Interactive Framework Research Articles

Bridging the implementation gap for climate mitigation in ASEAN: a comprehensive capacity-building framework

PurposeThe paper systematically examines the capacity building needs of energy and climate stakeholders in the Association of Southeast Asian Nations (ASEAN). It looks at conditions and opportunities for improvements in institutional, organisational, technological, innovation and financing capacities. This paper provides a guide to concrete capacity building programs and implementations to accelerate the implementation of National Determined Contributions (NDCs) and low-carbon energy transition in the ASEAN region.Design/methodology/approachThis paper proposes a comprehensive capacity-building framework, drawing on transition management theory and the interactive systems framework for capacity building. The assessment is based on interviews with representatives of the ministry responsible for energy policy and the ministry responsible for climate policy in each ASEAN country, as well as a survey among a broader set of Southeast Asian energy and climate experts from academia, think tanks and international development partners.FindingsThe paper identifies the priority areas for capacity building for each ASEAN country and the region as a whole. Each country has a unique set of needs and priorities. At the regional level, the widest capacity gaps were observed in institutional capacity, technical capacity, human resources capacity, financing capacity and the capacity to develop policy and legislation. Specific gaps for capacity building are discussed in delivering strategic areas of energy transition, such as electrification of transportation, development of the green supply chain, deploying renewable energy, energy efficiency, strengthening finance and investment and reducing dependencies on fossil fuels.Originality/valueThis paper helps fill the gap for detailed capacity needs analysis and facilitates long-term plans/strategies and their implementation. The insights help to increase ASEAN energy and climate stakeholders’ understanding of the interaction between energy and climate, therefore enhanced capability in developing more effective action maps and intervention points in achieving NDCs and sustainable development goals.

Doing Virtual Companionship with Alexa

Technologists often claim that virtual assistants, e.g., smart speakers, can offer 'smart companionship for independent older people'. However, the concept of companionship manifested by such technologies is rarely explained further. Studies of virtual assistants as assistive technologies have tended to conceptualise companionship as a 'special form of friendship' or as a way of strengthening 'psychological wellbeing' and 'emotional resilience'. While these abstractions can be measured using psychological indices or self-report, they are not necessarily informative about how 'virtual companionship' may be performed in everyday interaction. This case study focuses on how a virtual assistant is used by a person living with dementia and asks to what extent it takes on a role recognizable, from interactional studies, as 'doing companionship'. We draw on naturalistic video data featuring a person living with dementia in her own home using a smart speaker. Our results show how actions such as complaints about and blamings directed towards the device are achieved through shifts of ‘footing’ between turns that are ostensibly ‘talk to oneself’ and turns designed to occasion a response. Our findings have implications for the design, feasibility, and ethics of virtual assistants as companions, and for our understanding of the embedded ontological assumptions, interactive participation frameworks, and conversational roles involved in doing companionship with machines.

Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

Road safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This article proposes a control optimiser interaction framework (COIF) for organising information transmission between the AV and interacting “Thing”. The framework relies on the neuro-batch learning algorithm to improve the consistency measure’s adaptability with the interacting “Things”. In the information-sharing process, the maximum extraction and utilisation are computed to track the AV with precise environmental knowledge. The interactions are batched with the type of traffic information obtained, such as population, accidents, objects, hindrances, etc. Throughout travel, the vehicle’s learning rate and the surrounding environment’s familiarity with it are classified. The learning neurons are connected to the information actuated and sensed by the AV to identify any unsafe vehicle activity in unknown or unidentified scenarios. Based on the risk and driving parameters, the safe and unsafe activity of the vehicles is categorised with a precise learning rate. Therefore, minor changes in vehicular decisions are monitored, and driving control is optimised accordingly to retain 7.93% of navigation assistance through a 9.76% high learning rate for different intervals.

