Minimum Cost Research Articles

Day-Ahead Optimization of Proton Exchange Membrane Electrolyzer Operations Considering System Efficiency and Green Hydrogen Production Constraints Imposed by the European Regulatory Framework

Clean hydrogen (H2) use (i.e., produced using either renewable or low-carbon energy sources) can help decarbonize energy-intensive industries, the transport sector, and the power sector. The European regulatory framework establishes that the production of green H2 must be supported either by the electricity grid through a power purchase agreement (PPA) or by intermittent renewable energy source (RES) plants owned by the hydrogen producer. Although the issue of the optimization of hydrogen production costs has already been approached, constraints related to the current regulatory framework and the modeling of nonlinear electrolyzer efficiency still represent open problems. In this paper, a mixed-integer linear programming (MILP) problem, assuming as the objective function the overall cost minimization of the allowed energy mix for green H2 production, is formulated. Two approaches are compared: in the first one, electrolyzers can only operate at 100% load, whereas the second one allows for more flexible electrolyzer scheduling, by enabling partial-load working operations. The simulation results of several scenarios considering different H2 production targets, forecasted RES production, and cost for PPAs demonstrate the effectiveness of the proposed methodology.

A single and multiobjective robust optimization of a microgrid in distribution network considering uncertainty risk.

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters.

Teaching principles in Type 1 error and reproducible research using dice

AbstractControlling Type 1 error and encouraging reproducible research are important in clinical and translational research. These concepts are frequently discussed in lectures with mathematical language, analytic examples, and probability distributions that demonstrate the issues. However, first‐time learners in biostatistics courses focusing on fundamental topics may benefit from a more interactive demonstration in which learners participate, contribute to the results, and have a personal stake in the outcomes. This article summarizes a simple activity using dice to simulate Type 1 error that requires minimal preparation and cost. The activity may be appropriate for learners enrolled in a first course in statistics, biostatistics, or probability.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

Solution of Schrödinger Equation for Simple Diatomic Molecules Using One‐Parameter 1s Slater‐Type Orbitals Wave Function

ABSTRACTThe Schrödinger equation for simple homonuclear and heteronuclear diatomic molecules is analytically solved using one‐parameter Slater‐type orbitals (STOs) to approximate the electronic wave function within a molecular orbital (MO)‐like approach. The resulting total energies, equilibrium bond lengths, potential curves, and electron densities are presented in detail. Calculations using a selected orbital exponent accurately reproduce results from standard methods. Furthermore, the optimization of the orbital exponent allows for a more accurate representation of the electronic wave function, leading to the improved results of the total energy and the equilibrium bond length, as well as minimal computational cost. Seen in the heteronuclear diatomic molecule, the use of the one‐parameter STOs allows the transformation of the heteronuclear problem into the homonuclear one, revealing the electron repulsion effect on the orbital exponent parameter.

Cost minimization analysis of treatments for metastatic HER2-positive breast cancer in Peru: Fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injections.

The main objective of this study is to determine whether the employment of fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection (PH FDC SC; and Phesgo as brand name) to treat metastatic HER2-positive breast cancer patients would minimize costs compared to the traditional treatment of separate intravenous doses of pertuzumab and trastuzumab in Peru. To achieve this, we used EsSalud (the social security health insurance) data and assessed it through a mixed strategy, which consisted of a quantitative and a qualitative approach. The first one aimed to calculate the direct (non-drug consumables, drugs, and healthcare professionals) and indirect costs of both treatments to develop a comparison, whilst the second aimed to validate information and internalize the procedure in an EsSalud context. Overall, we found that the usage of PH FDC SC would be cost saving in EsSalud's context. Specifically, we found three main advantages. Firstly, PH FDC SC generates a savings of 62% in non-drug consumables, which helps alleviate the healthcare system budget constraint. Secondly, its adoption frees up 61 hours of treatment and observation time for a single patient per year, which in turn increases the attention capacity of the healthcare system in terms of nursing hours and chemotherapy couches. Thirdly, the reduction of clinic time supposes an advantage for the patient in the form of increased productivity and well-being. Hence, the adoption of this drug would improve the quality of life of patients while reducing costs and pressure on the healthcare system. This is aligned with the strategy of prioritizing the appropriate breast cancer treatment within the National Cancer Care Plan. In this regard, we also found that the savings produced from switching from the traditional intravenous treatment to the subcutaneous one would allow EsSalud to afford full annual costs of 2 additional treatments, but without increasing their budget. This would cover 7% of the gap of 29 patients who do not have access to full treatment.

