Future Expansion Research Articles

Supervised toothbrushing programmes in England: a national survey of current provision and factors influencing their implementation.

Introduction Supervised toothbrushing programmes (STPs) in nurseries and schools are effective at reducing inequalities in caries when targeted to areas of dental disease. Recent changes to government education and health policy have increased interest in STPs in England. This study aimed to establish the current level of provision of STPs in England, describe changes over time, understand associations with predictor variables, and summarise key barriers and facilitators to their implementation.Methods A national survey was conducted at upper tier local authority level about the extent of a STP across England between December 2023 and April 2024. Quantitative data were analysed using descriptive statistics, with regression analyses examining associations with key predictor variables. Barriers and facilitators to implementation were analysed and summarised based on the Consolidated Framework for Implementation Research.Results Data were received from 152 local authorities. Around 60% implemented a STP. Nearly one-third of programmes were commissioned by local authorities (28.9%) and most adopted a targeted approach (77.8%). Statistical analyses demonstrated significant and positive associations between dental caries prevalence and the number of children in STPs. Barriers to implementation included: 1) funding; 2) capacity; 3) pressures at settings; 4) logistics; and 5) lack of engagement. Facilitators included: 1) partnerships and connections; 2) available resources; 3) oral health expertise; 4) external policy and incentives; 5) shared knowledge; and 6) engagement.Conclusions Provision of STPs in England has increased since 2022. Any future expansion of STPs should consider the barriers and facilitators identified to enable smooth implementation.

Solar parking lot capacity: an abundant dual-use alternative to meet demand for the renewable energy transition

Abstract Most utility-scale solar facilities (USSFs) in the United States (US) are planned or installed in grasslands, pastures, agriculture land, and timberland, with aggressive future expansion plans. Investor-owned electricity providers have lobbied aggressively to remove obstacles to approval of USSFs on agricultural lands and for limits on regulations that support distributed solar installation. Rural USSF expansion may have potentially significant local, state, and regional impacts on agricultural production and the economy, property values and affordability, and conservation of habitats, connectivity, and endangered species. The potential use of parking lots in urban and suburban areas for solar development to meet demands is poorly studied and parking lot solar has not gained much traction in the US. The objective of this research was to estimate whether solar PV systems installed in parking lots can meet electricity demands across multiple urban and suburban metropolitan areas within the US. Parking lot data across four municipalities with variation in latitude, solar radiation, and population density was obtained, along with modeled annual electricity demand by sector. Parking lot area and potential generating capacity was calculated for canopy and carport designs using standard and premium efficiency panels, and annual electricity production was estimated using the National Renewable Energy Laboratory’s PVWatts Calculator. Premium efficiency canopy designs can meet ~100% of demand in some large car dependent low to medium density municipalities. High density urban areas may receive marginal direct benefit from parking lot solar development within their boundaries. Dual-use of lands without any ecosystem or productive value can substantially contribute to renewable energy mixes to meet demands.

Predicting the impact of dynamic global urban expansion on urban soil organic carbon

Urban expansion has triggered significant changes in soil organic carbon (SOC), profoundly affecting the global carbon cycle. The accurate prediction of the global distribution of urban SOC and assessment of the impact of future urban expansion on SOC are essential for urban soil carbon management. By using data from 377 urban locations, this study estimated the global distribution of urban SOC and projected future SOC changes under two socioeconomic scenarios: SSP126 and SSP585. The results showed that compared with the urban SOC density in 2000 (64.75 Mg C ha− 1), the global urban SOC density by 2100 decreased by 1.19% and 4.72% under the SSP126 and SSP585 scenarios, respectively. However, urban SOC stocks increased by 66.13% and 178.75% by 2100 under SSP126 and SSP585, respectively, reflecting a “carbon transfer” from natural ecosystems to urban land use. This study provides critical insights into the dynamics of urban SOC and underscores the necessity to balance urban expansion with ecosystem protection to reduce carbon loss and stabilise the global carbon balance.

Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan

Amidst the increasingly escalating global concern regarding climate change, adopting a low-carbon approach has become crucial for charting the future developmental trajectory of urban areas. It also offers a novel angle for cities to avoid high-temperature risks. This paper estimates carbon emissions in Wuhan City from both direct and indirect aspects. Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. Additionally, the WRF (Weather Research and Forecasting Model)—UCM (Urban Canopy Model) model is used to investigate the thermal environmental impacts of varying urban development scenarios. This study uses a low-carbon perspective to explore how cities can develop scientifically sound urban strategies to meet climate change challenges and achieve sustainable development goals. The conclusions are as follows: (1) The net carbon emission for Wuhan in 2022 is estimated to be approximately 20.8353 million tonnes. Should the city maintain an average annual emission reduction rate of 10%, the carbon sink capacity of Wuhan would need to be enhanced by 382,200 tonnes by 2060. (2) In the absence of anthropogenic influence, there is a propensity for the urban construction zone of Wuhan to expand primarily towards the southeast and western sectors. (3) The Ecological Priority (EP) and Tight Growth (TG) scenarios are effective in alleviating the urban thermal environment, achieving a reduction of 0.88% and 2.48%, respectively, in the urban heat island index during afternoon hours. In contrast, the Natural Growth (NG) scenario results in a degradation of the urban thermal environment, with a significant increase of over 4% in the urban heat island index during the morning and evening periods. (4) An overabundance of urban green spaces and water bodies could exacerbate the urban heat island effect during the early morning and at night. The findings of this study enhance the comprehension of the climatic implications associated with various urban development paradigms and are instrumental in delineating future trajectories for low-carbon sustainable urban development models.

Protocol for the development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data.

Propofol is a widely used sedative-hypnotic agent for critically-ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimizing sedation strategies and preventing adverse outcomes. Machine learning (ML) models offer a promising approach for predicting individualized patient risks of propofol-associated hypertriglyceridemia. We propose the development of a ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will utilize retrospective data from four Mayo Clinic sites. Nested cross-validation (CV) will be employed, with a 10-fold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and LASSO-penalized logistic regression. Data preprocessing steps include missing data imputation, feature scaling, and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimization will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations (SHAP). The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested cross-validation will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23-007416). Robust external validation using a nested cross-validation (CV) framework will help assess the generalizability of models produced from the modeling pipeline across different hospital settings.A diverse set of machine learning (ML) algorithms and advanced hyperparameter tuning techniques will be employed to identify the most optimal model configuration.Integration of feature explainability will enhance the clinical applicability of the ML models by providing transparency in predictions, which can improve clinician trust and encourage adoption.Reliance on retrospective data may introduce biases due to inconsistent or erroneous data collection, and the computational intensity of the validation approach may limit replication and future model expansion in resource-constrained settings.

Critical Contingency Analysis: Enhancing Stability in Nigeria's 330kv Grid

This study presents a contingency ranking analysis for the Nigerian 330kV power system using ETAP software. The network model, consisting of 65 buses, 120 transmission lines, 16 generators, and 14 loads, was evaluated to identify critical contingencies affecting system performance. Results indicate that the outage of the M2S line poses the most significant threat to grid stability, while the SAPELE NIPP generator failure has negligible impact. Recommendations focus on enhancing system resilience through load management, network restructuring, and renewable energy integration. Limitations of the study include model simplification and software dependency, suggesting areas for future research expansion

The Impact of Pop Mart’s Hot Secondary Market on the Consumption Behavior of Mainstream Consumers and Brand Image

The blind box economy, as an emerging consumer phenomenon, capitalizes on the sense of surprise and anticipation driven by uncertainty. Pop Mart, a leading brand in this sector, has successfully attracted a vast consumer base through its unique design style and a wide range of co-branding strategies with various intellectual properties (IPs). The distinct sales mechanism of blind boxes has further fostered the development of a secondary trading market. This study focuses on the primary groups involved in Pop Mart’s secondary market by conducting surveys and interviews. Surveys were administered to a sample of buyers aged 16-30, aiming to capture their purchasing behaviors and motivations. In addition, interviews with senior trendsetters were conducted to explore the influence of the booming secondary market on consumer speculation and its impact. The analysis of the survey and interview data reveals that the consumers opt to buy “open box” items directly from the secondary market, the secondary market offers a form of speculation that differs from the primary market. This approach not only provides consumers with a wider range of choices but also offers the brand greater opportunities for consumer engagement and increased visibility. For trendsetters, their participation in the secondary market often act as brand ambassadors, enhancing brand value and extending product promotion within the secondary market. The operation of Pop Mart’s secondary market presents valuable insights for the future promotion and expansion of the brand. Moreover, it offers an effective reference model for other brands with similar business models.