Towards a Common Metrics and Evaluation Framework for Assessment of Older Adults and Caregivers Interacting with Artificial Intelligence

Artificial intelligence (AI) has applications in assisting older adults to age in place and provide support to them and their caregivers as their cognition declines with age. However, effective assessment methods of this technology are needed in order to benchmark their performance and a common set of metrics and evaluation methods would enable such assessments to be compared to one another. To this end, we propose a common framework for human-AI interaction involving care recipients and their care networks. From the results of a literature review exercise, a framework with sample metrics, related measures, qualified evaluation tools, and contextual factors that impact assessment are reviewed. This paper provides a sample of common metrics in one of the framework’s measurement spaces (human-AI interaction) and discusses some of the impacts of contextual factors and how use of the common metrics and evaluation framework can be used for meta-analysis and to guide future research. Additional future articles are planned to cover the other measurement spaces in the framework (system performance, task performance, and well-being), including their particular common metrics and evaluation methods. This effort aims to provide guidance for researchers in this domain as well as highlight measurement gaps that can be filled by future research.

A Framework for Multimodal Medical Image Interaction.

Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed.

Evaluation of the cross sections and photoelectron angular distribution parameters following atomic photoionization under extreme conditions

In this manuscript, we report on a theoretical study of the atomic structures, cross sections, and photoelectron angular distribution parameters following the atomic photoionization under extreme conditions. To achieve this goal, a relativistic approach using the Dirac-Coulomb Hamiltonian within the framework of relativistic configuration interaction, taking advantage of independent particle basis wave functions, is proposed. To describe the interaction of charged particles in the ideal and non-ideal plasmas, the Debye potential and the pseudopotential are implemented, the latter being derived from a progressive resolution of the Bogolyubov chain equations. Both bound and continuous state wave functions, essential for a comprehensive understanding of quantum systems, are determined from the modified local central potential, which is obtained in a self-consistent manner to represent the electronic shielding effect on the nuclear potential. The photoionization processes are evaluated using the relativistic distorted wave approach, which is consistent with the principles of relativistic Dirac theory and thus provides an accurate description of the dynamics. As a test desk, the present method is applied to the evaluation of the energies, ionization potentials, wave functions, cross sections, and photoelectron angular distribution parameters, using the single photon ionization of the Li-like Fe XXIV ions as the basis for analysis. Our results demonstrate that the plasma environment effect not only decreases the ionization potentials and increases the cross sections but also affects the photoelectron angular distribution parameters across different shells, leading to a more balanced and symmetrical photoelectron distribution pattern. A detailed comparison is made between our results, and the available well-established theoretical predictions and experimental data of the unshielded case in the literature shows a good agreement. The present work provides novel insights and theoretical models that not only help us to better understand the fundamental properties of the complex systems but are also beneficial for innovative applications in the study of astrophysical and laboratory plasmas.

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework

Aggregate gradation plays a crucial role in determining the asphalt pavement performance, necessitating assessment post-construction. Additionally, efficient gradation measurement is vital for the design and construction of reclaimed pavement. This study proposes a methodology for predicting gradation based on imagery. Initially, a scanning device is devised to capture unfolded side surface images of pavement core samples. Subsequently, an interactive software named PyImageJ is developed for image post-processing and acquiring aggregate feature information. A morphological model is then introduced to determine the mass ratios among various grading levels of coarse aggregates with nominal particle sizes greater than 2.36 mm. Finally, sequence prediction models are established to forecast the mass ratios among different grading levels of fine aggregates with nominal particle sizes smaller than 2.36 mm. Simple conversion of the mass ratios can yield the gradation. Experimental results indicate that the Long Short-Term Memory (LSTM) network offers more accurate predictions for the mass ratios of fine aggregates in stone mastic asphalt (SMA-13), with an R-squared value of 0.8985. Conversely, one-dimensional convolutional neural networks (1D CNN) are better suited for predicting the fine aggregate mass ratios in asphalt concrete (AC-20 and AC-25), with R-squared values of 0.9010 and 0.8824, respectively. Visualization results demonstrate that the predicted complete gradation closely aligns with the actual values (R-squared > 0.99), opening a new avenue for evaluating pavement quality.

Transregional Cooperation of German Federal States with Eastern Regions and Countries (Late 20th — 21st Century)

This study examines the formation and development of transregional relations between the federal states of Germany (Bundesländer) and Eastern countries and regions. It presents the findings of a normative-legal analysis of the foundations of international and foreign policy activities of German federal states, based on national legislation. The focus is on the institutional framework for cross-border interactions between individual administrative units of the Federal Republic of Germany and their foreign partners. The research explores partnerships with both neighboring Middle Eastern territories and more distant Eastern regions. The findings indicate that the international and foreign economic activities of the German states are extensive, aligning with the opportunities outlined in Germany’s constitutional documents, and are significantly broader and more diverse compared to similar activities in the regions of France and Italy. It has been identified that German states engage in a system of “horizontal partnerships” primarily with provinces of the People’s Republic of China. Additionally, it is reported that a principle of “network cooperation” is applied to most other Eastern countries, encompassing not only economic but also scientific, cultural, and humanitarian ties. The study demonstrates that among the examined EU countries at the regional level, German federal states lead in establishing the most successful and effective partnerships with regions in Eastern countries.

Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap

This study used bibliometric analysis and a systematic literature review (SLR) to examine how the tourism and hospitality industries use generative artificial intelligence (GAI), identifying developed patterns, theoretical frameworks, strengths and limitations, and future research challenges. We conducted a systematic review using the Scopus database, adhering to PRISMA principles. We analyzed a sample of 25 articles published between 2019 and 2023 through narrative synthesis and bibliometric analysis using the VOSviewer software, a tool for visualizing network analysis. The USA, China, India, and Saudi Arabia are the major countries engaged in GAI research in tourism and hospitality. Significant research topics emphasize decision-making, chatbots, deep learning, and sentiment analysis, mainly through the Technology Acceptance Model (TAM), Stimulus-Organism-Response (S-O-R), and Human-Computer Interaction (HCI) frameworks. GAI applications demonstrate strength in improving user experience and operational efficiency, though gaps exist in scope, ethics, technology performance, and collaboration between humans and AI. This study, therefore, provides a fundamental foundation for understanding the current status of GAI research in tourism and hospitality by pointing out some trends and areas that require further investigation to ensure the responsible and effective integration of AI within the industry.

Ecological protection makes the ecological Kuznets curve turning point come earlier

Exploring the relationship between land use cover/change (LUCC) and ecosystem service value (ESV) under different future scenarios can provide guidance for selecting future development patterns and for the scientific utilization of land resources in the region. In this study, LUCC under different scenarios in the North Slope of Tianshan Mountain (NSTM) was simulated using the PLUS model. The ESV coefficients were adjusted for regional differences and social development factors to better reflect the actual situation in the study area. The interactions between LUCC, landscape pattern (LSP), and ESV were systematically analyzed, while at the same time, ESV and the level of economic development were fitted to the Ecological Kuznets Curve, which was then used to determine its trend and inflection point. The following conclusions were drawn: (1) Cropland and unused land are the main types of land use change in the NSTM, both historically and in the future. Cropland shows an increase in the natural development scenario and a decrease in the ecological protection scenario. Unused land shows an increase in the different development scenarios, indicating that unused land has higher development potential in the NSTM. NSTM shows a continuous decrease in ESV in the natural development scenario and a continuous increase in ESV in the ecological protection scenario. (2) LSP in both historical and future NSTM have evolved to show fragmentation, heterogeneity, and complexity in patch forms. However, this trend is slower in the ecological protection scenario than in the natural development scenario. LUCC, LSP, and ESV form an integrated framework of interactions, where LUCC influences ESV through LSP, and changes in ESV feedback to LUCC through LSP, which acts as a bridging mediator. (3) The Ecological Kuznets Curve of NSTM exhibits an N-shape, showing a clear overall rightward trend across different development scenarios at the annual level. At the interannual level, the curves for the natural development scenario are situated in the middle of the declining phase of the N-shape, with no ecological inflection point occurring during the study period. In contrast, the curves for the ecological protection scenario display a declining-ascending trend, with the ecological inflection point occurring when per capita GDP reaches 2.5 × 10^6 CNY.

The case for a Framework for UnderStanding Ice-Ocean iNteractions (FUSION) in the Antarctic-Southern Ocean system

We are in a period of rapidly accelerating change across the Antarctic continent and Southern Ocean, with land ice loss leading to sea level rise and multiple other climate impacts. The ice-ocean interactions that dominate the current ice loss signal are a key underdeveloped area of knowledge. The paucity of direct and continuous observations leads to high uncertainty in the glaciological, oceanographic and atmospheric fields required to constrain ice-ocean interactions, and there is a lack of standardised protocols for reconciling observations across different platforms and technologies and modelled outputs. Funding to support observational campaigns is under increasing pressure, including for long-term, internationally coordinated monitoring plans for the Antarctic continent and Southern Ocean. In this Practice Bridge article, we outline research priorities highlighted by the international ice-ocean community and propose the development of a Framework for UnderStanding Ice-Ocean iNteractions (FUSION), using a combined observational-modelling approach, to address these issues. Finally, we propose an implementation plan for putting FUSION into practice by focusing first on an essential variable in ice-ocean interactions: ocean-driven ice shelf melt.

