Bridging the gap or widening the divide? Exploring the dual role of social capital in healthcare financing in rural Uttar Pradesh, India.

Social capital, defined as the nature of the social relationship and the resources embedded within the social network of an individual or community, influences how individuals within a group interact and collaborate within their communities or organizations. While it is acknowledged that social capital can be drawn from as a coping strategy to mitigate financial stress, there is a notable absence of the lived experience in the literature on how social capital influences households to tap resources from their social network. We have investigated the role of social capital in healthcare financing in rural Uttar Pradesh, India, highlighting the challenges faced by households in managing healthcare expenses. We took a qualitative research approach, conducting in-depth interviews with 24 households in the Hardoi District of Uttar Pradesh in August 2017 to explore participants' lived experience of accruing support from their community during their healthcare crisis. Data analysis followed a thematic content analysis approach. The study finds that households leverage social capital for both financial and non-financial support during health crises. Social networks, trust, and community cohesion play critical roles in resource acquisition. However, overreliance on social capital can be coercive, leading to inequity, privacy invasion, and dependency. Though social capital serves as a crucial resource of support in healthcare emergencies, its unequal distribution and potential for misuse highlight the need for more structured health financing policies in India. The findings underscore the importance of integrating community-driven resources into broader health financing strategies, considering local social structures and community dynamics.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Appraising LaQshya's potential in measuring quality of care for mothers and newborns: a comprehensive review of India's Labor Room Quality Improvement Initiative.

BACKGROUND: Poor intrapartum care in India contributes to high maternal and newborn mortality. India's Labor Room Quality Improvement Initiative (LaQshya) launched in 2017, aims to improve intrapartum care by minimizing complications, enforcing protocols, and promoting respectful maternity care (RMC). However, limited studies pose a challenge to fully examine its potential to assess quality of maternal and newborn care. This study aims to bridge this knowledge gap and reviews LaQshya's ability to assess maternal and newborn care quality. Findings will guide modifications for enhancing LaQshya's effectiveness. METHODS: We reviewed LaQshya's ability to assess the quality of care through a two-step approach: a comprehensive descriptive analysis using document reviews to highlight program attributes, enablers, and challenges affecting LaQshya's quality assessment capability, and a comparison of its measurement parameters with the 352 quality measures outlined in the WHO Standards for Maternal and Newborn Care. Comparing LaQshya with WHO standards offers insights into how its measurement criteria align with global standards for assessing maternity and newborn care quality. RESULTS: LaQshya utilizes several proven catalysts to enhance and measure quality- institutional structures, empirical measures, external validation, certification, and performance incentives for high-quality care. The program also embodies contemporary methods like quality circles, rapid improvement cycles, ongoing facility training, and plan-do-check, and act (PDCA) strategies for sustained quality enhancement. Key drivers of LaQshya's assessment are- leadership, staff mentoring, digital infrastructure and stakeholder engagement from certified facilities. However, governance issues, understaffing, unclear directives, competency gaps, staff reluctance towards new quality improvement approaches inhibit the program, and its capacity to enhance quality of care. LaQshya addresses 76% of WHO's 352 quality measures for maternal and newborn care but lacks comprehensive assessment of crucial elements: harmful labor practices, mistreatment of mothers or newborns, childbirth support, and effective clinical leadership and supervision. CONCLUSION: LaQshya is a powerful model for evaluating quality of care, surpassing other global assessment tools. To achieve its maximum potential, we suggest strengthening district governance structures and offering tailored training programs for RMC and other new quality processes. Furthermore, expanding its quality measurement metrics to effectively assess provider accountability, patient outcomes, rights, staff supervision, and health facility leadership will increase its ability to assess quality improvements.

Comparing multifunctional viral and eukaryotic proteins for generating scission necks in membranes.

