Optimal Resource Allocation Research Articles

Development of Semi-Mountainous and Mountainous Areas: Design of Trail Paths, Optimal Spatial Distribution of Trail Facilities, and Trail Ranking via MCDM-VIKOR Method

This study proposes a sustainable ecotourism framework for the development of semi-mountain and mountain regions of Paiko in Greece, focusing on the strategic design and ranking of trail paths using the multi-criteria decision-making (MCDM) VIKOR method. Aiming to balance environmental conservation with economic benefits, we designed 19 trails paths and allocated signage for resting and recreation facilities. The trail paths were assessed based on criteria such as length, difficulty, scenic appeal, and accessibility. This approach identified key trails that combine scenic beauty with infrastructure suitable for a broad range of visitors, thereby enhancing sustainable tourism appeal. Stakeholder engagement was integral to shaping the trail network, ensuring that the selected paths reflect local values and priorities. This study highlights how the VIKOR method can optimize resource allocation by ranking trails according to their environmental and visitor-centered attributes, supporting regional economic growth through ecotourism. This framework offers a replicable model for other mountainous regions seeking to harness ecotourism’s potential while preserving natural ecosystems. The findings demonstrate the capacity of well-planned trail networks to attract nature-based tourism, stimulate local economies, and respond to the rising post-pandemic interest in outdoor recreation, while promoting long-term conservation efforts. This approach offers a replicable model for the sustainable development of mountainous and semi-mountainous areas in Greece and beyond.

Duration of In-patient Physical Therapy at a Tertiary Hospital: Implications of a Time and Motion Study

Background and Objectives. Inpatient physical therapy (PT) care entails careful provision of service for individuals across a spectrum of disorders needing evidence-based physical rehabilitation during their hospital admission. The main difficulty is identifying adequate time allocation for safe patient service within an eight-hour working time frame. At a selected tertiary teaching government-run hospital, an arbitrary personnel-population ratio method is used for human resource allocation which may lead to issues in service delivery and healthcare workforce wellness. Apart from patient care, physical therapists also assume non-clinical roles revolving on administration, clinical education, and research. This highlights the importance of identifying the duration of tasks to assess work efficiency and manage time constraints posed by the limitations of the work shift. Practice guidelines may help improve staff workload scheduling, however, there is a lack of available guidelines regarding physical therapy practice in the hospital or acute care setting for developing countries. The purpose of this study was to determine the duration of care for inpatients with musculoskeletal and neurological conditions and administrative tasks, and to determine the relationship of duration of care with months of experience. Methods. A continuous observation time motion study of a typical workday of all physical therapists in an in-patient setting of a tertiary teaching hospital was conducted. An external observer will time the tasks done by the PT. The external observer kept distance from the direct patient encounter to minimize interference that may affect timing. Patients with conditions of different etiologies and functional levels were included. Results. Nineteen physical therapists with a mean experience of 54.63 months were observed. There were no adverse events during the implementation of this study. The total mean time in minutes of management of neurological patients is 37:32, and musculoskeletal patients is 28:30. Time for administrative tasks took an average of 20:33 minutes. There was also a low positive correlation on months of experience and treatment duration for patients with neurologic conditions (p=0.0471). Results showed that the allocated treatment duration is determined to be sufficient for performing PT activities. The optimal number of patients to be decked can also be appropriately determined to optimize resource allocation. Conclusion. Our pilot study attempted to quantify the duration of PT care in an inpatient setting that caters to patients with different diagnosis and varying needs for PT management. Although descriptive statistics and a weak to insignificant correlation was seen in most of the variables, there may be some benefit in gathering more duration data across different acute care settings within the country. Our pilot time and motion study can contribute to the limited evidence of duration of inpatient care that may inform human resource allocation, deliverables prioritization, and employee wellness and development. Figures have been presented which could be the basis for future policy research for management science and resource allocation studies.

Research on the path of technological innovation and resource allocation optimization of state-owned enterprises from the perspective of ecological environment and biomechanics

In the evolving global landscape, competition among nations has increasingly centered on the allocation of innovation resources, which are crucial for enterprises to implement development strategies under supply-side reforms. The efficiency and rationality of resource allocation directly affect the innovation capacity and operational performance of enterprises. This paper examines the relationship between technological innovation and resource allocation in state-owned enterprises through the lens of the ecological environment, employing data from multiple sources and utilizing the Data Envelopment Analysis (DEA) model to assess resource allocation efficiency. The study reveals that the proportion of R&D personnel invested in enterprises in China increased significantly from 46.35% to 73.38%, while the proportion in research institutions and universities declined to 11.48% and 11.36%, respectively. These shifts underscore the growing dominance of state-owned enterprises in driving technological innovation. The findings highlight that aligning technological innovation mechanisms with ecological sustainability not only enhances innovation capabilities and competitiveness but also accelerates industrial development and contributes to the sustainable growth of the national economy.