The relationship between square dance exercise and quality of life in middle-aged and older adults: chain mediated effects of negative emotions and attitudes toward aging.

With the continuous development of society and the economy, population aging has become an inevitable global development trend, triggering a series of social problems and having a more serious impact on the physical and mental health of middle-aged and older adults. Physical exercise is one of the key factors for improving the overall health and quality of life of individuals, but the role of square dance exercise and the quality of life of middle-aged and older adults have not yet been clarified. This study aimed to investigate the associations between square dance exercise and the quality of life of middle-aged and older adults and its mechanism of action, including its effects on physical health and mental health, with negative emotions and attitudes towards aging as mediating variables. This study adopted a cross-sectional research method, using the Physical Activity Rating Scale (PARS-3), Depression-Anxiety-Stress Scale in Simplified Chinese (DASS-21), Attitudes toward Aging Questionnaire (AAQ), and 12-item Short Form of the Health Survey (SF-12), and launched an online survey on the QuestionStar platform from August-September 2023 for 4,636 middle and older adults aged 45-74 years. The survey results were analyzed via Pearson correlation analysis via SPSS 27.0 and structural equation modeling analysis via Mplus 8.3 to test the model fit and mediation effects. Square dance exercise was significantly correlated with negative emotions, attitudes towards aging, and quality of life (r = -0.042-0.878, p < 0.01). Square dance exercise significantly and positively predicted quality of life (β = 0. 202, p < 0.001). Negative emotions and attitudes towards aging are chain mediators of square dance exercise and quality of life, and the mediation consists of three pathways: separate mediation of negative emotions, separate mediation of attitudes towards aging, and chain mediation of negative emotions-attitudes towards aging. This study is dedicated to deepening the scientific role of how square dancing, as a health-promoting activity, can optimize the quality of life of middle-aged and older adults through the mediating mechanism of negative emotion alleviation and positive attitudes towards aging and then optimize the quality of life of middle-aged and older adults. This process not only enriches the theoretical framework of the complex interactions between psychosocial factors and healthy aging but also provides a solid empirical foundation for the development of health intervention strategies aimed at enhancing the overall well-being and quality of life of the middle-aged and older adults.

Impact of Nepal-India Relations on Arun III Hydroelectric Project Implementation

The Arun III Hydroelectric Project, Nepal's largest hydropower enterprise, is a cornerstone of Nepal-India bilateral ties, demonstrating the intricate interaction of energy diplomacy, political dynamics, and economic cooperation. This paper investigates how these bilateral ties have impacted the implementation of the Arun III project, from its inception to its current evolution. It examines the historical framework of Nepal-India interactions, including political agreements, economic interdependence, and regional geopolitics, which have influenced the project's progress. The article also discusses the obstacles that have hampered the project's timely completion, such as border conflicts, economic blockades, and changes in political leadership. It also looks at the opportunities that come with effective partnership, with a focus on energy commerce and sustainable development in South Asia. This paper, using international relations theory and detailed case studies, provides a comprehensive understanding of how bilateral relations between Nepal and India have shaped the Arun III project's trajectory, as well as insights into the broader implications for cross-border infrastructure development in the region.

English Vocabulary Learning System Web Based Using Rapid Application Method

English lexicon dominance is one of the imperative viewpoints in dialect learning at the Kindergarten (TK) level. Be that as it may, ordinary strategies utilized in lexicon learning are frequently less curiously and less successful in spurring understudies to memorize. The foundation of this think about is the require for a more interactive learning framework that's in understanding with innovative advancements to back the educating and learning handle at TK Al Amanah. The issue confronted is how to plan a lexicon learning framework that can increment students' intrigued and understanding successfully. As a arrangement, this consider proposes the improvement of a web-based English lexicon learning framework utilizing the Fast Application Advancement (RAD) strategy. The RAD strategy was chosen since it is able to create quick models and include clients in each organize of improvement, so that the coming about system can be more in agreement with the requirements of clients, in this case understudies and instructors at TK Al Amanah. The framework developed includes highlights such as intelligently works out, advanced flashcards, and tests planned to create it simpler for understudies to memorize and get it lexicon. The investigate strategy includes needs examination, framework plan, model advancement, testing, and assessment. The trial comes about appear that this framework isn't only able to extend understudy inspiration and interest, but too gives comfort for instructors in conveying learning materials. Hence, this web-based learning framework offers an innovative solution that can bolster the method of educating English lexicon at the kindergarten level