Carbon footprinting of railway infrastructure: a standardized, consistent data collection method

Greenhouse gas (GHG) emissions from the transport industry, comprised almost entirely of CO2, account for around 23% of global emissions. Reducing these emissions is crucial, if countries are to meet their carbon reduction targets. Given that net zero carbon emissions are beginning to be enacted into various countries’ laws, transport infrastructure owners now have time bound targets to meet. The accurate measurement of carbon currently expended by transport infrastructure is the first, and most critical, step of infrastructure owners’ journey towards carbon reduction, as this Business as Usual carbon expenditure will form the basis for both carbon reduction measures and expansion of carbon measurement from limited embodied and construction scopes though to whole life cycle carbon assessment. This article provides an overview of a transferable method for calculating the carbon footprint of railway infrastructure assets, based on a novel carbon calculation data form. Use of this form will ensure a standardized and consistent approach in data collection methods, enabling the creation of carbon footprints for small scale case studies of individual railway earthworks (around 100 m linear length) with a specific focus on the construction life cycle stages. These stages form the basis for future expansions to the whole life cycle and therefore accuracy in carbon footprinting is vital. These figures can also be used for aggregation across multiple interventions, and again accurate data are required to ensure inaccuracies are not amplified.

Introducing and Validating the Multiphasic Evidential Decision-Making Matrix (MedMax) for Clinical Management in Patients with Intrahepatic Cholangiocarcinoma.

Background: Despite the significant advancements of liver surgery in the last few decades, the survival rate of patients with liver and pancreatic cancers has improved by only 10% in 30 years. Precision medicine offers a patient-centered approach, which, when combined with machine learning, could enhance decision making and treatment outcomes in surgical management of ihCC. This study aims to develop a decision support model to optimize treatment strategies for patients with ihCC, a prevalent primary liver cancer. Methods: The decision support model, named MedMax, was developed using three data sources: studies retrieved through a systematic literature review, expert opinions from HPB surgeons, and data from ihCC patients treated at Heidelberg University Hospital. Expert opinions were collected via surveys, with factors rated on a Likert scale, while patient data were used to validate the model's accuracy. Results: The model is structured into four decision-making phases, assessing diagnosis, treatment modality, surgical approach, and prognosis. Prospectively, 44 patients with ihCC were included for internal primary validation of the model. MedMax could predict the appropriate treatment considering the resectability of the lesions in 100% of patients. Also, MedMax could predict a decent surgical approach in 77% of the patients. The model proved effective in making decisions regarding surgery and patient management, demonstrating its potential as a clinical decision support tool. Conclusions: MedMax offers a transparent, personalized approach to decision making in HPB surgery, particularly for ihCC patients. Initial results show high accuracy in treatment selection, and the model's flexibility allows for future expansion to other liver tumors and HPB surgeries. Further validation with larger patient cohorts is required to enhance its clinical utility.

Leveraging Machine Learning, Cloud Computing, and Artificial Intelligence for Fraud Detection and Prevention in Insurance: A Scalable Approach to Data-Driven Insights

This paper aims to establish an understanding of how developments in technology have affected insurance fraud detection and control. This paper discusses the applicability of combining ML, cloud environment and AI to build flexible and effective fraud discovery systems. The existing strategies for fraud detection and prevention may have a weakness with the amount, variety and real-time nature of data. This paper proposes a detailed framework to improve the effectiveness of fraud detection with the help of ML algorithms for accurate prediction models, AI for decision automation support, and cloud computing for future expansion. It will be clear from the above results that enhanced detection accuracy, operations efficiency and compliance to set legal standards have been attained. This research work’s objective is to present recommendations for insurers interested in preventing fraud while keeping the antidote affordable and easily soluble in large volumes.

