused on extracting and characterizing the protein isolates from red and white quinoa varieties to evaluate their physicochemical and functional properties. Protein isolation involved alkaline solubilization and isoelectric precipitation, followed by characterization through amino acid analysis, phenolic profiling, scanning electron microscopy (SEM), zeta potential measurement, particle size distribution analysis, Differential Scanning Calorimetry (DSC), and rheological studies. The results showed that both the red and white quinoa protein isolates exhibited high protein content and essential amino acids, with notable differences in their amino acid compositions. The phenolic and flavonoid content varied between the red and white quinoa seeds, highlighting their potential antioxidant properties. SEM revealed distinct microstructural differences between the red and white quinoa protein isolates. Zeta potential measurements indicated the negative surface charges, influencing the stability in the solution. A particle size distribution analysis showed the monomodal distributions with minor variations in the mean particle size. The DSC profiles demonstrated multiple denaturation peaks, reflecting the complex protein compositions. Rheological studies indicated diverse gelation behaviors and mechanical properties. Overall, this comprehensive characterization underscores the potential of quinoa protein isolates as functional food ingredients with diverse applications in the food industry.

Diversity and functional role of bacterial microbiota in spontaneous coffee fermentation in northern Peru using shotgun metagenomics.

Peru is the ninth-largest coffee producer and the largest organic coffee exporter worldwide. Specific modifications in the microbial consortia during fermentation control the flavor of coffee. It is still unclear how fermentation duration affects microbial communities. This study aimed to provide insights into the diversity and functional behavior of the bacterial microbiome during coffee fermentation in northern Peru using shotgun metagenomics. Accordingly, metagenomic DNA was extracted and sequenced from samples of the liquid fraction during the short fermentation process (SFP) in Amazonas (6 and 12h) and long fermentation process (LFP) in Cajamarca (6, 12, 18, 24, and 36h). Our findings indicate that common (e.g., Acetobacter, Lactobacillus, Leuconostoc, and Weissella) and unique (e.g., Acidiphilium and Methylobacterium) acid-tolerant bacteria from the SFP and LFP play crucial roles and have a positive impact on the sensory qualities of coffee. Specifically, the LFP from San Ignacio might be associated with the high sensory quality of coffee based on the release of catalytic, hydrolase, oxidoreductase, transferase, and transporter enzymes in the InterPro and KEGG profiles. Additionally, these bacterial microorganisms metabolize several compounds (e.g., isoleucine, betaine, galactose, tryptophan, arginine, and cobalamin) into volatile compounds, mainly in the LFP, enhancing the flavor and aroma of coffees. This characteristic suggests that the LFP has a stronger effect on coffee quality than does the SFP on the basis of bacterial diversity and functional prediction. These findings provide new perspectives on the potential biotechnological uses of autochthonous microorganisms to produce superior-quality coffee beans from northern Peru.

A Container Escape Detection Method Based on a Dependency Graph

With the rapid advancement in edge computing, container technology has gained widespread adoption. This is due to its lightweight isolation mechanisms, high portability, and fast deployment capabilities. Despite these advantages, container technology also introduces significant security risks. One of the most critical is container escape. However, current detection research is incomplete. Many methods lack comprehensive detection coverage or fail to fully reconstruct the attack process. To address these gaps, this paper proposes a container escape detection method based on a dependency graph. The method uses various nodes and edges to describe diverse system behaviors. This approach enables the detection of a broader range of attack types. It also effectively captures the contextual relationships between system events, facilitating attack traceability and reconstruction. We design a method to identify container processes on the dependency graph through label generation and propagation. Based on this, container escape detection is implemented using file access control within the graph. Experimental results demonstrate the effectiveness of the proposed method in detecting container escapes.

Exploring giant dielectric constant in Dy2CoMnO6: evidence of non-overlapping small polaron hopping

Abstract Perovskites, both natural and synthetic, form a large class of materials are a vast class of materials with significant technological relevance due to their remarkable physical properties, such as superconductivity, magnetoresistance, ionic conductivity, and diverse dielectric behaviors. In this study, the dielectric relaxation and transport properties of the double perovskite Dy2CoMnO6(DCMO) synthesized via high-energy ball milling are investigated. DCMO exhibits a notably large dielectric constant, attributed to a combination of intrinsic and extrinsic mechanisms. Frequency-dependent dielectric studies reveal non-Debye-like behavior, validated by augmented Havriliak-Negami function fitting. Impedance spectroscopy confirms the semiconducting nature of DCMO, showing a negative temperature coefficient of resistance, and identifies two distinct relaxation processes corresponding to grain boundaries and grain interiors thereby highlighting the impact of microstructure and defects. The Cole-Cole plot further supports the non-Debye behavior, while thermally activated relaxation suggests damped charge carrier dynamics at grain boundaries. Conduction analysis using augmented Jonscher's power law reveals non-overlapping small polaron tunneling as the dominant mechanism driving both the dielectric response and transport properties, with DC conductivity suggesting a three-dimensional variable range hopping model. These results provide significant insights into the dielectric and transport properties of DCMO, highlighting its promising potential for advanced electronic applications.