Necrotizing Enterocolitis Detection in Premature Infants Using Broadband Optical Spectroscopy.

Necrotizing enterocolitis (NEC) is a devastating disease affecting premature infants. Broadband optical spectroscopy (BOS) is a method of noninvasive optical data collection from intra-abdominal organs in premature infants, offering potential for disease detection. Herein, a novel machine learning approach, iterative principal component analysis (iPCA), is developed to select optimal wavelengths from BOS data collected invivo from neonatal intensive care unit (NICU) patients for NEC classification. Neural network models were trained for classification, with a reduced-feature model distinguishing NEC with an accuracy of 88%, a sensitivity of 89%, and a specificity of 88%. While whole-spectrum models performed the best for accuracy and specificity, a reduced feature model excelled in sensitivity, with minimal cost to other metrics. This research supports the hypothesis that the analysis of human tissue via BOS may permit noninvasive disease detection. Furthermore, a medical device optimized with these models may potentially screen for NEC with as few as seven wavelengths.

Optimizing Connected Components Graph Partitioning With Minimum Size Constraints Using Integer Programming and Spectral Clustering Techniques

ABSTRACTIn this work, a graph partitioning problem in a fixed number of connected components is considered. Given an undirected graph with costs on the edges, the problem consists of partitioning the set of nodes into a fixed number of subsets with minimum size, where each subset induces a connected subgraph with minimal edge cost. The problem naturally surges in applications where connectivity is essential, such as cluster detection in social networks, political districting, sports team realignment, and energy distribution. Mixed Integer Programming formulations together with a variety of valid inequalities are demonstrated and computationally tested. An assisted column generation approach by spectral clustering is also proposed for this problem with additional valid inequalities. Finally, the methods are tested for several simulated instances, and computational results are discussed. Overall, the proposed column generation technique enhanced by spectral clustering offers a promising approach to solve clustering and partitioning problems.

Optimum Planning of Carbon Capture and Storage Network Using Goal Programming

Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goal programming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models’ results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic–environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the very important strategies of mitigating climate change and reducing global warming.

Charge Diffusion and Repulsion in Semiconductor Detectors

Semiconductor detectors for high-energy sensing (X/γ-rays) play a critical role in fields such as astronomy, particle physics, spectroscopy, medical imaging, and homeland security. The increasing need for precise detector characterization highlights the importance of developing advanced digital twins, which help optimize the design and performance of imaging systems. Current simulation frameworks primarily focus on modeling electron–hole pair dynamics within the semiconductor bulk after the photon absorption, leading to the current signals at the nearby electrodes. However, most simulations neglect charge diffusion and Coulomb repulsion, which spatially expand the charge cloud during propagation due to the high complexity they add to the physical models. Although these effects are relatively weak, their inclusion is essential for achieving a high-fidelity replication of real detector behavior. There are some existing methods that successfully incorporate these two phenomena with minimal computational cost, including those developed by Gatti in 1987 and by Benoit and Hamel in 2009. The present work evaluates these two approaches and proposes a novel Monte Carlo technique that offers higher accuracy in exchange for increased computational time. Our new method enables more realistic performance predictions while remaining within practical computational limits.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

When the Bough Breaks: The Financial Burden of Childbirth and Postpartum Care by Insurance Type.