Deterministic formation of membrane scission necks by protein machinery with multiplexed functions is critical in biology. A microbial example is the M2 viroporin, a proton pump from the influenza A virus which is multiplexed with membrane remodeling activity to induce budding and scission in the host membrane during viral maturation. In comparison, the dynamin family constitutes a class of eukaryotic proteins implicated in mitochondrial fission, as well as various budding and endocytosis pathways. In the case of Dnm1, the mitochondrial fission protein in yeast, the membrane remodeling activity is multiplexed with mechanoenzyme activity to create fission necks. It is not clear why these functions are combined in these scission processes, which occur in drastically different compositions and solution conditions. In general, direct experimental access to changing neck sizes induced by individual proteins or peptide fragments is challenging due to the nanoscale dimensions and influence of thermal fluctuations. Here, we use a mechanical model to estimate the size of scission necks by leveraging Small-Angle X-ray Scattering (SAXS) structural data of protein-lipid systems under different conditions. The influence of interfacial tension, lipid composition, and membrane budding morphology on the size of the induced scission necks is systematically investigated using our data and molecular dynamic simulations. We find that the M2 budding protein from the influenza A virus has robust pH-dependent membrane activity that induces nanoscopic necks within the range of spontaneous hemi-fission for a broad range of lipid compositions. In contrast, the sizes of scission necks generated by mitochondrial fission proteins strongly depend on lipid composition, which suggests a role for mechanical constriction.

Origin of the nonlinear structural and mechanical properties in oppositely curved lipid mixtures.

Structural and mechanical properties of membranes such as thickness, tail order, bending modulus and curvature energetics play crucial role in controlling various cellular functions that depend on the local lipid organization and membrane reshaping. While behavior of these biophysical properties are well understood in single component membranes, very little is known about how do they change in the mixed lipid membranes. Often various properties of the mixed lipid bilayers are assumed to change linearly with the mole fractions of the constituent lipids which, however, is true for "ideal" mixing only. In this study, using molecular dynamics simulations, we show that structural and mechanical properties of binary lipid mixture change nonlinearly with the lipid mole fractions, and the strength of the nonlinearity depends on two factors - spontaneous curvature difference and locally inhomogeneous interactions between the lipid components.

Dipole moment as the underlying mechanism for enhancing the immobilization of glucose oxidase by ferrocene-chitosan for superior specificity non-invasive glucose sensing.

Non-invasive methods for sensing glucose levels are highly desirable due to the comfortableness, simplicity, and lack of infection risk. However, the insufficient accuracy and ease of interference limit their practical medical applications. Here, we develop a non-invasive salivary glucose biosensor based on a ferrocene-chitosan (Fc-Chit) modified carbon nanotube (CNT) electrode through a simple drop-casting method. Compared with previous studies that relied mainly on trial and error for evaluation, this is the first time that dipole moment was proposed to optimize the electron-mediated Fc-Chit, demonstrating sturdy immobilization of glucose oxidase (GOx) on the electrode and improving the electron transfer process. Thus, the superior sensing sensitivity of the biosensor can achieve 119.97 µA mM-1 cm-2 in phosphate buffered saline (PBS) solution over a wide sensing range of 20-800 µM. Additionally, the biosensor exhibited high stability (retaining 95.0% after three weeks) and high specificity toward glucose in the presence of various interferents, attributed to the specific sites enabling GOx to be sturdily immobilized on the electrode. The results not only provide a facile solution for accurate and regular screening of blood glucose levels via saliva tests but also pave the way for designing enzymatic biosensors with specific enzyme immobilization through fundamental quantum calculations.

Simulation study of membrane bending by protein crowding: a case study with the epsin N-terminal homology domain.

The mechanisms by which peripheral membrane proteins generate curvature is currently an active area of research. One of the proposed mechanisms is amphipathic insertion or the 'wedge' mechanism in which the protein shallowly inserts an amphipathic helix inside the membrane to drive the curvature. However, recent experimental studies have challenged the efficiency of the 'wedge' mechanism as it requires unusual protein densities. These studies proposed an alternative mechanism, namely 'protein-crowding', in which the lateral pressure generated by the random collisions among the membrane bound proteins drives the bending. In this study, we employ atomistic and coarse-grained molecular dynamics simulations to investigate the effects of amphipathic insertion and protein crowding on the membrane surface. Considering epsin N-terminal homology (ENTH) domain as a model protein, we show that amphipathic insertion is not essential for membrane bending. Our results suggest that ENTH domains can aggregate on the membrane surface by employing another structured region (H3 helix). And this protein crowding decreases the cohesive energy of the lipid tails which causes a significant decrease in the membrane bending rigidity. The ENTH domain can generate a similar degree of membrane curvature irrespective of the activity of its H0 helix. Our results are consistent with the recent experimental results.

Aurolab aqueous drainage implant implantation made easy: A video-based step-by-step guide.