Cognitive Models for Machine Theory of Mind

AbstractSome of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence.

Compositional effect on pressure-induced polymorphism in high-entropy alloys

Recently, pressure-induced polymorphic phase transitions were recently discovered in several fragmented high-entropy alloys (HEAs), offering a valuable opportunity to deepen our understanding of these materials. However, the chemical and physical factors that govern these transitions are still unclear. Here, we combined in situ high-pressure and high-temperature synchrotron X-ray diffraction, X-ray emission spectroscopy (XES), and high-resolution transmission electron microscopy (HRTEM) to systematically study the evolution of the atomic and electronic structures in the Cantor alloy and its face-centered-cubic (fcc) subset alloys (CoCrFeMnNi, CoCrFeNi, CoCrMnNi, CoFeMnNi, CoCrNi, CoFeNi, CoMnNi, CrFeNi, and FeMnNi). Surprisingly, diverse behavior was observed among these closely related alloys during compression and decompression, which includes irreversible, reversible fcc to hexagonal close-packed (hcp) phase transitions, or even no detectable phase transitions up to ∼40 GPa. HRTEM measurements confirmed that the fcc and hcp phases abided by the classic Shoji-Nishiyama orientation relationship during the transitions. XES data indicated that high-pressure suppresses the local magnetic moments in all the studied alloys, suggesting that magnetic states do not significantly influence the polymorphic transitions. By comparing the effects of the atomic size difference, entropy, valence electron concentration, and stacking fault energy across all the compositions studied, only the stacking fault energy shows a strong correlation with the phase transitions, indicating it plays a key role in inducing polymorphism in HEAs.

Synchronization of extraction and chromatographic conditions of diverse physicochemical behavior of pseudoephedrine, loratadine and desloratadine for their simultaneous determination in human plasma: Application to pharmacokinetic study

A selective, sensitive, and rapid LC-MS/MS method has been developed, optimized, and validated for the simultaneous determination of pseudoephedrine (PSE), loratadine (LOR), and LOR active metabolite, desloratadine (DES) in K3EDTA human plasma. A simple liquid–liquid extraction method was adopted for extraction of the 3 analytes and their internal standards; chlorpheniramine (CLP), loratadine D4 (LOR-D4), and desloratadine D4 (DES-D4), respectively, giving consistent recovery results (78.38––88.23 %) using diethyl ether: dichloromethane (80:20, v/v) as the extraction solvent. The chromatographic separation was challenging due to the polarity differences between PES, which is more polar, and LOR and DES, which are less polar that affected their physicochemical properties including the retention of the drugs on C18 column. The separation was achieved using of isocratic elution of 20 mM ammonium formate and 2 mM trifluoracetic acid ammonium salt: acetonitrile containing 0.15 % formic acid (12:88, v/v) as a mobile phase pumped onto a Supelco Discovery® C18 (4.6 mm x 100 mm, 5 µm) at 35 °C with a flow rate of 0.8 mL/min. The use of trifluoracetic acid ammonium salt as an ion pair reagent in the selected mobile phase allowed good retention of the polar PSE on the used C18 column with a reasonable retention time. Positive Electrospray ionization was employed followed by detection using a tandem mass spectrometer in multiple reactions monitoring (MRM) mode. The sample analysis of this study was conducted in compliance with ICH guideline M10 on bioanalytical method validation and study sample analysis. The method was linear over the concentration range of (4–1200) ng/mL for PSE, (50–15000) pg/mL for both LOR, and DES. The developed method was accurate (91.14–110.30 %), precise (0.53–7.86 %), and was applied to study the pharmacokinetic profile of the cited drugs in the plasma of healthy human volunteers after single oral administration of the fixed-dose combination Claritin D® tablet, (240/10 mg) of PSE, and LOR, respectively.

Network structure and time delays shape synchronization patterns in brain network models.