High-precision prediction of microalgae biofuel production efficiency: employing ELG ensemble method

Microalgae biofuels are considered a significant source of future renewable energy due to their efficient photosynthesis and rapid growth rates. However, practical applications face numerous challenges such as variations in environmental conditions, high cultivation costs, and energy losses during production. In this study, we propose an ensemble model called ELG, integrating Empirical Mode Decomposition (EMD), Long Short-Term Memory (LSTM), and Gradient Boosting Machine (GBM), to enhance prediction accuracy. The model is tested on two primary datasets: the EIA (U.S. Energy Information Administration) dataset and the NREL (National Renewable Energy Laboratory) dataset, both of which provide extensive data on biofuel production and environmental conditions. Experimental results demonstrate the superior performance of the ELG model, achieving an RMSE of 0.089 and MAPE of 2.02% on the EIA dataset, and an RMSE of 0.1 and MAPE of 2.21% on the NREL dataset. These metrics indicate that the ELG model outperforms existing models in predicting the efficiency of microalgae biofuel production. The integration of EMD for preprocessing, LSTM for capturing temporal dependencies, and GBM for optimizing prediction outputs significantly improves the model’s predictive accuracy and robustness. This research, through high-precision prediction of microalgae biofuel production efficiency, optimizes resource allocation and enhances economic feasibility. It advances technological capabilities and scientific understanding in the field of microalgae biofuels and provides a robust framework for other renewable energy applications.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Disaster Preparedness and capacity building for Resilience in Agriculture

Abstract. Agriculture faces unprecedented challenges due to the increasing frequency and severity of natural disasters and extreme weather events. Agriculture sector needs resilience to support food production and livelihoods because it is susceptible to cyclones, floods, droughts, soil erosion, pests, and disease outbreaks. Geospatial technology plays a significant role in disaster management for agriculture, offering tools for preparedness, response, and recovery. This study explores the role of geospatial technology in early warning systems and risk assessment for different types of natural disasters that impact agriculture activity. Sustainable farming practices that are essential for resilience include crop diversity, climate-resilience varieties, and adaptation strategies. Capacity building and training are vital for effective geospatial technology utilization, especially in developing countries like India, where infrastructure and technology access may be limited. Tailored capacity-building programs are essential, emphasizing climate-smart practices, sustainable land management, and post-disaster recovery strategies. Access to financial services and insurance schemes enhances resilience by helping farmers cope with losses and recover from disasters. In order to improve resilience and sustainability in agriculture, geospatial decision support systems make it possible to evaluate alternative planning, optimise resource allocation, and implement adaptive management.

Surrogate Modeling for Solving OPF: A Review

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies.

A Scientific Approach for Optimizing Patient Bed Occupancy in an Overcrowded Teaching Hospital in Sri Lanka

Background: In Sri Lanka, the healthcare system predominantly relies on the public sector, which delivers nearly 95% of inpatient care and approximately 50% of outpatient care. Effective monitoring of bed occupancy rates is crucial for optimizing resource allocation amidst limited hospital resources and fluctuating patient demands.Objectives: This study aims to develop a scientific approach to enhance patient bed utilization at Colombo South Teaching Hospital (CSTH) in Sri Lanka.Methodology: A mixed-method approach was employed, integrating quantitative analysis of historical patient data from CSTH's medical record room with qualitative insights from key stakeholders, including hospital administrators, consultants, medical officers, and nursing staff.Results and Discussion: In 2023, CSTH reported an overall bed occupancy rate of 80%, surpassing both national and global averages, indicating strain on healthcare delivery. The study identifies disparities in occupancy rates across different wards, highlighting overcrowding in medical and surgical units alongside underutilization in other specialities.Recommendations and Conclusions: Revising the consultant-based ward allocation system, implementing patient-centric ward clustering based on occupancy rates, appointing bed managers, and expanding facilities in high-demand units like Medical, Accident Service Unit, Emergency Treatment Unit, and Nephrology. Implementing these strategies could mitigate overcrowding, improve bed availability, and enhance the quality and safety of patient care services at CSTH.

Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints

The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness of traffic event detection. To overcome these challenges, this paper introduces a heuristic optimal scheduling approach designed to enhance traffic event detection efficiency while operating within limited computational resources. This method leverages historical data and prior knowledge to compute a weighted event feature value for each camera, providing a quantitative measure of its detection efficiency. To optimize resource allocation, a cyclic elimination mechanism is implemented to exclude low-performing cameras, enabling the dynamic reallocation of resources to higher-performing cameras, thereby enhancing overall detection performance. Finally, the effectiveness of the proposed method is validated through a case study conducted in a representative region of a major metropolitan city in China. The results revealed a substantial improvement in traffic event detection efficiency, with increases of 40%, 28%, 17%, and 28% across different time periods when compared to the pre-optimized state. Furthermore, the proposed method outperformed existing resource scheduling algorithms in terms of average load degree, load balance degree, and higher computational resource utilization. By avoiding the common issues of resource wastage and insufficiency often found in static allocation models, this approach offers greater flexibility and adaptability in computational resource scheduling, thereby effectively addressing the practical demands of traffic anomaly detection and early warning systems.

Enhancing data management and real‐time decision making with IoT, cloud, and fog computing

AbstractThe convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques.

Analyzing Passenger Flows in an Airport Terminal: A Discrete Simulation Model

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario.

Challenges and Opportunities

In this article, the authors consider the future possibility of public administration in South Africa being transformed by artificial intelligence (AI). It discusses the difficulties and advantages of AI/ML in this set up. They can use artificial intelligence (AI) to enhance the productivity of the public sector, quality in decision, and contribute to people's participation. The results show that AI can reduce paperwork, anticipate public health trends, make recommendations for people to get the best chance of recovery, and even optimise resource allocation – all with a view towards improving service delivery and governance. Problems with infrastructure, gaps in digital access, as well as scarcity of AI expertise within the public service work against any real integration of artificial intelligence. The study underscores the significance of strategic planning, large-scale infrastructural investments, and robust policy reforms in order to harness the full range of benefits from artificial intelligence (AI) with a fair share accruing to all.

The intersection of financial modeling and public health: A conceptual exploration of cost-effective healthcare delivery

This review paper explores the intersection of financial modeling and public health, highlighting the importance of cost-effective healthcare delivery in contemporary health systems. Financial modeling serves as a crucial tool for policymakers, providing evidence-based insights that inform decision-making and optimize resource allocation. Key financial models, including cost-benefit analysis and predictive modeling, are examined for their applications in public health initiatives, particularly in areas such as preventive care and chronic disease management. The paper discusses core public health priorities—prevention, access, and quality—and the economic constraints faced by healthcare systems. Recommendations are provided for policymakers and healthcare professionals to enhance the application of financial models, emphasizing the need for training, investment in data infrastructure, and fostering cross-sector collaborations. By leveraging financial modeling, public health systems can achieve improved health outcomes while maximizing resource efficiency, ultimately contributing to a more sustainable and equitable healthcare landscape.. Keywords: Financial Modeling, Public Health, Cost-Effectiveness, Healthcare Delivery, Resource Allocation, Preventive Care.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

3D Inversion and Interpretation of Airborne Multiphysics Data for Targeting Porphyry System, Flammefjeld, Greenland

The exploration of porphyry deposits in Greenland has become increasingly important due to their significant economic potential. We utilized total magnetic intensity (TMI) and mobile magnetotelluric (MobileMT) airborne data to delineate potential porphyry mineralization zones. The TMI method was employed to map variations in the Earth’s magnetic field caused by subsurface geological features, including mineral deposits. By analyzing anomalies in TMI data, potential porphyry targets were identified based on characteristic magnetic signatures associated with mineralized zones. Complementing TMI data, MT airborne surveys provided valuable insights into the electrical conductivity structure of the subsurface. Porphyry deposits exhibited distinct conductivity signatures due to the presence of disseminated sulfide minerals, aiding in their identification and delineation. Integration of the TMI and MobileMT datasets allowed for a comprehensive assessment of porphyry exploration targets in Flammefjeld. The combined approach facilitates the identification of prospective areas with enhanced geological potential, optimizing resource allocation and exploration efforts. Overall, this study demonstrates the efficacy of integrating TMI and MobileMT airborne data for porphyry exploration in Greenland, offering valuable insights for mineral exploration and resource development in the region.

