Nature Of Interactions Research Articles

AirCanvas using OpenCV and MediaPipe

Human-Computer Interaction (HCI) has undergone significant transformations with the advent of Artificial Intelligence (AI) and Machine Learning (ML), enhancing the ways in which users engage with computing systems. This paper introduces AirCanvas, a novel hands-free digital interaction tool that leverages air gestures for intuitive and seamless computer control. The system uses advanced image processing techniques, specifically OpenCV for visual data analysis and MediaPipe for accurate hand gesture recognition, enabling users to manipulate virtual environments without physical touch. By integrating a standard webcam as the primary sensor, AirCanvas offers an accessible solution for gesture-based interaction, eliminating the need for specialized external hardware like motion sensors or gloves. Users can perform a variety of tasks such as virtual drawing, cursor control, and presentation navigation with simple hand gestures in 3D space. The system's gesture recognition capabilities are powered by deep learning models trained on large datasets of hand movements, ensuring robust performance in diverse lighting and environmental conditions. The potential applications of AirCanvas are far-reaching, ranging from interactive art creation to more practical uses in assistive technology, where people with mobility impairments can benefit from hands-free control. In the field of robotics, the tool can enable more natural human-robot interaction, while in gaming, it can introduce new forms of immersive gameplay through gesture-based interfaces. Furthermore, the tool's open-source nature allows for further customization and enhancement, fostering innovation and collaboration in various industries. As human-computer interaction continues to evolve, AirCanvas represents a significant step forward in making technology more intuitive, engaging, and accessible for all users.

With a little help from my friends: importance of protist-protist interactions in structuring marine protistan communities in the San Pedro Channel.

Network analyses are commonly used to identify some of the ecological interactions that may be occurring between protists in the ocean; however, evaluating predictions obtained from these analyses remains difficult due to the large number of interactions that may be recovered and the limited amount of information available on protist-protist interactions in nature. In this study, ecological network analyses were conducted using data collected at the San Pedro Ocean Time-series (SPOT) station and the network predictions were compared against a database of established protist-protist interactions. These database comparisons revealed hundreds of confirmed protist-protist interactions, and thousands of putative, novel interactions that may be occurring at the SPOT station. The database comparisons carried out in this study provide a new way of evaluating network predictions and highlight the complex, yet critical role that ecological interactions play in shaping protistan community composition in marine ecosystems.

Interplay of Tetrel, Hydrogen, and Halogen Bonds in F3GeOCl and HCN Complexes: A Comprehensive Theoretical Study of Dimers, Trimers, and Tetramers.

This study investigates the nature and interplay of noncovalent interactions (NCIs)─tetrel bonds (TB), hydrogen bonds (HB), and halogen bonds (XB)─in molecular assemblies formed between trifluorogermyl hypochlorite (F3GeOCl) and hydrogen cyanide (HCN). Using a combination of high-level computational methods, we explored the geometric, energetic, and electronic properties of dimers, trimers, and tetramers formed in different molar ratios of interacting reagents. Various analyses reveal a significant cooperativity between TB and HB, which mutually reinforce each other, while XB interactions are diminished in the presence of TB and HB. Energy decomposition analysis (EDA) through SAPT and sobEDAw methods identified electrostatic and orbital interactions as key contributors to the stabilization of TB and HB, while dispersion plays a prominent role in XB. A perfect linear correlation was found between interaction energy and charge density at bond critical points (BCPs), underscoring the predictive value of these metrics. These findings shed light on the cooperative nature of NCIs and provide a framework for designing molecular systems in supramolecular chemistry and crystal engineering.

Intramolecular Redox-driven Synthesis of Mono- and Diradical Iridium(III) Complexes: Insights into Molecular and Electronic Structure, Electrochemistry, and Spin-Communication.

