Adaptive System Research Articles

Transforming Tax Compliance with Machine Learning: Reducing Fraud and Enhancing Revenue Collection

The integration of machine learning (ML) in tax administration has the potential to revolutionize tax compliance, enhancing fraud detection and optimizing revenue collection. This literature review explores the application of ML in tax systems, emphasizing its transformative role in addressing the limitations of traditional, labor-intensive compliance methods. Justification for adopting a literature review approach is rooted in the need to consolidate diverse perspectives, address research gaps, and provide an informed synthesis of existing findings. The study highlights the criteria used for selecting case studies and research papers, ensuring a robust analysis of ML’s ability to automate detection processes, improve risk assessment, and enable predictive analytics for efficient tax administration. Despite its potential, ML adoption is challenged by data quality issues, privacy concerns, technical infrastructure demands, and ethical considerations, which must be systematically addressed. This paper also identifies literature gaps, particularly the lack of balanced discourse, and provides recommendations for overcoming barriers, including enhancing data management practices, adopting ethical frameworks, and fostering cross-border collaboration. By addressing these challenges, ML can equip tax authorities with tools for creating efficient, adaptive, and fair systems. This research underscores ML’s growing importance in transforming global tax compliance, setting the stage for a future of more responsive and effective revenue administration.

Reservoir outflow prediction using adaptive neuro-fuzzy interference system

Reservoir outflow prediction using adaptive neuro-fuzzy interference system

Abstract A031: ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer

Abstract Cancer progression poses a clinical hurdle due to tumor heterogeneity that contributes to treatment resistance and immune evasion. While the adaptive immune system can effectively eliminate cancer in certain instances, tumor escape remains challenging. The immune microenvironment plays an important role in this process. One unanswered question is the role of ECM geometry, also known as Tumor Associated Collagen Signatures (TACS), and its effects on tumor immune recognition. In solid malignancies, ECM geometry has been associated with disease stage and T cell infiltration. Specifically, empirically observed ECM topologies are frequently categorized based on fiber arrangement: random fibers (TACS1), circumferentially aligned fibers (TACS2), and radially arranged fibers (TACS3). Even though a clear negative correlation between TACS and patient survival has been established, the specific roles and extent of TACS in T cell-driven cancer evolution remain uncharacterized. The precise mechanisms underlying how TACS influences cell movement are still not fully elucidated, with divergent opinions on whether and how the ECM mediates immune cell infiltration. At present, we currently lack a physical model relating the impact of TACS on the spatial co-evolution between an adaptive immune repertoire and a heterogeneous population of evading cancer cells. Here, we developed the EVO-ACT (EVOlutionary Agent-based Cancer T cell interaction) model, which is based on the Gillespie algorithm, to study the effects of TACS on tumor evolution and dynamical tumor-T cell interactions. Our analysis of tumor-T cell interactions across three TACS types reveals distinct migration patterns, with TACS3 showing the highest efficiency, TACS1 intermediate, and TACS2 the lowest. TACS impacts T cells more than tumors, with stronger chemokine gradients aiding T cell infiltration. Our model suggests that despite TACS2's limited efficiency, it cannot fully impede T cell infiltration. Varied migration efficiencies lead to diverse T cell infiltration patterns and immunoediting levels. TACS3 shows a benefit to immune recognition and higher survival, contrary to lower survival in TACS3 patients observed clinically in breast cancer. Our results indicate that only after introducing phenotypic changes, such as Epithelial-Mesenchymal Transition (EMT), and noting that EMT occurs exclusively after the TACS2-TACS3 progression, can our findings successfully explain the clinically observed trends. Considering mesenchymal tumor cells' ability to upregulate PD-1/PD-L1, we explore TACS-specific tumor evolution's impact on PD-1/PD-L1 inhibitor responses and patient survival. We observe higher tumor escape rates and lower survival in TACS3 compared to TACS2. Thus, TACS3 alone does not account for lower survival in breast cancer; TACS3-associated late-stage cancer byproducts like EMT and elevated checkpoint expression significantly decrease survival. Citation Format: Yijia Fan, Jason Tomas George. ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A031.