Spatiotemporal Changes and Utilization Intensity of the Zhoushan Archipelago Coastline over Four Decades

Coastal changes in China, notably in the Zhoushan Islands, have primarily been driven by coastal reclamation since the establishment of New China. This study conducted a comprehensive analysis of the Zhoushan Archipelago shoreline spanning four decades, employing remote sensing, aerial photographs, and shoreline data since 1984, along with GIS (Geographic Information System) technology. We assessed shoreline changes using the shoreline change index and shoreline artificialization index, as well as examined the influence of the Yangtze River’s suspended sediment and impoldering activities on Zhoushan’s shoreline. Furthermore, the correlation between local economic development and shoreline development was explored. The results revealed the following key findings: (1) From 1984 to 2018, the Zhoushan Archipelago shoreline decreased by 7.05 km. Temporally, the shoreline change index was −0.08%, with the most significant reduction occurring between 2008 and 2018. Spatially, differences among island groups were not pronounced. (2) The shoreline diversity index consistently increased, indicating greater diversity and complexity in shoreline use over the four decades. (3) The shoreline artificiality index steadily rose, particularly after 2000. It was highest in the south, followed by the center, and lowest in the north. (4) The intensity index of coastal land use continuously increased, with the southern island group having a higher index compared to the Zhoushan Islands. (5) The Yangtze River contributed significantly to sand inflow, influencing shoreline changes and beach shaping in Zhejiang. However, reclamation projects were identified as the primary and direct factor. (6) A positive correlation existed between Zhoushan City’s economic development and the intensity of coastal land use. This study emphasized the need for improving the control over reclamation projects and the better management of coastal protection and use. These measures could optimize resource allocation and establish a more scientific and rational coastal zone pattern.

Forecasts of Video Game Sales based on the ARIMA Model

Abstract. With the rapid development of the digital age, video games have become an important component of the cultural industry, and their sales forecasting has become a focus of industry attention. This article aims to predict the sales of video games based on the ARIMA model and explore effective methods to improve prediction accuracy. Through in-depth research, this article found that the ARIMA model performs well in capturing the time series characteristics of sales data and can accurately predict future sales trends. The ARIMA model provides a systematic and scientific method for predicting the future market performance of video games by comprehensively considering the trends, seasonality, and random changes in historical sales data. This research achievement not only provides strong decision support for game developers, publishers, and platform operators but also promotes the optimization of resource allocation and market risk avoidance in the video games industry, which is of great significance for promoting the healthy and sustainable development of the industry.

Predicting Ride-Hailing Demand with Consideration of Social Equity: A Case Study of Chengdu

In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, Mean Percentage Error indicators are employed in the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 12.9%, respectively, compared to base models. Additionally, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality, supporting sustainable transport practices. The proposed framework effectively captures the demands of diverse social groups, thereby contributing to the advancement of social equity and long-term sustainability in urban mobility.

EFFICIENCY OF URBAN PASSENGER TRANSPORT BY BUS: AN APPLICATION TO THE CASE OF CUENCA-ECUADOR

Objective: The study aims to analyze the technical and scale efficiency of public transportation in Cuenca, Ecuador, using Data Envelopment Analysis (DEA). It evaluates both supply and demand aspects. Theoretical Framework: DEA measures the efficiency of decision-making units (DMUs) in using inputs to produce outputs. It's widely used to assess public transportation efficiency by comparing resource usage and service outcomes. Method: The study analyzes 37 DMUs, considering kilometers traveled, number of frequencies, and total fleet as inputs, and operating costs and passenger demand as outputs. Results and Discussion: DEA_Demand showed a 90% technical efficiency and 45% scale efficiency. DEA_Offer showed 94% and 96% efficiency, respectively, highlighting the potential for adjustments in both approaches. Research Implications: The findings help policymakers optimize resource allocation and improve public transport services. Originality/Value: The study uniquely combines supply and demand analysis using DEA in a mid-sized city like Cuenca, offering actionable insights.

Advancements in Organoid Culture Technologies: Current Trends and Innovations.

Organoids have emerged as valuable tools in investigating disease mechanisms, drug efficacy, and personalized medicine due to their capacity to recapitulate crucial aspects of tissue physiology, including cell-cell interactions, heterogeneity, microenvironmental cues, and drug responses. Despite their broad applicability across various research domains, conventional organoid culture methods are plagued by several limitations that hinder research progress. These limitations include the inability to faithfully recreate tissue microenvironments, immune contexts, and vascular systems. Fortunately, ongoing advancements in organoid culture techniques are addressing these shortcomings. In this review, we provide a comprehensive overview of current mainstream organoid culture protocols. By evaluating these protocols, researchers can identify the most suitable experimental methods, thereby optimizing resource allocation and experimental outcomes.