Two π-radical complexes containing bisazo-aromatic-centered radical anion (1•-) were synthesized through in-situ electron transfer from metal-to-ligand using [IrI] and 2-(2-Pyridylazo)azobenzene (1) in inert hydrocarbon solvent. These are characterized as diradical [IrIII(1•-)2]+[2]+ and monoradical [IrIII(1•-)Cl2(PPh3)] 3. In contrast, a rare metal-mediated hydrolytic cleavage of the C(sp2)-N bond occurred in protic solvent resulting in quaternary radical complex [IrIII(1•-)(1')(PPh3)]+(4)+. This provides an easy way to synthesize stable unsubstituted pyridine-2-diazotate (1'), an otherwise unstable organic template. Theoretical scrutiny has been performed at (U)B3LYP/6-31G(d,p)/LANL2DZ level to explore the origin of redox and optical properties in radical complexes. Magnetic study of [2]+ reveals that a weak antiferromagnetic (AF) spin-communication (J = -4.39 cm-1) exists between two radicals, leading to an open-shell singlet ground state. Broken symmetry density functional theory (BS-DFT) calculations were carried out to probe the nature of antiferromagnetic exchange interaction between the two radical centers in species [2]+. This method has been employed with different basis functionals (BP86, BLYP, OLYP, TPSS0, TPSSh, ωb97D and B3LYP) to comprehend the nature of the exchange in [2]+. The best result is obtained for pure functional OLYP with a J value -8.4 cm-1.

Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior – a crucial aspect of intelligent conversations – is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains.

Pain Science Education for Patients with Chronic Pain Referred to Community-Based Rehabilitation: Translation, Adaptation, and Clinical Feasibility Testing of PNE4Adults

Background/Objectives: Pain science education accommodating low health literacy is needed for people with chronic pain. The purpose of this study was to translate PNE4Kids, contextually adapt it into PNE4Adults, and test the feasibility of the newly developed pain science education program (PNE4Adults) for adult patients with chronic musculoskeletal pain in the municipality. Methods: A three-step approach was used to (1) translate PNE4Kids into Danish, (2) adapt to age and context (PNE4Adults), and (3) test the feasibility. (1) Translation was performed by a native Dane fluent in Dutch. (2) Two think-aloud group sessions were held, with therapists and end users. (3) Feasibility was tested amongst twenty adult patients with chronic musculoskeletal pain consecutively referred for rehabilitation in the municipality. The a priori success criteria were determined to be 70% acceptability and 70% understandability. Prior to inclusion of the first patient, the study was pre-registered on clinicaltrial.gov [NCT05140031]. Results: Translation was successfully performed. Both the therapist and end users found the program easy to grasp, the simplicity and interactive nature of the program ingenious, and the program to be well suited to an adult population. All patients (100%), across health literacy levels, found PNE4Adults comprehensible and acceptable. Conclusions: The aims were successfully met. Progression to a full trial is warranted and is underway.

Fostering collaborative practice: a pilot study on interprofessional education through simulation-based team-oriented learning sessions among pharmacy and medical students

BackgroundIt is pivotal to implement effective collaboration among professionals from different disciplines in the healthcare sector. As the complexity of patients’ health needs grows, so does the importance of developing innovative and efficient models of patient care that rely on interprofessional collaborative teamwork. Interprofessional education (IPE) is a valuable approach that improves communication, collaboration, and ultimately safe patient outcomes in healthcare settings. Students studying medicine and pharmacy are among the healthcare professionals who could potentially benefit from IPE. Despite the existence of newly added undergraduate curricula for interprofessional communication and professionalism, active implementation to achieve the required competencies and skills is not yet widespread, particularly in developing countries. This article presents a successful implementation of simulation-based team-oriented learning sessions among pharmacy and medical students at Delta University for Science and Technology in Egypt. The purpose of the IPE activity was to train students to work collaboratively as members of an interprofessional healthcare team by simulating a real-life situation.ResultsStudent feedback was very positive with most pharmacy and medical students being satisfied with their IPE experience. Moreover, students’ feedback emphasized the significance of IPE and a simulated team-based environment in improving communication skills and collaborative practice.ConclusionThe high level of student satisfaction with the IPE sessions, particularly regarding the clarity of training objectives and the interactive nature of the experience, suggests that simulation-based learning is an effective tool for developing key interprofessional collaboration skills among healthcare students. This highlights the potential of IPE to cultivate future healthcare professionals who excel in interprofessional collaboration, ultimately leading to improved patient care and outcomes. Overcoming barriers to implementing IPE in Egypt needs a multifaceted strategy and a comprehensive long-term follow-up plan is critical for evaluating the lasting effect of IPE on students’ collaborative practices in real-life healthcare settings.

The roles of viruses in tephritid pest multitrophic interactions and an outlook for biological control.