Out-of-pocket (OOP) costs related to childbirth and postpartum care may cause financial hardship, depending on type of insurance and income. We estimated OOP spending on childbirth and postpartum care and financial strain 1 year after birth, comparing Medicaid-insured births with commercially insured births. The Postpartum Assessment of Health Survey followed up with respondents to the Centers for Disease Control and Prevention (CDC) Pregnancy Risk Assessment Monitoring System after a 2020 birth in six states and New York City. The survey included questions on health care costs and financial well-being. Our analytic sample consisted of 4,453 postpartum people, 1,544 with a Medicaid-insured birth and 2,909 with a commercially insured birth. We observe significant financial hardship from childbirth that persists into the postpartum year, with significant differences by insurance and income. We find Medicaid is highly financially protective relative to commercial insurance; 81.4% of Medicaid-insured births were free to the patient, compared with 15.7% of commercially insured births (p < 0.001). Six of ten commercially insured births (59%) cost over $1,000 OOP. Among respondents reporting OOP costs for childbirth, we found that Medicaid enrollees are more likely to have borrowed money from friends or family to pay for childbirth (8% vs. 1%, p < 0.001) and one in five had not made any payments 1 year postpartum (26% vs. 5% of commercially insured births, p < 0.001). Among the commercially insured, those with incomes under 200% of the federal poverty level (FPL) fared worse financially than those above 200% FPL on a number of indicators, including debt in collection (33% vs. 13%, p < 0.001) and financial worry (55% vs. 34%, p < 0.001). The cost of childbirth and postpartum health care results in significant and persistent financial hardship, particularly for families with lower income with commercial insurance. Medicaid offers greater protection for families with low income by offering reduced cost sharing for childbirth and postpartum health care, but even minimal cost sharing in Medicaid causes financial strain.

Modern-Day Green Strategies for the Removal of Chromium from Wastewater

Chromium is an essential element in various industrial processes, including stainless steel production, electroplating, metal finishing, leather tanning, photography, and textile manufacturing. However, it is also a well-documented contaminant of aquatic systems and agricultural land, posing significant economic and health challenges. The hexavalent form of chromium [Cr(VI)] is particularly toxic and carcinogenic, linked to severe health issues such as cancer, kidney disorders, liver failure, and environmental biomagnification. Due to the high risks associated with chromium contamination in potable water, researchers have focused on developing effective removal strategies. Among these strategies, biosorption has emerged as a promising, cost-effective, and energy-efficient method for eliminating toxic metals, especially chromium. This process utilizes agricultural waste, plants, algae, bacteria, fungi, and other biomass as adsorbents, demonstrating substantial potential for the remediation of heavy metals from contaminated environments at minimal cost. This review paper provides a comprehensive analysis of various strategies, materials, and mechanisms involved in the bioremediation of chromium, along with their commercial viability. It also highlights the advantages of biosorption over traditional chemical and physical methods, offering a thorough understanding of its applications and effectiveness.

STEWART PLATFORM MULTIDIMENSIONAL TRACKING CONTROL SYSTEM SYNTHESIS

Context. Creating guaranteed competitive motion control systems for complex multidimensional moving objects, including unstable ones, that operate under random controlled and uncontrolled disturbing factors, with minimal design costs, is one of the main requirements for achieving success in this class devices market. Additionally, to meet modern demands for the accuracy of motion control processes along a specified or programmed trajectory, it is essential to synthesize an optimal control system based on experimental data obtained under conditions closely approximating the real operating mode of the test object. Objective. The research presented in this article aims to synthesize an optimal tracking control system for the Stewart platform’s working surface motion, taking into account its multidimensional dynamic model. Method. The article employs a method of a multidimensional tracking control system structural transformation into an equivalent stabilization system for the motion of a multidimensional control object. It also utilizes an algorithm for synthesizing optimal stabilization systems for dynamic objects, whether stable or not, under stationary random external disturbances. The justified algorithm for synthesizing optimal stochastic stabilization systems is constructed using operations such as addition and multiplication of polynomial and fractional-rational matrices, Wiener factorization, Wiener separation of fractional-rational matrices, and the calculation of dispersion integrals. Results. As a result of the conducted research, the problem of defining the concept of analytical design for a Stewart platform’s optimal motion control system has been formalized. The results include the derived transformation equations from the tracking control system to the equivalent stabilization system of the Stewart platform’s working surface motion. Furthermore, the structure and parameters of the main controller transfer function matrix for of this control system have been determined. Conclusions. The justified use of the analytical design concept for the Stewart platform’s working surface optimal motion control system formalizes and significantly simplifies the solution to the problem of synthesizing complex dynamic systems, applying the developed technology presented in [1]. The obtained structure and parameters of the Stewart platform’s working surface motion control system main controller, which is divided into three components W1, W2, and W3, improve the tracking quality of the program signal vector, account for the cross-connections within the Stewart platform, and increase the accuracy of executing the specified trajectory by increasing the degrees of freedom in choosing the controller structure