In this paper, we investigate synchronization patterns and coherence for a network of delayed Wilson-Cowan nodes. To capture information processing across different brain regions, our model incorporates two distinct delays: an intra-nodal delay that reflects the time signals take to travel within a cortical region due to local circuitry and an inter-nodal delay representing the longer communication times associated with white matter connections between brain areas. To investigate the role of network topology, we consider a range of toy network structures as well as the known (macro-scale) cortical structure of the Macaque monkey. We examine how global network dynamics are shaped by a combination of network configuration, coupling strength, and time delays. Our focus lies on two dynamic measures: synchrony and metastability, the latter reflecting the temporal variation of the former, both crucial for the brain's real-time functionality. Our investigation identifies extensive regions within the system's parameter space where the synchronized state exhibits transverse instabilities. These instabilities give rise to diverse dynamical behaviors contingent upon the network architecture and the interplay between coupling strength and time delay. While similar complex partially synchronized states existed for all network topologies considered, the cortical network demonstrated time-dependent behaviors, such as phase cluster dynamics, which were absent in the toy network architectures, and which are considered crucial in its ability to orchestrate complex information processing and behavior. Additionally, we illustrate how delays can regulate a cortical network with chaotic local dynamics, thus emphasizing the potential importance of delays in suppressing pathological spreading dynamics.

The mechanism of selective separation of stibnite and arsenopyrite by Cu2+ coordination assembly KBX collector

The similar physicochemical properties of stibnite and arsenopyrite resulted in the difficult separation by traditional collectors in flotation. Hence, this study utilized Cu2+ and potassium butyl xanthate (KBX) coordinate assembly to form a novel Cu-KBX complex collector, investigating its properties, conformation, and its role in flotation separation of stibnite and arsenopyrite, as well as its diverse adsorption behavior on mineral surfaces. The stable Cu-KBX complex solution was formed when the molar ratio of Cu2+ to KBX was 1:2, exhibiting a network-like structure at that time. The micro-flotation results showed that at pH 5, the grade of Sb in the concentrate was as high as 61.08 %, with As content at only 4.65 %, enabling effective separation of stibnite and arsenopyrite. Furthermore, the Cu-KBX complex exhibited a reticulated structure at the stibnite surface, while it adhered in a granular fashion on the arsenopyrite surface. FTIR analysis confirmed stronger chemisorption of Cu-KBX onto stibnite compared to arsenopyrite. XPS results indicated that Cu-S was the main collecting component. However, a weak Fe(II)-S substance was found on the arsenopyrite interface, likely due to Fe3+ oxidizing in the slurry with the Cu-KBX complex. Therefore, this disruption of the Cu-KBX complex structure by Fe3+ in arsenopyrite sharply reduced its adsorption on arsenopyrite, enhancing the selective separation of stibnite and arsenopyrite.

A dual-optimization building energy prediction framework based on improved dung beetle algorithm, variational mode decomposition and deep learning

Accurately predicting building energy consumption is essential for enhancing energy utilization efficiency in buildings. However, the inherent volatility and noise in building energy data, caused by diverse user behaviors and potential sensor errors, make significant challenges to energy consumption prediction. To address these issues, a dual-optimization framework (IDBO-VMD-IDBO-BiLSTM) for building energy consumption prediction, which incorporates improved dung beetle optimization algorithm (IDBO), variational mode decomposition (VMD), and bidirectional long short-term memory network (BiLSTM), was proposed. In this framework, IDBO firstly optimizes the VMD by adaptively determining its optimal parameters to decompose the original building energy consumption series into multiple intrinsic modal functions (IMFs) with smoother characteristics, thereby the effect of mitigating data noise. Then, each IMF component is predicted using the BiLSTM model, with IDBO selecting the optimal hyperparameters for BiLSTM. Finally, the individual predictions of each IMF are superimposed and reconstructed to yield the final predictions. To verify the framework’s effectiveness, real energy consumption data from an office building in Shanghai was collected and analyzed in a comprehensive comparison with seven other comparative models. Experimental results suggested that the proposed framework outperformed the comparative models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2), showing both high predictive accuracy and strong robustness. Therefore, the proposed framework can be an effective tool for predicting building energy consumption

Designer Helical Fibers and Tubes: Self-assembling Hybrid Peptides via Leu/Ile-Phe Zipper.

Here, we present a family of simple peptides that show diverse self-assembling behaviors. We used aliphatic (Leu/Ile) and aromatic (Phe) amino acids to delineate our design. The design consists of phenylene urea at the N-terminus of the peptide. The urea peptides with sequence Phe-LeuOMe (1) or Phe-IleOMe (2) associate to form polygonal peptide tubes via zipper arrangements, supported by microscopic and single crystal X-ray diffraction studies. The peptide with Phe placed away from the phenylene urea (3 and 4), showed fibrous assembly. All the peptides showed autofluorescence and red edge excitation behavior upon self-assembly.