Tephritid fruit fly pests remain a considerable problem for agricultural fruit production around the world. New control methods that do not rely on synthetic insecticides are increasingly desirable to diversify tephritid pest management programs. Biological control through the release of parasitoid wasps has historically provided effective suppression of fruit fly pests, although molecular factors that influence the success of fruit fly parasitoids are understudied. Microbes have been demonstrated to facilitate myriad interactions between insects and their environment and have been the subject of recent investigation within tephritids. Specifically, the diversity and function of viruses found within fruit flies and associated parasitoids is an emerging field of research that has the potential to deepen our understanding of previously hidden factors that facilitate sustainable pest control. Most work to date has focused on identifying resident viral communities within fruit flies using metagenomic and metatranscriptomic sequencing approaches. Additionally, a growing body of evidence has revealed a multitude of functional dynamics that viruses have with fruit fly hosts, including vertically transmitted commensal viruses and parasitoid-vectored pathogens. Heritable viruses transmitted by fruit fly parasitoids, in particular, have been shown to play prominent roles in fruit fly multitrophic interactions, in which viral infection can shape the survival rate and host range of infected parasitoids. Furthermore, at least one parasitoid virus represents a lethal pathogen to a wide range of fruit fly pest species. Parasitoid viruses could therefore present novel opportunities to leverage natural antagonistic interactions for fruit fly pest control innovations.

Humanoid robot learning of complex behaviors with LLMs.

Learning complex behaviors by humanoid robots could be achieved with natural interactions aided by large language models.

Rebuilding a DSS with feedback from behavioral and neuroscience studies

AbstractAs the complexity of decisions and development technologies increase, decision support systems (DSSs) emerge to support decision‐making and improve the system's interaction with decision‐makers (DMs). Hence, this study seeks to show the modulations made in the FITradeoff (Flexible and Interactive Trade‐off) DSS, highlighting its flexible and interactive nature. These modulations have been made due to findings from behavioral and neuroscience studies conducted on the FITradeoff method and represent an important advance in research in the multicriteria decision‐making/aiding field. The study focuses on the main modulation made in the preference modeling process of the FITradeoff method, namely combining two paradigms for modeling preferences: elicitation by decomposition and holistic evaluation. Thus, the study discusses its implementation in the DSS, shows the flexibility this gives DMs, and highlights the two decision strategies presented in the holistic evaluation, that is, selecting the best alternative and eliminating the worst alternative.

Adsorption of perfluorooctanoic acid from aqueous matrices onto chitosan-modified magnetic biochar: Response surface methodology-based modeling, performance, and mechanism.

Perfluorooctanoic acid (PFOA) removal has gained significant attention due to its environmental stability and potential toxicity. This study aims to synthesize a chitosan-modified magnetic biochar (CS_MBC) for efficient PFOA removal from aqueous solutions. Various CS loading ratios (0.25:1, 0.5:1, and 1:1) were explored to determine the optimal adsorbent, with preliminary experiments exhibiting superior performance of CS1_MBC. To explore the impact of various experimental conditions (pH, adsorbent dose, time, and initial PFOA concentrations) on PFOA removal and optimize these parameters, central composite design of response surface methodology was applied. Statistical analysis of variance was conducted to assess the model's adequacy, which demonstrated a strong correlation between experimental results and the model. The predicted optimal conditions for achieving maximum PFOA removal (∼94%) were pH 4, 120mg/L dose, 60min time, and 20mg/L PFOA concentration. The kinetics and isotherm studies revealed that the pseudo-second-order (R2=0.9996) and Redlich-Peterson (R2=0.999) models better described PFOA adsorption, with Langmuir maximum adsorption capacity of ∼517mg/g. Thermodynamic study confirmed the spontaneous, endothermic, and physisorptive nature of PFOA adsorption, with electrostatic and hydrophobic interactions and hydrogen bonding governing the process. Further, the fixed-bed column experiment was conducted to evaluate the effectiveness of CS1_MBC for practical applications, which demonstrated the maximum experimental adsorption capacity of 39.63mg/g. The breakthrough data showed an excellent fit with both the Thomas and Yoon-Nelson models, with a high correlation coefficient (R2=0.99). Therefore, this research underscores the potential of CS_MBC as an efficient adsorbent for mitigating PFOA contamination in aqueous environments.

Capturing the Effect of Anion Type on the Intermolecular Interactions between Water and Imidazolium-Based Ionic Liquids: A Comparative DFT Study.