Adaptive Industrial Control Systems via IEC 61499 and Runtime Enforcement

This work envisions industrial control systems that can reliably adapt to requirements. We rely on the international standard IEC 61499 to achieve this goal. The standard allows downtimeless system evolution such that an application can be modified at runtime to satisfy the requirements. However, an IEC 61499 application consisting of multiple Function Blocks (FBs) can be modified in many different ways, such as inserting or deleting FBs, creating new FBs with their respective internal behaviours and adjusting the connections between FBs. These changes require considerable effort and cost, and there is no guarantee to satisfy the requirements. This article applies runtime enforcement techniques for supporting adaptive IEC 61499 applications. This set of techniques can modify the runtime behaviour of a system according to specific requirements. Our approach begins with specifying the requirements as a state machine-based notation called contract automaton. This automaton is then used to synthesise an enforcer as an FB. Finally, the new FB is integrated into the application to execute according to the requirements. A tool support is developed to automate the approach. Experiments were performed to evaluate the performance of enforcers by measuring the execution time of several applications before and after the integration of enforcers.

Bioinspired design and validation of a soft robotic end-effector with integrated SMA-driven suction capabilities.

The exploration of adaptive robotic systems capable of performing complex tasks in unstructured environments, such as underwater salvage operations, presents a significant challenge. Traditional rigid grippers often struggle with adaptability, whereas bioinspired soft grippers offer enhanced flexibility and adaptability to varied object shapes. In this study, we present a novel bioinspired soft robotic gripper integrated with a shape memory alloy (SMA) actuated suction cup, inspired by the versatile grasping strategies of octopus arms and suckers. Our design leverages a tendon-driven composite arm, enabling precise bending and adaptive grasping, combined with SMA technology to create a compact, efficient suction mechanism. We develop comprehensive kinematic and static models to predict the interaction between arm bending deflection and suction force, thereby optimizing the gripper's performance. Experimental validation demonstrates the efficacy of our integrated design, highlighting its potential for advanced manipulation tasks in challenging environments. This work provides a new perspective on the integration of bioinspired design principles with smart materials, paving the way for future innovations in adaptive robotic systems.&#xD.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Human Resource Planning Strategy to Improve the Quality of Online Library Management Information Systems (E-Libraries) of Higher Education in the Industrial Era 5.0

Library management information systems in universities are essential for managing e-libraries in universities. The world of education will face one of the challenges after 2030: the use of information technology or robotics in various sectors of human life, one of which is university libraries. This research was conducted using a literature approach by collecting data from various relevant theoretical sources about human resource management and higher education management information systems in the context of university library human resource planning strategies. This article delves into the practical application of library human resource planning strategies in universities. It discusses the formulation of university library human resource planning and the development of human resources in the era of Society 5.0. Furthermore, it explores the challenges and opportunities in implementing these strategies, such as change management, adaptive higher education management information system policies, and the development of librarians' professionalism in the era of society 5.0. This study uses a literature approach by collecting data from relevant theoretical sources about library human resource management and higher education management information systems in the educational context of university library human resource planning strategies. Data is analyzed through systematic literature synthesis to identify university library human resource planning strategies that can improve the quality of management e-library in the era of Society 5.0. In this era, adaptive and technology-oriented human resource management strategies are the key to improving the quality of university e-library management.

Angle‐Based Neuromorphic Wave Normal Sensing

AbstractAngle‐based wavefront sensing has a rich historical background in measuring optical aberrations. The Shack–Hartmann wavefront sensor is widely employed in adaptive optics systems due to its high optical efficiency and high robustness. However, simultaneously achieving high sensitivity and large dynamic range is still challenging, limiting the performance of diagnosing fast‐changing turbulence. To overcome this limitation, angle‐based neuromorphic wave normal sensing, which serves as a differentiable framework developed on the asynchronous event modality is proposed. Herein, it is illustrated that the emerging computational neuromorphic imaging paradigm enables a direct perception of a high‐dimensional wave normal from the highly efficient temporal diversity measurement. To the best of available knowledge, the proposed scheme is the first to successfully surpass the spot‐overlapping issue caused by the curvature constraint in classical angle‐based wavefront sensing setups under challenging dynamic scenarios.

