Diverse Behaviors Research Articles

Consumer Preferences of Chia Products: A Conjoint Analysis

Chia (Salvia hispanica L.) is a nutrient-rich crop native to Mexico and Guatemala, valued for its high content of omega-3 and omega-6 fatty acids, protein, dietary fiber, vitamins, and antioxidants. While historically significant for the Aztecs and Mayans, chia has become a globally cultivated crop due to its health benefits. Introduced to India by CFTRI Mysore in 2015, it has gained substantial traction, particularly in Karnataka, where it has emerged as a key crop. This study investigates chia seed consumption among 80 urban consumers in Bangalore using purposive sampling and structured questionnaires. Conjoint analysis evaluates consumer preferences, while Garrett's ranking technique identifies critical factors influencing consumption patterns. The findings reveal that chia seeds are widely integrated into daily diets, with their whole form being universally consumed. Products such as chia pudding, beverages, and snacks are favored weekly. Key preferences are shaped by health benefits, dietary trends, and accessibility, with moderate to high socio-economic status consumers showing greater engagement. Awareness about chia seeds is primarily driven by social media and direct interactions at food festivals, whereas organized retail outlets and online platforms are the dominant purchase channels. The study emphasizes the growing relevance of chia seeds in urban diets, aligning with global health trends and sustainable agriculture practices. However, limitations include a focus on a specific geographic area and consumer segment, which may not capture the diversity of broader consumer behavior. Future research could explore rural consumption patterns and strategies for expanding chia’s accessibility to diverse socio-economic groups, thereby enhancing its market potential and sustainability impact.

Strategic Public Relations Practices and Consumer Buying Behaviour in Sub-Saharan Africa

The purpose of this work was to assess if strategic public relations practices have made an appreciable impact on consumer buying behaviour in Nigeria. In a descriptive research design, we adopted a quantitative survey and both descriptive and inferential statistics were used to answer the four research questions. Primary data was generated from a sample of 800 respondents selected across the six geopolitical zones of the country using multiple sampling techniques. Results from the estimation of a multiple regression analysis to determine the impact of both community relations practice, customer relations practice, media relations practice and government relations practice shows that significant efforts have been made by strategic public relations practitioners in the area that will help businesses succeed in the true sense of business success. The study concluded that any increase in the strategic public relations practice affects consumers’ decision to buy, hence increases the profitability of the companies while also preserving the environment and image of the company. In line with the findings, the study recommended a nuanced approach to strategic public relations practices in the Nigerian context, where diverse consumer preferences and behaviours are at play and continuous monitoring and adaptation of strategic PR practices remain very critical.

Minimum dietary diversity behaviour among children aged 6 to 24 months and their determinants: insights from 31 Sub-Saharan African (SSA) countries

BackgroundFeeding practices during infancy have a significant impact on a child’s cognitive development and long-term health outcomes. Dietary diversity guidelines from the WHO and UNICEF recommend a diverse range of foods for children aged below 24 months for their optimal growth and development. However, in sub-Saharan Africa (SSA), little is known about the extent to which dietary diversity behaviour in children aged 6 to 24 months aligns with the recommendations and the factors associated with the differentials in dietary behaviour. This study aimed to fill this gap.MethodsThis study employed an analytical cross-sectional approach, drawing on recent Demographic and Health Survey (DHS) data from 31 SSA countries. The study included a weighted sample of 44,071 children aged between 6 and 24 months, and their respective mothers aged 15–49 years. The primary outcome was Minimum Dietary Diversity (MDD) categorised per WHO recommendation. Multivariable logistic regression was used to examine the association of MDD with demographic and socio-economic characteristics.ResultsThe pooled MDD intake among children aged 6–24 months in SSA was 11% ranging from as low as 1.3% in Burkina Faso to 32.9% in South Africa. Children of mothers aged 45–49 years had 52% significant higher likelihood of MDD compared to those aged 15–19 years (AOR = 1.52, 95% CI:1.03, 2.24). Higher maternal education levels also increased MDD odds in the children: 22% higher for mothers who had attained secondary education (AOR = 1.22, 95% CI:1.07, 1.39), and 36% higher for those with education beyond secondary level (AOR = 1.36, 95% CI:1.09, 1.71) compared to no education. Children of rich mothers had 44% higher odds of MDD than those with poor mothers (AOR = 1.44, 95% CI:1.27, 1.62). Increased antenatal visits, and urban residence also contributed to higher MDD odds.ConclusionBased on the current global estimate of approximately 28% MDD rate, the reported 11% MDD intake among children in this study is relatively low. There is a positive association between MDD intake in children and several factors, including maternal education, antenatal visits, wealth index, and residency. These findings highlight the need for policymakers and other stakeholders to give urgent attention to empowering parents to ensure adequate nutrient intake among children for better child growth and development.

Investigating the Effect of Human Factors on the Underground Mine Evacuation Process Using Agent-Based Simulation

Human factors play a critical role in underground mine evacuations, significantly impacting performance during emergencies. Traditional mine evacuation strategies rely on predetermined routes and static signs, but these methods do not account for the dynamic and unpredictable nature of real emergencies or the diverse behaviors of individuals. This research addresses these limitations by using agent-based simulations to explore evacuation behavior and performance in underground mines. The study involved three key steps. First, agent-based simulations were deployed to model both individual and group behaviors during emergencies. Second, evacuation performance was compared across three scenarios: miners following traditional passive signage, those making decisions in chaotic conditions, and miners equipped with smart evacuation devices. Finally, evacuation times were quantified to assess the effectiveness of each approach. The results revealed that miners using smart devices improved evacuation efficiency by 35% compared with those relying on passive signage and by 37% compared with chaotic decision-making scenarios. The median evacuation time was reduced from 10.8 min with passive signage to 7 min when using smart devices, taking varying stamina levels into account. These findings underscore the importance of integrating intelligent systems that account for pre-evacuation and wayfinding behaviors, offering new insights and setting a higher standard for emergency protocols in underground mining.

Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS

The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems.

Orexin in depression: Evidence from basic and clinical research

Orexin – a neuropeptide – is extensively distributed in the central nervous system and is involved in the regulation of diverse physiological functions and behaviors. Orexin is strongly associated with the onset and development of depression. The most important function of orexin is to interact with the transport system, mediating arousal, and energy homeostasis. Hypothalamic-ventral tegmental and hypothalamic-ventral thalamic pathways provide clues for understanding the function of orexin and related disorders, such as sleep disorders, eating disorders, and substance abuse. This article summarizes the basic and clinical studies on the role of the orexin system in regulating and treating depression, including the relationship between the expression level of orexin in specific brain areas and diseases, relationship between adolescent depression and orexin, orexin receptor antagonists as a treatment for depression, and narcolepsy, which is closely associated with depression. Research progress on the role of orexin in depression and recent relevant studies are summarized, providing novel directions for developing depression treatment strategies.

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.