CONVO ZONE

The Chat Engine Project is an advanced software solution designed to enable real-time communication through an intuitive and dynamic chat interface. This platform caters to diverse communication needs by supporting both one- on-one and group chat functionalities. The Group Chat feature is a cornerstone of the project, fostering team collaboration by enabling multi-user conversations in an organized and intuitive environment. With features such as group creation, role assignment, and permission management, it provides a structured space for productive interactions. Additionally, the chat interface supports the exchange of media files, documents, and links, enhancing the overall collaboration experience. On the frontend, the application leverages React.js and a UI Kit to provide a responsive, user-friendly, and visually appealing interface. React.js ensures a smooth and dynamic user experience by utilizing efficient state management and a component-based architecture that promotes reusability and modularity. This allows developers to quickly implement and expand features while maintaining performance and responsiveness. The UI Kit complements React.js by offering a pre-built collection of modern and consistent UI components, significantly speeding up development while adhering to contemporary design principles. It ensures accessibility and seamless adaptability across various devices, including desktops, tablets, and mobile platforms. The backend of the Chat Engine is designed for scalability, performance, and security. It utilizes technologies such as WebSocket for real-time messaging, robust data storage mechanisms for persistent message history, and encryption services to secure communications. A load balancer ensures even distribution of traffic, enhancing reliability and fault tolerance, while modular architecture makes the system flexible for future expansions. The combination of real- time synchronization and a consistent user interface across devices ensures that users can stay engaged and updated regardless of their location. This platform is ideal for a wide range of use cases, including team collaboration, customer support, and community engagement. The Group Chat functionality is particularly suited for organizational use, enabling teams to coordinate effectively in a digital workspace. At the same time, the One-on-One Chat feature facilitates personalized interactions, such as customer service conversations. By combining real-time communication, robust backend infrastructure, and an elegant frontend interface, the Chat Engine delivers a comprehensive, scalable, and secure solution for modern communication needs. Its versatility and extensibility make it a reliable foundation for personal, professional, and organizational use cases. KEYWORDS: Chat Engine, Group Chat, Real-time Communication, Team Collaboration Tool.

Molecular Glues and Molecular Glue Degraders: Mechanisms, Design, and Therapeutic Applications

Molecular glues and molecular glue degraders appear to be a fast-growing class of therapeutic agents that selectively modulate protein-protein potential interactions and can enhance the selective degradation of proteins. These small molecules are providing the possibility to target proteins that previously thought to be very challenging or inaccessible that are involved in a host of diseases. The present review focuses on describing the basic concepts of molecular glues and the rationale behind the interactions and the general concept that drives their design. Special attention is paid to their uses in cancer therapy, neurodegenerative diseases, and infections therapy. Exploring new therapeutic targets and drugs based on the molecular glue degraders’ ability to recognize and bind E3 ubiquitin ligases and promote ubiquitination proteolysis of specific proteins. Furthermore, the review looks at some of the limitations and the difficulties in the creation of these agents, as well as that potential in the field of pharmacogenomics. Thus, future expansion of molecular glue with state-of-the-art technologies including artificial intelligence and CRISPR is expected to extend its therapeutic applicability; evidently a step-up for targeted therapies. In this review, it offers the state-of-art on molecular glue studies and future direction for such work.

Municipal solid waste management practices and sanitary landfill site selection using the AHP approach for emerging industrial zones

Selecting the right location for waste management is crucial for emerging and rapidly growing industrial towns. The intricate nature of waste management systems necessitates careful consideration of numerous options and evaluation criteria when selecting a suitable landfill site. This study, conducted in Arerti Town, Ethiopia, utilized the GIS and Analytical Hierarchy Process (AHP) to establish a hierarchical model for identifying a suitable landfill location by considering the ten main influencing factors (groundwater table depth, proximity to boreholes, slope, soil, geology, land use, land cover, proximity to reservoirs and proximity to lineaments, built-up area, and road availability). Besides, GIS were used to manipulate and display spatial data, with all maps graded on a scale of 1 to 5, representing the lowest to highest suitability, respectively. The combined results of the AHP and GIS analyses indicated that 20.6% of the study area was deemed unsuitable, 77.67% moderately suitable and 1.75% highly suitable for a landfill. We chose a 12.5-hectare site in the southwest as the optimal location, taking into account future town expansion and its proximity to urban areas and roads. The findings of this study serve as a valuable resource for decision-makers in assessing waste management challenges in emerging towns and rapidly developing urban areas.