The studies on ionic liquids (ILs) and their interaction with different solvents have always been an interesting topic for experimental and computational chemists. Recently, however, deep insights on the molecular structures of the IL-water binary mixtures have been mainly performed through classical simulations. Here, a comprehensive quantum mechanical study is presented on seven 1-butyl-3-methylimidazolium-based ILs in the absence and presence of water. As the most important intermolecular interaction between ionic moieties of ILs and water molecules, hydrogen bonding is studied through different bonding analyses. The effect of different anions, [NO3]-, [HSO4]-, [SCN]-, [DCA]-, [BF4]-, [PF6]-, and [NTf2]-, on the behavior of ILs interacting with a sample of water molecules is investigated. Comparing the implicit and explicit approaches to consider water solvent indicated that the structure of ILs in the solvent depends on the selected solvent model. By considering explicit water molecules, we analyzed the intermolecular interactions between ILs and the water sample. The energy decomposition analysis indicated that the stability of the IL···water systems is mainly due to the electrostatic component of the total interaction energy. The interaction region indicator (IRI) analysis discovered that chemical bond and van der Waals (vdW) interactions are important in the IL···water systems. Indeed, investigation of each ion/ion pair surrounded by ten nearest neighbor water molecules discovered that the vdW interactions are responsible for the cation···anion and the cation···water interactions, while chemical bonding is important in the anion···water and the water···water interactions. Therefore, the anion···water interaction requires further analysis. The quantum theory of atoms in molecules verified the ionic nature of the H-bond in the anion···water interaction. The IRI analysis showed that the interaction between water molecules and cyano-based anions, [SCN]- and [DCA]-, is only due to chemical bonding, while in the oxygenated anions, [NO3]- and [HSO4]-, the vdW forces are also important. For the other anions, [BF4]-, [PF6]-, and [NTf2]-, the vdW forces have the main contribution in the anion···water interaction. Natural bond orbital analysis indicated that these intermolecular interactions originate from nanion → σO-H* electron transfer. Finally, the law of matching water affinity (LMWA) using energy-based parameters was used to predict the hydrophilicity of ILs as follows: [BMIM][NO3] > [BMIM][SCN] > [BMIM][DCA] > [BMIM][HSO4] > [BMIM][BF4] > [BMIM][NTf2] > [BMIM][PF6]. Results obtained in the current work give insights into the electronic nature of intermolecular interactions between ILs and water molecules, which is necessary due to importance of water in modifying properties of ILs in various applications.

Research on Financial Support Mechanism and Policy Innovation of Guangzhou Sports Industry Upgrading under the Perspective of New Quality Productivity

In the context of economic globalization and rapid development of informationization, this study thoroughly explores the financial support mechanism and policy innovation for the upgrading of the sports industry in Guangzhou City under the perspective of new quality productivity. Given the important role of the sports industry in enhancing the city image, strengthening the physical fitness of the citizens and promoting the regional economic development, the significance of the study is to reveal the key role of financial support and policy innovation in the transformation and upgrading of the sports industry, and to provide theoretical support and practical guidance for the development of the sports industry in Guangzhou and even other cities. Through the methods of literature review, case analysis and comparative study, this study systematically sorted out the relevant theories, deeply analyzed the status quo, challenges and opportunities of the sports industry in Guangzhou City, and put forward the research questions on the role of financial support and policy innovation in the upgrading of the sports industry. Based on the theoretical framework constructed by the new quality productivity theory, this study proposes a series of countermeasure suggestions for financial support and policy innovation, aiming to promote the high-quality development of Guangzhou's sports industry. The key findings of the study emphasize the complementary and interactive nature of financial support and policy innovation in the upgrading of the sports industry, as well as their importance in promoting industrial innovation, enhancing industrial competitiveness and achieving sustainable development. Ultimately, this study concludes that financial support and policy innovation are indispensable dual-wheel driving forces for the upgrading of the sports industry in Guangzhou, and their effective combination will provide a solid foundation for the future development of the sports industry.

The effects of specialist co-teaching STEM intervention on primary students’ attitudes, perceptions, behaviors, and career aspiration: A mixed-methods study in China

STEM education, essential for imparting problem-solving skills, is increasingly emphasized in primary schools worldwide. However, the efficacy of specialist co-teaching STEM interventions at this level is less explored than in higher education. This study evaluates the impact of a specialist co-teaching STEM intervention on primary students’ attitudes, perceptions, behaviors, and STEM-related career aspiration. The intervention, rooted in the Theory of Planned Behavior (TPB), introduces students to notable STEM professions. The pilot study was conducted for survey validation and involved 203 targeted primary students. Of these, 40 underwent a one-month intervention after obtaining parental consent. A mixed-methods approach was adopted, introducing hands-on tasks associated with two STEM professions: airplane piloting and bridge engineering. The study employed pre and post quasi-experiment surveys and video-recorded class observations to gauge changes in students’ attitudes, perceptions, and career aspiration, and to capture learning behaviors during the intervention. The study revealed enhanced attitudes, perceptions, and STEM career aspiration among students after the intervention. Observations emphasized the interactive nature of the intervention, underscoring its effectiveness in enriching students’ STEM learning experiences and fostering positive STEM career aspiration.