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights.

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-linear thermal radiation and exponential and thermal- space dependent heat source/sink coefficients are considered with the intent of conceiving an Runge-Kutta-Fehlberg method with shooting procedures integrated with a combination of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Reptile Search Algorithm (RSA). Then, ANFIS-RSA, is used to predict the Nusselt number, skin friction co-efficient in radial and tangential velocities. Reliable self-similarity variables have reduced a non-linear partial differential set of equations into an ordinary differential equation. According to the empirical evidence, Sisko fluid parameter rises the radial velocity whereas for magnetic field and Darcy-Forchheimer the azimuthal and axial velocities visualizations decreasing trend, respectively. The entropy generation and Bejan number rises for electric and radiation effects. Also, ANFIS-RSA indicates that the model attained a high level of precision in terms of radial velocity (98.13%), tangential velocity (98.18%) and Nusselt number (98.91%). Thus, the longer rendering of the nanoparticles used here might, makes them potentially helpful for regulating the therapeutic impact in the management and treatment of cancer.

Closed-loop control strategy design for Caputo–Fabrizio definition-based fractional-order Boost converters considering parametric uncertainties

This paper proposes a dual closed-loop adaptive PI control system based on parameter identification for Caputo–Fabrizio (C-F) definition-based fractional-order boost converters. First, a C-F definition-based fractional-order model of the Boost converter is derived. Afterward, an online component parameter identification method is proposed to obtain the values and orders of capacitors and inductors in the proposed model. Based on these identified parameters, a dual closed-loop adaptive PI control system considering parametric uncertainties for the Boost converters is designed using the frequency domain analysis method. Finally, simulation validations are conducted. The online parameter identification method can accurately identify the values and orders of capacitors and inductors. The identification error is less than [Formula: see text] for the capacitance value and [Formula: see text] for the other parameters. Moreover, the proposed control system can automatically adjust the parameters with changes in the component parameters, effectively improving the robustness. Compared to traditional PI controllers, when the parameters of components change, the proposed control system can reduce voltage overshoot by [Formula: see text] in the case of sudden changes in the input voltage. These results confirm the efficacy of the proposed online parameter identification method and the dual closed-loop adaptive PI control system.

Abstract A008: Adaptive immune receptor repertoire characterization highlights the importance of interplay between B and T cells in disease progression

Abstract Understanding the adaptive immune system is integral to disease progression studies, development of diagnostic biomarkers, identification of therapeutic targets, and discerning varied treatment responses. Despite growing interest in profiling B and T cells in cancer research, the interaction between these cells and also the crucial roles of γδ T cells often go overlooked. In this research project, we explored the role of crucial yet frequently ignored γδ T cells in cancer research, using immune repertoire data from bladder cancer patients. We also studied the synergy between B and T cells in the immune repertoires of COVID-19 patients.Roche has developed immunoPETE (immune repertoire Primer Extension Target Enrichment), a DNA- based target enrichment assay to capture adaptive immune repertoire data*. immunoPETE efficiently identifies the heavy chains of B cells, alongside αβ and γδ T cells in one reaction, facilitating normalized clone counts across the adaptive immune system. To explore the significance of γδ T cells, we profiled the immune repertoires of 24 Non-Muscle Invasive Bladder Cancer patients treated with Bacillus Calmette-Guerin (BCG). Additionally, we evaluated the interplay between B and T cells in 117 peripheral blood immune repertoire from healthy and COVID-19 afflicted individuals, ranging from mild to severe cases.Immune repertoire data analysis indicates that underrepresentation of T cell receptor delta locus (TRD) is associated with higher risk of bladder cancer recurrence or progress. Urine pellet DNA was available for a subset of the patients in this study. Comparing immune repertoires from PBMC and urine revealed that urine repertoires represent the tumor repertoire more accurately than PBMC. Analysis of the immune repertoires from the COVID-19 patients linked disease severity with immune repertoire measures across all three cell types, highlighting the importance of multiple lymphocyte classes in disease progression. Using immunoPETE technology, which is capable of capturing the heavy chain of multiple immune cell types simultaneously in a single reaction, we have demonstrated that both B cell and T cell compartments provide distinct and valuable information in cancer and infectious diseases, each responding uniquely to the disease. Additionally, we emphasized the significance of γδ T cells in predicting cancer prognosis. Note: immunoPETE is for Research Use Only. Not for use in diagnostic procedures. Citation Format: Hossein Asgharian, Hamid Mirebrahim, Dilduz Telman, Hahn Zhao, Ulrich Schlecht, Sylvie McNamara, Rajiv Dua, Jan Berka, Patrick Bogard, Dinesh Kumar. Adaptive immune receptor repertoire characterization highlights the importance of interplay between B and T cells in disease progression [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A008.