Bidirectional associations between parenting stress and coparenting: A longitudinal actor-partner interdependence analysis.

Although parents' parenting stress has been found to be related to coparenting, existing research has mostly focused only on one parent or examined the unidirectional effects between the two factors. The present study investigated the actor and partner effects as well as the bidirectional association between parents' parenting stress and coparenting in Chinese society. The study participants were 522 Chinese father-mother dyads with elementary school-aged children who followed for two measurement occasions (half a year apart). The cross-lagged actor-partner interdependence model (APIM) was used to examine the actor effects and partner effects in the bidirectional associations between both parents' parenting stress and coparenting. The findings revealed that both fathers' and mothers' parenting stress significantly negatively predicted their spouses' coparenting (partner effects), and mothers' coparenting significantly negatively predicted their spouses' parenting stress (partner effects). However, the actor effects between parents' parenting stress and coparenting were not significant. Furthermore, there were significant bidirectional associations between fathers' parenting stress and mothers' coparenting. Findings from the present study reveal the dynamic interaction between parenting stress and coparenting. The results point to the need for providing preventive interventions to parents with high levels of parenting stress and establishing a collaborative interaction framework between parents.

Conceptual Frameworks for Conversational Human-AI Interaction (CHAI) in Professional Contexts

Artificial intelligence (AI) is revolutionizing sectors like financial services, healthcare, and education, driving unprecedented progress and fostering innovation across domains. In this backdrop, basic conversational interface aka chat emerged as the predominant way to interact with AI systems. However, the current Human-AI (H2AI) conversations are fraught with a host of challenges necessitating a critical exploration into their design, strategy, and implications. Human-AI interaction design is hindered by fragmented and disjointed technology-driven approaches that lack design-led strategies to address emotional, adaptive, and holistic aspects of the field. This systematic study addressed this issue by developing conceptual models and frameworks integrating emotional, adaptive, and holistic social dynamics into Human-AI conversation design in professional settings. The comprehensive literature review spanning communication studies, user experience frameworks, design process models and conversational AI technologies revealed four research gaps. Through a multiple case study analysis across various industries, we developed four significant recommendations to enhance Human-AI interaction design. First, a typology of 12 Conversational Archetypes was established, providing a framework to inform dynamic and purpose led conversations in professional settings. Second, the Adaptive Conversational Interaction Dynamics (ACID) framework was introduced, integrating five dimensions—Conversation Management, Expertise and Competence, Emotional Intelligence, Trust and Credibility, and Personalization—to improve user engagement and satisfaction. Third, the Dynamic Experience Design (DxD) process emphasized a symbiotic approach with User Resonant Design principles and to create emotionally resilient and adaptable AI systems. Finally, the Conversational Human-AI Interaction (CHAI) framework integrated interactional, emotional, and ethical dimensions, ensuring AI systems are empathetic and ethically grounded. These contributions offer a comprehensive approach to designing advanced conversational AI that is responsive and adaptive. Further research needs to be undertaken to validate these frameworks through qualitative studies to ensure applicability across wider contexts, scenarios and cultures.