Minimasi Biaya Distribusi pada UMKM Keset Samian dengan Metode VAM dan Modi

Transportation methods are methods used to optimally manage the distribution of goods from suppliers to destinations. The method used is Vogel's Approximate Method (VAM) and Modified Distribution (MODI). This research aims to minimize distribution costs for Keset Samian MSMEs. Samian Doormat UMKM is a small and medium business engaged in the production of doormat crafts. The Samian Doors Umkm experiences problems in managing distribution costs efficiently which has a direct impact on business profits. This research uses primary and secondary data collection related to costs. distribution, demand, and production capacity. The data was analyzed using VAM to determine the initial optimal cost solution in allocating the distribution of doormat products. The results of the VAM method were further optimized using the MODI method to achieve minimal costs in the distribution of doormat products. The research results show that the VAM and MODI methods are able to reduce distribution costs significantly compared to the traditional methods currently used by the Samian Doormat MSMEs. Where from the initial cost of Rp. 4,250,000 to Rp. 2,843,500 which experienced a decrease in transportation costs of Rp. 1,406,500 or 33.09%. So the results of calculating transportation costs for UMKM Keset Samian are optimal and increase business profits.

Vegetation restoration strategies in arid or semi-arid regions-From the perspective of optimal control.

Inappropriate human activities contribute to the degradation of ecosystems in arid or semi-arid regions. Therefore, emphasizing the importance of strategies for restoring vegetation in these areas cannot be overstated. However, there has been insufficient research on how to develop effective restoration strategies at minimal cost. This paper addresses this gap by studying how optimizing the spatiotemporal distribution of human activities through local and boundary controls can reduce the level of desertification in vegetation pattern structures, thereby facilitating the recovery of arid land vegetation. The results indicate that vegetation restoration depends on the proportion and number of human activity areas, with a trade-off between them. Furthermore, consistent conclusions were obtained on circular regions, demonstrating the robustness of the approach to boundary shapes. This paper aims to offer new insights into the restoration of arid land vegetation and the prevention of catastrophic ecosystem changes from the perspective of optimal control.

Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation

Deep learning networks excel in image segmentation with abundant accurately annotated training samples. However, in medical applications, acquiring large quantities of high-quality labeled images is prohibitively expensive. Thus, learning from imperfect annotations (e.g. noisy or weak annotations) has emerged as a prominent research area in medical image segmentation. This work aims to extract high-quality pseudo masks from imperfect annotations with the assistance of a small number of clean labels. Our core motivation is based on the understanding that different types of flawed imperfect annotations inherently exhibit unique noise patterns. Comparing clean annotations with corresponding imperfectly annotated labels can effectively identify potential noise patterns at minimal additional cost. To this end, we propose a two-phase framework including a noise identification network and a noise-robust segmentation network. The former network implicitly learns noise patterns and revises labels accordingly. It includes a three-branch network to identify different types of noises. The latter one further mitigates the negative influence of residual annotation noises based on parallel segmentation networks with different initializations and a label softening strategy. Extensive experimental results on two public datasets demonstrate that our method can effectively refine annotation flaws and achieve superior segmentation performance to the state-of-the-art methods.