Nurse Managers' Experiences in Organisational Adaptation During Public Health Emergencies: A Qualitative Study.

To elucidate strategies for improving organisational adaptation during public health emergencies from the perspectives of nurse managers. This study utilised a qualitative approach, incorporating complex adaptive system theory within a phenomenological tradition. Semi-structured interviews were conducted from May 2022 to June 2022. Participants included core members of the Shanghai Public Health Emergency Teams-Nursing from 12 tertiary hospitals in Shanghai, all of whom had substantial experience as nurse leaders impacting health systems. Data obtained were coded and analysed using interpretative phenomenological analysis. Seventeen frontline nurse managers participated, leading to the emergence of three key themes related to complex organisational adaptation: seeking institutional support for environmental adaptation, building intrateam support for structural adaptation and activating individual support for behavioural adaptation. A conceptual framework of organisational adaptation was proposed, displaying the interconnections among these themes. The study provides valuable insights into managing nursing teams involved in public health emergency preparedness and response as an adaptive system. Identified managerial strategies offer guidance to ensure effective organisational assembly and optimal utilisation of the nursing workforce within the healthcare system. Recognising the crucial role of nurses in managerial positions, who leverage leadership, adaptability and decision-making skills to coordinate frontline teams, is vital for effective public health emergency response. Their involvement in administrative roles underscores the importance of nursing perspectives in strategic planning, organisational adaptation and enhancing health system resilience during crises. The organisational adaptation strategies effectively adopted by nurse managers provide a reference for international scholars seeking to enhance the function and performance of nursing staff in healthcare system during public health emergencies. Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. No Patient or Public Contributions.

Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.

Implications of glioblastoma-derived exosomes in modifying the immune system: state-of-the-art and challenges.

Glioblastoma is a brain cancer with a poor prognosis. Failure of classical chemotherapy and surgical treatments indicates that new therapeutic approaches are needed. Among cell-free options, exosomes are versatile extracellular vesicles (EVs) that carry important cargo across barriers suchas the blood-brain barrier (BBB) to their target cells. Thismakes exosomes an interesting option for the treatment ofglioblastoma. Moreover, exosomes can comprise many therapeutic cargos, including lipids, proteins, and nucleic acids,sampled from special intercellular compartments of their origin cell. Cells exposed to various immunomodulatory stimulican generate exosomes enriched in specific therapeutic molecules. Notably, the secretion of exosomes could modify theimmune response in innate and adaptive immune systems. For instance, glioblastoma-associated exosomes (GBex) uptake bymacrophages could influence macrophage dynamics (e.g., shifting CD markers expression). Expression of critical immunoregulatory proteins such as cytotoxic T-lymphocyte antigen-1 (CTLA1) and programmed death-1 (PD-1) on GBex indicates the direct crosstalk of these nano-size vesicles with the immune system. The present study reviews the role of exosomes in immune system cells, including Bcells, Tcells, natural killer (NK) cells, and dendritic cells (DCs), as well as novel technologies in the field.