Shared and individual tuning curves for social vision.

A stimulus with light is clearly visual; a stimulus with sound is clearly auditory. But what makes a stimulus "social", and how do judgments of socialness differ across people? Here, we characterize both group-level and individual thresholds for perceiving the presence and nature of a social interaction. We take advantage of the fact that humans are primed to see social interactions-e.g., chasing, playing, fighting-even in very un-lifelike stimuli such as animations of geometric shapes. Unlike prior work using these stimuli, we exploit their most advantageous property, which is that their visual features are fully parameterizable. We use this property to construct psychophysics-inspired "social tuning curves" for individual subjects. Social tuning curves are stable within individuals, unique across individuals, and show some relationship to socio-affective traits. Results support the view that social information processing begins early in the perceptual hierarchy. Further, our approach lays the foundation for a generative account of social perception in single subjects.

A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States

The correlation between poverty and air pollution in the contiguous United States is a widely debated issue, often suggesting that impoverished areas suffer higher pollution levels due to socioeconomic disparities. However, existing studies frequently lack the integration of advanced analytical techniques and fail to account for a comprehensive range of variables. This research paper addresses these gaps by employing sophisticated machine learning models to analyze an extensive dataset encompassing various socioeconomic and environmental metrics. Utilizing linear regression, decision trees, and neural networks, we rigorously examined the data. While the variables were able to predict county-wide poverty rates to reasonably low RMSE and MAE values, our results indicate no significant correlation between poverty levels alone with air pollution indices. The latter finding challenges conventional understanding and highlights the complexity of the relationship between socioeconomic status and environmental quality. By offering a data-driven perspective, our study encourages policymakers to reconsider the factors influencing environmental justice and to look beyond economic status alone. Our work underscores the necessity for further investigation into other potential determinants of pollution, contributing to the discourse on environmental equity. This research provides a fresh, nuanced view that questions established beliefs and underscores the multifaceted nature of socioeconomic-environmental interactions.

Neural mechanisms underlying the interactive exchange of facial emotional expressions.

Facial emotional expressions are crucial in face-to-face social interactions, and recent findings have highlighted their interactive nature. However, the underlying neural mechanisms remain unclear. This EEG study investigated whether the interactive exchange of facial expressions modulates socio-emotional processing. Participants (N = 41) displayed a facial emotional expression (angry, neutral, or happy) towards a virtual agent, and the agent then responded with a further emotional expression (angry or happy) or remained neutral (control condition). We assessed subjective experience (valence, arousal), facial EMG (Zygomaticus, Corrugator), and ERPs (EPN, LPP) elicited by the agent's response. Replicating previous findings, we found that an agent's happy facial expression was experienced as more pleasant and elicited increased Zygomaticus activity when participants had initiated the interaction with a happy compared to an angry expression. At the neural level, angry expressions resulted in a greater LPP than happy expressions, but only when participants directed an angry or happy, but not a neutral, expression at the agent. These findings suggest that sending an emotional expression increases salience and enhances the processing of received emotional expressions, indicating that an interactive setting alters brain responses to social stimuli.

DYNAMIC OBSERVATION OF UPPER AIRWAYS COLONIZATION IN A CYSTIC FIBROSIS PATIENT INFECTED WITH ROSEOMONAS AEROFRIGIDENS UNDER TARGETED THERAPY