The Impact of Leadership Training on Burnout and Fulfillment Among Direct Reports

SUMMARY Goal Burnout, decreased professional fulfillment, and resultant attrition across the medical professions are increasingly recognized as threats to sustainable and cost-effective healthcare delivery. While the skill level of leaders as perceived by their direct reports has been correlated with rates of burnout and fulfillment, no studies, to our knowledge, have directly evaluated whether intervention via leadership training impacts burnout and fulfillment among direct reports. The goal of this study was to evaluate the effectiveness of a leadership training intervention on direct reports’ perceptions of the leadership skills of supervising residents and subsequently on the well-being of the direct reports. Methods We implemented a leadership training program with supervising (i.e., chief) resident volunteers in two surgical residency programs. The leadership training included two sessions of approximately 2 hours each that consisted of interactive didactic and small group activities. The training focused on the following themes: defining leadership (i.e.,characteristics and behaviors), team building, fostering trust, managing conflict, navigating difficult conversations, and feedback. We administered pretraining and posttraining surveys to the direct reports (i.e., junior residents) to assess the perceived leadership skills of supervising residents, as well as burnout and professional fulfillment. Principal Findings Leadership scores significantly improved following the leadership training intervention. Additionally, improvement in leadership scores following training was positively correlated with professional fulfillment among the junior residents (direct reports). Practical Applications The results of this study suggest that incorporating leadership training into residency programs may serve as an appropriate initial intervention to improve the leadership skills of supervising residents, and in turn, improve professional fulfillment and retention among medical professionals. This intervention involved minimal cost and time investment, with potentially significant returns in combating the well-being and attrition crisis. These findings may be applicable across the healthcare field to tackle the impending healthcare worker crisis.

Early Detection of Wildlife Disease Pathogens Using CRISPR-Cas System Methods.

Wildlife diseases are a considerable threat to human health, conservation, and the economy. Surveillance is a critical component to mitigate the impact of animal diseases in these sectors. To monitor human diseases, CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) biosensors have proven instrumental as diagnostic tools capable of detecting unique DNA and RNA sequences related to their associated pathogens. However, despite the significant advances in the general development of CRISPR-Cas biosensors, their use to support wildlife disease management is lagging. In some cases, wildlife diseases of concern could be rapidly surveyed using these tools with minimal technical, operational, or cost requirements to end users. This review explores the potential to further leverage this technology to advance wildlife disease monitoring and highlights how concerted standardization of protocols can help to ensure data reliability.

Cost minimization analysis of chronic kidney disease management: Evaluating economic strategies for early intervention and treatment optimization

Chronic kidney disease is a common global health condition characterized by progressive loss of kidney function and significant cost burden on the healthcare system worldwide. The disease often coexists with conditions such as diabetes and hypertension, further complicates its management and increasing associated costs. Effective economic management, particularly in the early stages of CKD, is crucial for reducing long-term healthcare expenses and improving patient outcomes. This review examines the utility of cost minimization analysis (CMA) as a strategic tool for evaluating economic options in CKD management. CMA is an economic evaluation method that compares the costs of different therapeutic interventions that have demonstrated equivalent clinical effectiveness, thereby aiding healthcare providers and policymakers in identifying the most cost-efficient options to optimize resource utilization. This review highlights the economic impact of CKD, focusing on the increasing costs linked to advanced disease management and the essential role of early diagnosis and intervention in reducing these costs and explores various cost-effective strategies for CKD management, including pharmacological treatments, lifestyle modifications, and patient education, to identify approaches that are both clinically and economically advantageous. Additionally, the broader implications of cost minimization for healthcare policy, clinical decision-making, and resource allocation are discussed for their significance in a resource-limited healthcare setting. Evidence indicates that prioritizing cost-effective CKD management through early intervention and preventive care can significantly lower healthcare expenditures, slow the disease progression, and enhance patient’s quality of life. By adopting such strategies, healthcare systems can achieve better clinical outcomes and ensure financial sustainability.