A Formally Integrated Adaptive Speed Management for Proactive Traffic Safety

The emergence of connected autonomous vehicles (CAVs) represents a key development in the quest to enhance traffic safety. CAVs hold significant promise for improving traffic safety and have great potential to contribute to transportation sustainability. However, their safety depends on the accuracy of the programmed rules and algorithms that guide their decision-making process. This paper introduces an adaptive traffic management system that integrates a formal traffic safety rule defined by pre-established bounds for Traffic Conflict Techniques. This system enables dynamic speed adjustments for vehicles violating the traffic safety rule in order to prevent potential collisions. To evaluate the effectiveness of our approach, we study a traffic flow on the SR528 highway in Orlando, Florida, and analyze the behavior of each vehicle in traffic based on extracted traffic safety indicators such as time-to-collision and space headway. This analysis is performed by the traffic safety rule to identify violating vehicles, and the speed update is achieved through the integration of the computer algebra system Mathematica and a micro-simulation tool called SUMO. Our study aims to improve the safety and efficiency of the traffic flow by combining simulation, TCTs analysis, and semi-formal tools like Mathematica to aid in the decision-making process of vehicles during traffic events, particularly shockwaves. Preliminary results demonstrate the efficacy of our approach in mitigating the impact of shockwaves. After applying the speed update, we observe an increase in time-to-collision and space headway. This indicates improved safety and reduced likelihood of collisions. Our findings highlight the potential of adaptive traffic management systems to enhance transportation safety and efficiency.

Characterization of variably protease-sensitive prionopathy by capillary electrophoresis.

Variably Protease Sensitive Prionopathy (VPSPr) is a rare human prion disease that, like Creutzfeldt-Jakob disease (CJD), results in the deposition of abnormally folded prion protein aggregates in the brain and is ultimately fatal. Neuropathology and clinical features of VPSPr are heterogeneous. However, the key discriminating feature is the relative sensitivity of the pathological prion protein to proteinase digestion compared to that typically seen in other human prion cases. Three major fragments of 23, 17 and 7kDa are characteristic of the disease following digestion with proteinase K. We recently reported the utility of the highly adaptive and reproducible ProteinSimple™ capillary electrophoresis (CE) system to perform protein separation of PK digested prion protein in CJD. Consequently, we explored capillary-based electrophoresis (CE) technology as a sensitive method to detect and characterize VPSPr in a cohort of 29 cases. The unique 7kDa fragment has high intensity, particularly in cases with the codon 129 VV genotype, but can be missed by regular Western blotting due to the small size. However, this fragment is readily detected by CE in all cases. In addition, the flexibility of CE produced highly reproducible, semi-quantitative data for determining relative proteinase K sensitivity and epitope mapping of representative cases from each codon 129 genotype (VV, MV and MM).

A performance-centered design method for adaptive cruise control system

A performance-centered design method for adaptive cruise control system

Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing

This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction.

Hunting for Sustainability: Indigenous Stewardship in the Cofán Territory of Zábalo

ABSTRACTIndigenous lands are increasingly recognized for their critical role in biodiversity conservation. However, concerns persist about the sustainability of hunting practices within these territories. This study investigates the long‐term impact of Indigenous hunting practices on wildlife populations in the Cofán territory of Zábalo, Ecuador. We employed line transect surveys to estimate wildlife densities over three periods (2001–2003, 2006–2009, and 2018), focusing on 16 species that are integral to local livelihoods and subject to community management. Our results indicate generally stable or increasing populations for most species, particularly in nonhunted areas, where spatial restrictions are enforced. The Cofán's ability to sustainably manage relations between humans and wildlife is largely attributable to their adaptive management system, which includes species‐specific regulations and spatial zoning, and is realized through community decision‐making, knowledge sharing, and an ethos of care. These findings demonstrate that Indigenous stewardship can effectively sustain biodiversity in complex biocultural systems. The study underscores the importance of integrating Indigenous knowledge with scientific approaches to achieve long‐term conservation goals.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.