Abstract Cystic fibrosis (CF) is a disease characterized by a varying microbiological diversity, including bacteria such as Pseudomonas aeruginosa, Burkholderia cepacia complex, Achromobacter xylosoxidans/ruhlandii. Additionally, there are microorganisms with unknown clinical significance, including Roseomonas aerofrigidens. The main goal of the study is to evaluate the prevalence of such microorganisms among CF patients undergoing regular examinations at the microbiological laboratory of the Samara State Medical University, Ministry of Health of the Russian Federation. An analysis of 12,094 clinical material cultures was carried out, with identification done by using MALDI-ToF mass spectrometry. A composite correlation index was calculated. The Jaccard similarity coefficient was used to assess the nature of symbiotic or antagonistic interactions. A total of 20 strains of Roseomonas spp. bacteria was isolated. Four strains were obtained from the mucous membrane of the posterior pharyngeal wall, while the remaining strains – from nasal lavage fluid. The patient with F508del/F508del genetics had been receiving dual targeted therapy since October 2022. Before treatment, no clinically significant Gram-negative species or Roseomonas aerofrigidens strains were noted in the patient’s anamnesis. However, during observation, Roseomonas aerofrigidens cultures were obtained from nasal lavage fluid at the third, fifth, sixth, eighth, ninth, eleventh, and eighteenth months of observation, with no concurrent bacterial strain growth in sputum samples. The study of the protein profiles demonstrated a high degree of kinship among the isolates, with a correlation index exceeding 0.8, confirming the hypothesis on long-term colonization of the paranasal sinuses by same Roseomonas aerofrigidens strain. While assessing the consistency criterion, it was found that among 30 microbial species isolated from nasal lavage fluid over 18-month observation, two microorganisms were classified as representatives of the constant microbiota, four (including Roseomonas aerofrigidens) as additional microbiota. The remaining species belong to random microbiota. Based on the Jaccard coefficient, it was found that four pairs of microorganisms are characterized by a synergistic nature of the relationship. These observations demonstrate a case of multi-month colonization with a unique strain of Roseomonas aerofrigidens in a patient with cystic fibrosis receiving targeted therapy.

Molecular Insights into the Adsorption of Deposit Control Additives from Hydrocarbon Fuels.

Engine deposits can reduce performance and increase emissions, particularly for modern direct-injection fuel delivery systems. Surfactants known as deposit control additives (DCAs) adsorb and self-assemble on the surface of deposit precursors to keep them suspended in the fuel. Here, we show how molecular simulations can be used to virtually screen the ability of surfactants to bind to polyaromatic hydrocarbons, comprising a major class of carbonaceous deposits. We use molecular dynamics with the adaptive biasing force method to generate the potential of mean force as a function of the vertical distance between the surfactants and deposits in gasoline and diesel fuel surrogates. We find that a zwitterionic surfactant outperforms a conventional polyisobutylene succinimide for binding to these aromatic species. The amine groups in the succinimide headgroup only weakly adsorb on the polyaromatic deposit, while additional functional groups in the zwitterionic surfactant, particularly the quarternary ammonium ion, markedly enhance the binding strength. We decompose the adsorption free energies of the surfactants into their entropic and enthalpic components, to find that the latter dominates the attraction from these non-aqueous solvents. The adsorption free energy of both surfactants is slightly weaker from n-hexadecane (diesel) than iso-octane (gasoline), which is due to the larger steric barrier from stronger molecular layering of the former on the deposit. Density functional theory calculations of the adsorption of DCA fragments validate the force field used in the molecular dynamics simulations and provide further insights into the nature of the intermolecular interactions. The approach introduced here shows considerable promise for accelerating the discovery of novel DCAs to facilitate more advanced fuel formulations to reduce emissions.

Cyclodextrin Derivatives as Modulators for Enhanced Drug Delivery from Niosome Membrane: A Fluorescence Correlation Spectroscopy and Isothermal Titration Calorimetry Approach.

Designing efficient drug delivery systems for optimum therapeutic outcomes and minimum adverse effects remains a pivotal focus in pharmaceutical research. Understanding the nature of interactions between drugs and drug carriers and the drug-release mechanism are the key aspects for the development of effective delivery systems. This work presents a detailed investigation into the intricate interactions between niosomes and the drug Phenosafranin (PSF), and the subsequent release induced by a variety of cyclodextrins (CDs) employing a multifaceted approach. Ensemble average spectroscopic and single molecular level investigations based on fluorescence correlation spectroscopy (FCS), are employed to explore the binding interactions of PSF with the niosome membrane. Subsequently, the release of the drug was studied by disrupting the niosome structure using various CDs, and their efficacy was accessed through steady-state and time-resolved photophysical responses. FCS experiments provided precise insights into the binding and drug release process at the single-molecule level through the variation in translational and diffusion characteristics of the drug. Additionally, isothermal titration calorimetric (ITC) investigations further revealed the thermodynamics governing the CD-niosome host:guest interactions and the varying potential of different CDs in disrupting the niosome to release the drug which were further validated by electron microscopy and confocal fluorescence microscopy analyses. A broader analysis of niosomes prepared with various nonionic surfactants highlighted the influence of cavitand size and structure on the interaction with different niosome constituents. This comprehensive analysis sheds light on the complex interplay of these components and their interactions, providing insights into drug delivery systems and their potential therapeutic applications.