Efficient Simulator for P2P Energy Trading: Customizable Bid Preferences for Trading Agents

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences.

Regional and socio-demographic predictors of dietary proficiency of adolescent girls in Nigeria

Background: Dietary behaviors of female adolescents are of increasing concern due to their critical role in determining long-term health outcomes. Aims: This study aimed at investigating dietary proficiency of adolescent girls aged 10 – 19. Methods: A descriptive cross-sectional study was conducted with 2261 in-school female adolescents aged 10 – 19 years. Participants were selected using a multistage stratified random sampling procedure was used to select participants from three geopolitical zones in Nigeria. Two states were selected from three geopolitical zones in Nigeria: North-Central (Kogi and Niger States), South-East (Abia and Imo States), and South-West (Osun and Ondo States). A validated questionnaire was employed to collect data on sociodemographic variables, Dietary Diversity (DD), Nutrition Knowledge (NK), and Dietary Behavior (DB). These variables were combined to define Dietary Proficiency. Data were analyzed using SPSS version 27. Chi-square test assessed regional associations, while logistic regression analysis was used to identify predictors of dietary proficiency (DP). Statistical significance was set at p &lt; 0.05. Results: Significant regional variations were observed in DD (χ2 = 36.069), NK (χ2 = 56.895), DB (χ2 = 176.52), and DP (χ2 = 40.551). Older girls residing in peri-urban location, from Igbo ethnic group, and moderately large household predicted a likelihood of higher DD with odd ratios exceeding 1. Larger household size (OR = 1.056, 95% CI = 0.813 – 1.373) predicted higher NK. Residing in South-East region (OR = 2.030, 95% CI = 1.640 – 2.513) and being in the mid-adolescent age group (14 – 16 years) (OR = 2.093, 95%CI = 1.589 – 2.756) doubled the likelihood of high DB. Predictors of higher DP included older age (OR = 1.160, 95% CI = 0.923 –1.457) and residence in the South-East region (OR = 1.148, 95% CI = 0.883–1.49). Conclusion: Regional and sociodemographic differences significantly influenced dietary proficiency among adolescent girls. Addressing these disparities through targeted nutrition education and awareness programs is crucial for promoting healthier dietary behaviors and improving overall dietary proficiency in this population. Keywords: Dietary Factors, School Adolescent Female, Food Choices, Food Intake, Nigeria.

Particle-stabilized foams with fatty acid salts as stabilizers triggered by transition metal ions

Particle-stabilized foams have been becoming the focus on foaming field because of their excellent stability due to the high stiffness of interface membrane. However, the modification of particle surfaces to reach an appropriate wettability needs complicated process, resulting in accompanied inconvenient foaming-defoaming regulation. In this work, we reported a series of foams stabilized by insoluble particles generated from fatty acid anions with metal ions. Different hydrocarbon chains endowed the formed particles diverse wettability and different aggregation behaviors at the gas–liquid interface, e.g., different foamability and the formation of the “dry water”. The foams exhibited ultra-stable properties and were responsive to environmental acidity, thus possessing a smart foaming-defoaming transition tuned by pH. The combination ability of fatty acid anions with metal ions changed with the chain-length and the nature of metal ions, leading to a choice for the extraction or separation of some metal ions in a fast and simple way. This new aggregation behavior of fatty acid salts can be explored to expand the practical applications.

Organogels of FmocFF: Exploring the Solvent-Dependent Gelmorphic Behavior.

FmocFF (9-fluorenyl methoxycarbonyl-phenylalanine) is an extensively studied low-molecular-weight hydrogel. Although there have been numerous studies on FmocFF hydrogel, its potential to form organogels has not been well explored. In this work, we systematically explore the organogels of FmocFF in a wide range of organic solvents. FmocFF is found to be a robust organogeltor, and the subsequent organogels exhibit diverse gelmorphic behavior exhibiting various degrees of crystallinity and morphology depending on the solvent used. The mechanical strength of the organogels is evaluated using rheology. A novel technique, in situ SHG microscopy, is introduced to study the gel structure in its native state. In addition to the solvent-solute interactions that are typically used to predict gelmorphic behavior, we observed indications that the degree of crystallinity also plays a significant role in determining the mechanical properties and structure of FmocFF organogels.

Sexual and reproductive health of Sudanese refugee girls in Chad: mixed methods study with perspectives from 12-19 year old girls, parents, and health workers.