Background: Glaucoma is one of the major causes of irreversible blindness in the world, with trabeculectomy still being the primary surgical modality for the management of glaucoma. Glaucoma drainage devices (GDDs) have been conventionally used for the treatment of refractory glaucoma and are found to be beneficial in eyes with prior unsuccessful filtration surgeries and primary choice of surgery in certain glaucoma. Aurolab aqueous drainage implant (AADI) is a nonvalved device useful in refractory glaucoma to achieve low intraocular pressure (IOP). The device has been commercially available in India since 2013 and is like the Baerveldt glaucoma implant in design and function. AADI being the most economical and effective GDD in controlling IOP is becoming a popular choice among ophthalmologist in developing countries. AADI surgery has steep learning curve due to large end-plate surface area which needs a rigorous conjunctival dissection, muscle hooking, meticulous plate fixations, and careful tube ligations and insertion. There are different techniques of performing AADI surgery, but the authors have tried to simplify the complex surgery for easy and catchable learning of the procedure by novice surgeon with their experience and have elaborated a step-wise most effective way of performing surgery. Purpose: This video-based skill transfer depicts steps of AADI surgery with compilation of various modifications and authors' tips and tricks to novice surgeons. Synopsis: This video depicts detailed steps of AADI surgery with micro-points and authors experience. Video also shows various tailor-made modifications of surgical techniques for different case scenarios. Highlights: Steps of AADI surgery, modifications, and surgical pearls. Video Link: https://youtu.be/vmVqBb2FvP4.

'All my co-workers are good people, but ': collaboration dynamics between frontline workers in rural Uttar Pradesh, India.

Multisectoral collaboration has been identified as a critical component in a wide variety of health and development initiatives. For India's Integrated Child Development Services (ICDS) scheme, which serves >100 million people annually across more than one million villages, a key point of multisectoral collaboration-or 'convergence', as it is often called in India-is between the three frontline worker cadres jointly responsible for delivering essential maternal and child health and nutritional services throughout the country: the Accredited Social Health Activist (ASHA), Anganwadi worker (AWW) and auxiliary nurse midwife (ANM) or 'AAA' workers. Despite the long-recognized importance of collaboration within this triad, there has been relatively little documentation of what this looks like in practice and what is needed in order to improve it. Informed by a conceptual framework of collaborative governance, this study applies inductive thematic analysis of in-depth interviews with 18 AAA workers and 6 medical officers from 6 villages across three administrative blocks in Hardoi district of Uttar Pradesh state to identify the key elements of collaboration. These are grouped into three broad categories: 'organizational' (including interdependence, role clarity, guidance/support and resource availability); 'relational' (interpersonal and conflict resolution) and 'personal' (flexibility, diligence and locus of control). These findings underscore the importance of 'personal' and 'relational' collaboration features, which are underemphasized in India's ICDS, the largest of its kind globally, and in the multisectoral collaboration literature more broadly-both of which place greater emphasis on 'organizational' aspects of collaboration. These findings are largely consistent with prior studies but are notably different in that they highlight the importance of flexibility, locus of control and conflict resolution in collaborative relationships, all of which relate to one's ability to adapt to unexpected obstacles and find mutually workable solutions with colleagues. From a policy perspective, supporting these key elements of collaboration may involve giving frontline workers more autonomy in how they get the work done, which may in some cases be impeded by additional training to reinforce worker role delineation, closer monitoring or other top-down efforts to push greater convergence. Given the essential role that frontline workers play in multisectoral initiatives in India and around the world, there is a clear need for policymakers and managers to understand the elements affecting collaboration between these workers when designing and implementing programmes.

Precise temporal control of neuroblast migration through combined regulation and feedback of a Wnt receptor.

Many developmental processes depend on precise temporal control of gene expression. We have previously established a theoretical framework for regulatory strategies that can govern such high temporal precision, but experimental validation of these predictions was still lacking. Here, we use the time-dependent expression of a Wnt receptor that controls neuroblast migration in Caenorhabditis elegans as a tractable system to study a robust, cell-intrinsic timing mechanism in vivo. Single-molecule mRNA quantification showed that the expression of the receptor increases non-linearly, a dynamic that is predicted to enhance timing precision over an unregulated, linear increase in timekeeper abundance. We show that this upregulation depends on transcriptional activation, providing in vivo evidence for a model in which the timing of receptor expression is regulated through an accumulating activator that triggers expression when a specific threshold is reached. This timing mechanism acts across a cell division that occurs in the neuroblast lineage and is influenced by the asymmetry of the division. Finally, we show that positive feedback of receptor expression through the canonical Wnt pathway enhances temporal precision. We conclude that robust cell-intrinsic timing can be achieved by combining regulation and feedback of the timekeeper gene.

Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak.

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

Gamma-Ray Irradiation Induced Ultrahigh Room-Temperature Ferromagnetism in MoS2 Sputtered Few-Layered Thin Films.

Defect engineering is of great interest to the two-dimensional (2D) materials community. If nonmagnetic transition-metal dichalcogenides can possess room-temperature ferromagnetism (RTFM) induced by defects, then they will be ideal for application as spintronic materials and also for studying the relation between electronic and magnetic properties of quantum-confined structures. Thus, in this work, we aimed to study gamma-ray irradiation effects on MoS2, which is diamagnetic in nature. We found that gamma-ray exposure up to 9 kGy on few-layered (3.5 nm) MoS2 films induces an ultrahigh saturation magnetization of around 610 emu/cm3 at RT, whereas no significant changes were observed in the structure and magnetism of bulk MoS2 (40 nm) films even after gamma-ray irradiation. The RTFM in a few-layered gamma-ray irradiated sample is most likely due to the bound magnetic polaron created by the spin interaction of Mo 4d ions with trapped electrons present at sulfur vacancies. In addition, density functional theory (DFT) calculations suggest that the defect containing one Mo and two S vacancies is the dominant defect inducing the RTFM in MoS2. These DFT results are consistent with Raman, X-ray photoelectron spectroscopy, and ESR spectroscopy results, and they confirm the breakage of Mo and S bonds and the existence of vacancies after gamma-ray irradiation. Overall, this study suggests that the occurrence of magnetism in gamma-ray irradiated MoS2 few-layered films could be attributed to the synergistic effects of magnetic moments arising from the existence of both Mo and S vacancies as well as lattice distortion of the MoS2 structure.

Numerical and experimental investigation of static wetting morphologies of aqueous drops on lubricated slippery surfaces using a quasi-static approach.

The static wetting behavior of drops on surfaces with thin lubricating films is very different compared to solid surfaces. Due to the slow dynamics of the wetting ridge, it is challenging to predict the apparent contact angles of such drops. It is hypothesized that for a sinking drop on a lubricated surface, quasi-static wetting morphology can be numerically computed from the knowledge of interfacial energies, lubricant thickness, and drop volume. In this study, we use Surface Evolver to numerically compute the static wetting morphology for the four-phase system using a quasi-static approach with a sinking time similar to the early-intermediate times, and the results agree well with the corresponding experiments. We find that the apparent contact angles depend significantly on the lubricant thickness and substrate wettability compared to other parameters.

Everyday capabilities were a path to resilience during COVID-19: a case study of five countries.

COVID-19 demanded urgent responses by all countries, with wide variations in the scope and sustainability of those responses. Scholarship on resilience has increasingly emphasized relational considerations such as norms and power and how they influence health systems' responses to evolving challenges. In this study, we explored what influenced countries' national pandemic responses over time considering a country's capacity to test for COVID-19. To identify countries for inclusion, we used daily reports of COVID-19 cases and testing from 184 countries between 21st January 2020 and 31st December 2020. Countries reporting test data consistently and for at least 105 days were included, yielding a sample of 52 countries. We then sampled five countries representing different geographies, income levels and governance structures (Belgium, Ethiopia, India, Israel and Peru) and conducted semi-structured key informant interviews with stakeholders working in, or deeply familiar with, national responses. Across these five countries, we found that existing health systems capacities and political leadership determined how responses unfolded, while emergency plans or pandemic preparedness documents were not fit-for-purpose. While all five countries were successful at reducing COVID-19 infections at a specific moment in the pandemic, political economy factors complicated the ability to sustain responses, with all countries experiencing larger waves of the virus in 2021 or 2022. Our findings emphasize the continued importance of foundational public health and health systems capacities, bolstered by clear leadership and multisectoral coordination functions. Even in settings with high-level political leadership and a strong multisectoral response, informants wished that they-and their country's health system-were more prepared to address the pandemic and maintain an effective response over time. Our findings challenge emergency preparedness as the dominant frame in pandemic preparedness and call for a continued emphasis on health systems strengthening to respond to future health shocks and a pandemic moving to endemic status.