Virtual Nursing Initiatives in an Urban Hospital System with a Flagship Academic Hospital and Seven Satellite Hospitals: A Discussion of Successful Interventions and Future Directions

The implementation of virtual nursing enables remote support, reducing the workload of bedside nurses and enhancing patient care. Its use expanded in response to workforce shortages during the COVID-19 pandemic and has since gained approval from both bedside staff and patients, while also helping to mitigate staff burnout. Virtual nursing interventions have been shown to improve patient safety and the quality of care. At our institution, virtual nursing is facilitated through an artificial intelligence-assisted care platform, delivered via mobile carts equipped with tablets or two-way wall-mounted cameras with audio and visual capabilities. This discussion will highlight virtual nursing initiatives across our academic flagship hospital and seven satellite hospitals, which commenced in June 2022, as well as our vision and goals for future expansion.

A Brief History of Cell Culture: From Harrison to Organs-on-a-Chip.

This comprehensive overview of the historical milestones in cell culture underscores key breakthroughs that have shaped the field over time. It begins with Wilhelm Roux's seminal experiments in the 1880s, followed by the pioneering efforts of Ross Granville Harrison, who initiated groundbreaking experiments that fundamentally shaped the landscape of cell culture in the early 20th century. Carrel's influential contributions, notably the immortalization of chicken heart cells, have marked a significant advancement in cell culture techniques. Subsequently, Johannes Holtfreter, Aron Moscona, and Joseph Leighton introduced methodological innovations in three-dimensional (3D) cell culture, initiated by Alexis Carrel, laying the groundwork for future consolidation and expansion of the use of 3D cell culture in different areas of biomedical sciences. The advent of induced pluripotent stem cells by Takahashi and Yamanaka in 2006 was revolutionary, enabling the reprogramming of differentiated cells into a pluripotent state. Since then, recent innovations have included spheroids, organoids, and organ-on-a-chip technologies, aiming to mimic the structure and function of tissues and organs in vitro, pushing the boundaries of biological modeling and disease understanding. In this review, we overview the history of cell culture shedding light on the main discoveries, pitfalls and hurdles that were overcome during the transition from 2D to 3D cell culture techniques. Finally, we discussed the future directions for cell culture research that may accelerate the development of more effective and personalized treatments.

Assessment of cardiovascular diagnoses associated with telemedicine during and after the COVID-19 pandemic.

The COVID-19 pandemic led to widespread adoption of telemedicine, which has persisted in healthcare delivery. We aimed to characterize telemedicine use in ambulatory cardiology clinics over two years following the onset of the COVID-19 pandemic. Retrospective cross-sectional study from 16 March 2020 to 27 June 2022 in a single-center ambulatory cardiology clinic and telemedicine visits. Mixed effects logistic regression was used to model the association of diagnosis class (based on International Classification of Disease 10th Revision codes) with whether an encounter was scheduled as telemedicine, adjusting for age, sex, race, ethnicity, date, and zip code. This was performed for telemedicine and in-office encounters across 15 University of California Los Angeles (UCLA) Health System ambulatory cardiology clinics. The analysis included 76,127 patients (49.60% women, age 61.5 ± 17.30 years, 57.27% white, 12.25% Hispanic, 81.79% with zip code in a UCLA Health service area) over 255,674 encounters. Each patient had a median of two encounters (range 1-81). Of all encounters, 29,154 (11.40%) were scheduled as telemedicine. Telemedicine was more likely used in the management of chronic conditions, especially metabolic disorders (adjusted OR [aOR] 2.36, 95% CI 2.19-2.54) and cardiomyopathies (aOR 2.16, 95% CI 1.99-2.34), than for evaluation of undifferentiated signs/symptoms. Telemedicine was less likely used for general exam/screening (aOR 0.49, 95% CI 0.44-0.56) and heart transplant (aOR 0.51, 95% CI 0.40-0.64). Among the outpatient encounters in this study, the most established use case for telemedicine in cardiology was for care of chronic cardiovascular conditions among nontransplant patients, suggesting that future telemedicine expansion should be targeted toward the most appropriate clinical scenarios.