SPECIFICS OF LEGAL REGULATION OF VIRTUAL SPACE

The relevance of this research is driven by the rapid development of information technologies and global digitalization processes, which have led to the emergence of virtual space as a new environment for social interactions. Virtual space has become an integral part of modern life, influencing various areas of society. However, despite its growing importance, the existing legal frameworks have not fully adapted to the specific characteristics of virtual space, such as its anonymity, global nature, and dynamic structure. The purpose of this article is to examine the peculiarities of legal regulation in virtual space and to propose effective mechanisms to ensure legal governance in the context of digitalization. This study addresses the gaps in the current legal system and explores ways to adapt legal norms to meet the demands of the digital environment. The study is particularly timely, given the increasing impact of digital technologies on social relations and the need for appropriate legal frameworks to regulate interactions within virtual space. The research methodology is based on an analysis of recent studies and publications, highlighting the growing interest of scholars in the legal regulation of digital technologies and virtual interactions. While there is a substantial body of work on virtual assets and digital rights, the issue of comprehensive legal regulation of virtual space remains underexplored. This article fills that gap by focusing on the development of cross-sectoral legal mechanisms and emphasizing the need for harmonization with international standards. The interdisciplinary nature of virtual space requires legal frameworks that can address the complexity of digital interactions, taking into account both national and international legal norms. The findings of the study show that for effective regulation of virtual space, it is essential to adapt existing legal mechanisms to the specific conditions of the digital environment. This includes the integration of international legal norms, the development of self-regulation tools, and the creation of a comprehensive legal system that can manage social relations in virtual space. The practical value of the article lies in its analysis of the challenges facing legal regulation in virtual space and the solutions proposed for improving the effectiveness of legal mechanisms. These recommendations can be used by policymakers and legal practitioners to develop a more robust regulatory framework for virtual interactions. Only through a comprehensive and flexible approach to regulation can legal systems ensure the orderly conduct of social relations in virtual space and protect the rights of individuals in the digital age.

Legal regulation of environmental entrepreneurship: interaction between the state and business

The article explores the important aspects of the development of legal regulation of environmental entrepreneurship in the Russian Federation in the framework of interaction between the state and business. The relevance of the study of legal regulation of ecological entrepreneurship in the Russian Federation is conditioned by the ongoing changes in the sphere of politics, economics, law and ecology. Despite the importance of this form of economic activity for the development of the economy in its crisis state, there is a noticeable shortage of legal norms in the legislation, the subject of regulation of which would be environmental entrepreneurship. The article reveals the basics of state policy in the field of ecology, examines the problems of interaction between business and the state. It is emphasized that the level of development of the sphere of ecology largely determines the process of economic activity, which directly involves natural resources, directly using them as the main means of profit making and often having an indirect negative impact on the natural environment.

Deciphering the Intricate Interplay in the Framework of Antibiotic-Drug Interactions: A Narrative Review.

Drug interactions are a significant and integral part of the concept of medication-related adverse events, whether referring to potential interactions or those currently observed in real-world conditions. The high global consumption of antibiotics and their pharmacokinetic and pharmacodynamic mechanisms make antibiotic-drug interactions a key element that requires continuous study due to their clinical relevance. In the present work, the current state of knowledge on antibiotic-drug interactions, which are less studied than other drug-drug interactions despite their frequent use in acute settings, has been consolidated and updated. The focus was on the interactions of the commonly used antibiotics in clinical practice, on the characteristics of the geriatric population susceptible to interactions, and on the impact of online drug interaction checkers. Additionally, strategies for optimizing the management of these interactions, including spacing out administrations, monitoring, or avoiding certain combinations, are suggested. Sustained research and careful monitoring are critical for improving antibiotic safety and efficacy, especially in susceptible populations, to enhance precision in managing antibiotic-drug interactions.

The omnichannel effect: Evaluating consumer satisfaction in the wake of e-commerce closure among Bandung's MSMEs

This research delves into the impact of the government's closure of TikTok shop's online marketing interaction channel on MSMEs, emphasizing the reduction in offline interaction channels and resulting business losses. The study's primary objective is to dissect and assess the multifaceted omnichannel interaction framework, comprising online, offline, and cross channels, and its influence on consumer experience value and satisfaction. Conducted in Bandung, Indonesia, the research engaged 425 participants who interact through various marketing channels. Utilizing the partial least squares path model, the study deduces that while online and cross channels significantly influence experience value and consumer satisfaction, offline channels do not. This insight calls for a reevaluation of the TikTok shop closure decision, considering the profound role of satisfaction in purchasing decisions. The research, however, recognizes its limitations in terms of the temporal scope, analytical methods, and potential for hypothesis refinement. Future studies could broaden the geographic scope to incorporate diverse cities, offering insights shaped by varying local characteristics. Overall, the study contributes to the understanding of omnichannel interactions' effects on consumer satisfaction, presenting a nuanced perspective for academics and practitioners to enhance consumer shopping experiences.