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty.

The relationship between Analytics Capability of an Organization (ACO) and both Supply Chain Disruption Orientation (SCDO) and Supply Chain Resilience (SCR) in order to achieve adequate operational performance in an era of environmental uncertainty is carried out in this study. Total three hypotheses (seven sub-hypotheses) using a survey of 405 respondents are collected via a pre-tested instrument and tested further. Results indicated the influence of ACO on both SCDO and SCR to achieve the desired degree of operational performance. However, under the moderation of environmental uncertainty, the link between ACO and SCDO was not supported, although the link between ACO and SCR was supported and this further enhanced operational performance. Further investigation of unsupported hypotheses using statistical analysis was conducted to gain deeper insights. It is explained how ACO impacted dynamic capabilities to influence operational performance. The contribution to theory of this study lies in explaining the role of dynamic capabilities that emerge from analytics as compared to the traditional view of supply chain classification. Further, the influence of environmental uncertainty on positioning dynamic capabilities strategically to address disruption in supply chains is discussed in the present study.

The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry.

This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers' needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory.

Role of robotics and artificial intelligence in oral health education. Knowledge, perception and attitude of dentists in India.

BACKGROUND: Artificial intelligence or AI may be identified as the use of Personal Computers and/or machines in performing certain specific types of tasks that usually have the requirement of humanized knowledge. These specific tasks include acknowledgment of the problem, understanding disease dynamics, and determining the clinical diagnosis. MATERIALS AND METHODS: This cross-sectional and prospective study was conducted on Dental professionals who were practicing all across India after obtaining approval from the Institutional Ethical Board. A previously validated as well as pre-analyzed questionnaire form was distributed using electronic mail and through the use of social media with a briefly explained description of the study purpose and an informed consent form. The study questionnaire comprised "close-ended" queries that were then divided into foursections. All the study participants were then instructed to select any one option among all the provided answers. The entire study was completed within one month. Collected observations were entered within a Microsoft Excel 2007® master chart. Statistical analytical software tool SPSS version 20.0, IBM Corporation was employed. "Chi-square" test was performed for evaluating statistical association. A P value lesser than 0.05 was fixed with statistical significance. RESULTS: On analyzing the level of knowledge, 82.5% of subjects had knowledge of artificial intelligence while 11.4% had no knowledge and 6.1% had some knowledge of this tool. 69.1% were knowledgeable regarding the use of AI in lesional diagnosis, 12.8% had no knowledge regarding artificial intelligence for the diagnosis and 18.1% had no knowledge regarding AI in the diagnosis. 71% had knowledge concerning the use of AI for Imaging. Knowledge of AI in Oral Hygiene was seen in 54.3%. 91.2% of participants had knowledge of robotics use in Oral Surgery. 77% of dentists had knowledge regarding the use of AI for the enhancement of clinical practice. 95.5% had a higher 'positive' attitude toward the use of AI in academics. 69.1% of dentists had a positive attitude regarding AI incorporation in practice. 5% of dentists considered artificial intelligence better than human intelligence for diagnosis. 10% believed that disparities can exist between AI-based and human diagnosis. CONCLUSION: Positive correlations were noted between knowledge, attitude, and practice of AI among studied dentists.

Big data applications with theoretical models and social media in financial management.

This study presents big data applications with quantitative theoretical models in financial management and investigates possible incorporation of social media factors into the models. Specifically, we examine three models, a revenue management model, an interest rate model with market sentiments, and a high-frequency trading equity market model, and consider possible extensions of those models to include social media. Since social media plays a substantial role in promoting products and services, engaging with customers, and sharing sentiments among market participants, it is important to include social media factors in the stochastic optimization models for financial management. Moreover, we compare the three models from a qualitative and quantitative point of view and provide managerial implications on how these models are synthetically used along with social media in financial management with a concrete case of a hotel REIT. The contribution of this research is that we investigate the possible incorporation of social media factors into the three models whose objectives are revenue management and debt and equity financing, essential areas in financial management, which helps to estimate the effect and the impact of social media quantitatively if internal data necessary for parameter estimation are available, and provide managerial implications for the synthetic use of the three models from a higher viewpoint. The numerical experiment along with the proposition indicates that the model can be used in the revenue management of hotels, and by improving the social media factor, the hotel can work on maximizing its sales.