In humanitarian settings, refugee girls' vulnerability to negative sexual and reproductive health (SRH) outcomes and the barriers they face to access to SRH services increase. Despite global guidelines on adolescent sexual and reproductive health and rights (SRHR) in humanitarian settings, evidence on the diverse knowledge, attitudes, and behaviors of refugee adolescents are limited. This mixed methods study used a cross-sectional survey and participatory research activities to explore the knowledge, attitudes, and behaviors of 12-19 year old refugee girls from Darfur living in two refugee camps in Wadi-Fira, Chad. Focus group discussions with parents of adolescents and in-depth interviews with health workers were conducted to better understand community attitudes toward adolescent SRHR and barriers to accessing services. Overall, SRH knowledge, including of contraceptive methods, was mixed, but older girls had better knowledge than younger girls. Despite stigma around adolescent sexual activity expressed in this community, 20.9% of girls had already had sex. The majority of girls believed that health workers would maintain confidentiality if they sought contraception. Among girls who had ever had sex, 18.0% were currently using a modern contraceptive. None were using a long-acting method, but most obtained their method at the camp health center. Parents and health workers described how social stigma toward premarital sex and unintended pregnancy impeded adolescent access to SRHR information and services, although the midwives described helping girls to seek contraception. Despite community stigma towards premarital sex and contraception for adolescents, some girls in the camps successfully managed to receive a contraceptive method, demonstrating both their interest in and need for contraception. Although midwives were largely supportive of adolescent access, expanding contraceptive service delivery channels and making services more adolescent-responsive would further increase adolescent access. Gender transformative programming engaging girls and boys, should be expanded to improve adolescent knowledge and self-efficacy with respect to SRHR. These efforts must also engage parents and community members to create an enabling environment for adolescent SRHR and reduce stigma.

Evolutionary Game Theory: A General Review

This article explores the Evolutionary Game Theory (now EGT), encompassing its historical underpinnings, recent advancements, and future potential. Originating in the 1970s through the pioneering work of John Maynard Smith and George R. Price, EGT leverages game-theoretic concepts to elucidate the evolution of strategies within various populations across biological, economic, and social domains. Notably, recent progress has seen the integration of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 into agent-based simulations, thereby enriching the authenticity and intricacy of strategic interactions. Additionally, the study addresses the complexities associated with modeling diverse behaviors and bridging the insights derived from LLMs to practical applications in fields like biology, healthcare, education, and social sciences. Furthermore, it underscores the significance of interdisciplinary collaboration and innovative methodologies in addressing the multifaceted challenges within EGT. Finally, the article contemplates the potential avenues for future research, emphasizing the fusion of EGT with real-world applications and the necessity for comprehensive models that encompass the complexities of evolutionary dynamics in adaptive systems.

Global dynamics of a generalized arbitrary order Van der Pol–Duffing Oscillator

We study the global bifurcation diagram and corresponding global phase portraits in the Poincaré disc for a generalized van der Pol-Duffing oscillator, which has four nonlinear terms with arbitrary orders. This nonlinear oscillator possesses more diverse and complicated dynamical behaviours, including the heteroclinic bifurcation, generalized Hopf bifurcation and pitchfork bifurcation. Moreover, theoretical results are exhibited via numerical simulations.

The diversity of evolution behavior between stoichiometric and non-stoichiometric AlTM intermetallics in Mg melt

In this study, Al-5Ti, Al-18Si-5Ti, Al-12Si-2Sc and Al-12Si-1Sc-1Zr alloys, containing the Al3Ti, Ti7Al5Si12, AlSi2Sc2 and AlSi2(Sc, Zr)2 particles, respectively, were introduced into a Mg melt to understand the morphological and structural evolution behaviors of AlTM intermetallic compounds. Deposition layers at the bottom of the Mg melt were obtained due to the particle settlement. Scanning electron microscope and X-ray diffraction were employed to analyze the phase morphologies and structures of the AlTM intermetallic compounds in the deposition layers. A morphological transformation, characterized by the cracking of particles into fine clusters, was observed in all these AlTM intermetallic compounds, which is promoted by the inward diffusion of Mg atoms. The phase structures of the final particles remained Al3Ti and AlSi2Sc2 when Al-5Ti and Al-12Si-2Sc alloys were introduced into the Mg melt, indicating no structural evolution. However, structural evolutions were detected from Ti7Al5Si12 to Ti5Si4 and from AlSi2(Sc, Zr)2 to SiScZr when they were introduced in the Mg melt. It is hypothesized that the crystal structure, whether stoichiometric or non-stoichiometric, is closely related to the structural evolution behavior of AlTM intermetallic compounds. This work may provide new insights into phase control by introducing one type of metal matrix master alloy into another metal melt.