Hidden Giant of Embedded Plastics in China.

Combating plastic pollution requires an understanding of the complex intersectoral flows of plastics and related products. In this study, we develop an inventory of 150 categories of products consisting of/containing plastics based on bottom-up analysis and establish a plastics input-output material flow analysis (PIO-MFA) model that reveals intersectoral flows, sectoral consumption, exports, and stocks of embedded plastics. In 2018, consumption-based plastic usage in China reached 140 million tons (Mt), with 123.5 Mt being embedded plastics. Approximately 75.5% of embedded plastics were consumed domestically, and 21.3% were exported. Among the plastics exported, embedded plastics accounted for 68.5%, indicating the importance of the implicit transfer of embedded plastics through global trade. Furthermore, based on ambitious recycling targets, the amount of recycled plastics in China is expected to increase by 27.8 Mt. Plastics embedded in containers and packaging have the highest reduction potential, while joint efforts from the entire supply chain are crucial to reduce the amount of such plastics as industrial sectors consume 32.4% of embedded plastics. Identifying high plastic consumption sectors is critical for balancing economic and social goals with pollution control and ensuring comprehensive plastic treaty compliance. The PIO-MFA method's foreseeable future expansion to a global level is expected to inform plastic treaty obligations and support the development and implementation of national action plans.

Estimating urban growth on Mersin, Tarsus and Adana corridor in Türki̇ye by using Cellular Automata and Markov Chain

In many developing countries, urban growth is often unplanned and haphazard, leading to significant threats to the sustainable use of urban land. This study predicts future urban expansion patterns in the Mersin, Tarsus and Adana corridor in Türkiye using Cellular Automata and Markov Chain models. Analysing Landsat satellite images from 1989 to 2019, we simulated land use changes for 2019, 2031, and 2049. The results indicate a projected increase in urban areas from 3.8% in 2019 to 7.4% by 2049, with significant expansion onto fertile agricultural areas. This unregulated growth highlights the urgent need for a well-planned governance approach that balances economic, social, and environmental factors to ensure resilient and sustainable urban development.

Amplified threat of tropical cyclones to US offshore wind energy in a changing climate

The vulnerability of US offshore wind energy to tropical cyclones is a pressing concern, particularly along the Atlantic and Gulf Coasts, key areas for offshore wind energy development. Assessing the impact of projected climate change on tropical cyclones, and consequently on offshore wind resources, is thus critical for effective risk management. Herein, we investigate the evolving risk to offshore wind turbines posed by Atlantic tropical cyclones in a non-stationary climate using a synthetic tropical cyclone model. Integrated with climate model simulations, projections show widespread increases in tropical cyclone exposure, with historical 20-year storms occurring every ~12.7 years on average, increasing in intensity by about 9.3 ms−1. Subsequent fragility analysis reveals that the probabilities of turbine yielding and buckling from a 20-year tropical cyclone could increase by about 37% and 13%, respectively, with regional increases reaching up to 51%. These findings carry substantial implications for the operation and future expansion of offshore wind farms.

Optimalisasi Laba Warung Depot Ijo Kamal Melalui Analisis CVP dan Strategi Pendanaan

This study aims to analyze the application of Cost-Volume-Profit (CVP) analysis and funding strategies for profit optimization at Warung Depot Ijo Kamal, a small business in the food industry. The research explores how financial management, including cost control and strategic funding, contributes to the business’s sustainability and growth. A descriptive qualitative approach was used, involving semi-structured interviews with the business owner and direct observations of daily operations. Data from financial reports were also collected for analysis. The findings revealed that Warung Depot Ijo Kamal achieves profitability above the break-even point, with a monthly profit of IDR 9,170,000. The analysis of funding sources indicated that the business relies on personal capital and cooperative loans, but further diversification of funding sources is recommended to support future expansion. The study also identified challenges in cash flow management, particularly in meeting employee salary obligations. Recommendations include improving cash flow planning, diversifying funding sources, and exploring external financing options to support business growth. This research provides valuable insights for small business owners seeking to optimize profits and ensure long-term sustainability.