Efficient Implementation Research Articles

Making treatment guideline recommendations in chronic kidney disease and type 2 diabetes more accessible to primary care providers in the United States.

Clinical practice guidelines for the management of chronic kidney disease (CKD) associated with type 2 diabetes (T2D) are designed to assist healthcare professionals with clinical decision making by providing recommendations on the screening, detection, management, and treatment of these conditions. However, primary care practitioners (PCPs) may have clinical inertia when it comes to routinely enacting CKD and T2D guideline recommendations in their clinical practices. Guideline developers have published a range of resources with the aim of facilitating easier access to guideline recommendations to support efficient and consistent implementation into clinical practice of PCPs. Challenges remain in providing strategies to reduce inertia in the application of guideline recommendations in primary care. In this review, we explore reasons behind the low level of awareness and poor uptake of published evidence-based care approaches to the optimal management of patients with T2D and CKD. Finally, we present suggestions on strategies to improve the implementation of guideline-directed recommendations in primary care.

Contextual analysis of the first SARS-CoV-2 RNA screening period in nasopharyngeal swabs, 2020-2022: a comparison of two diagnostic tests

Background: exceptionally consistent COVID-19 laboratory diagnostics are crucial for case identification, patient management and contact tracing. The Coronavirus-2019 (COVID-19) pandemic affected over 771.407.825 people up until October 2023, with over 6.9 million deaths. The current process of clinical laboratory consolidation, impacting large geographic areas, presents an opportunity for the efficient and cost-effective implementation of novel laboratory technologies, as well as advancements in translational research and development. The aim of this study was to assess which of the two instruments could offer the most effective support to our laboratory’s activities, minimizing errors during the pre-analytical phase, optimizing human resources, and reducing the Turn-Around-Time (TAT). Materials and Methods: the diagnostic instruments available in the Microbiology laboratory of the Azienda Ospedaliero- Universitaria SS Antonio e Biagio e Cesare Arrigo (AOUAL) were the COBAS 6800 and the ALINITY platforms. Conclusions: the Alinity platform offers clinicians a more user-friendly approach to understanding patient infectivity, compared to the closed Cobas system. It permits clinicians to review curves and access a cumulative Cycle Threshold (Ct), facilitating the hypothesis of acute or initial/final infection stages. This positions Alinity by Abbott as the preferred system over other instruments.

Pattern matching algorithms in blockchain for network fees reduction

AbstractBlockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).

Energy decomposition analysis method using density matrix formulation

In this work, an energy decomposition analysis (EDA) method with the strategy of density matrix, called DM-EDA, is proposed on the basis of single reference electronic structure calculations. Different from traditional EDA methods, instead of an intermediate state wave function, the EDA terms in DM-EDA are expressed in the forms of the density matrix. This method can be carried out with various kinds of density matrices. With the efficient implementation, DM-EDA not only greatly improves the computational efficiency but also provides quantitative knowledge of intermolecular interactions with a large number of monomers.

Process-in-Memory realized by nonvolatile Task-Scheduling and Resource-Sharing XNOR-Net hardware Accelerator architectures

Due to the success of artificial intelligence systems in solving complex problems, these systems have been investigated in many applications. There is an increasing demand for these systems in many new applications. The XNOR-Net, a binary neural network, is a promising candidate for implementing these systems in devices with limited power and hardware resources. Efficient hardware implementation of XNOR-Nets is critical and has been widely investigated in recent research. In this paper, a resource-sharing gate and architecture and a task-scheduling gate and architecture have been proposed. To reduce power consumption, the proposed nonvolatile logic-in-memory gates are designed based on the magnetic tunnel junction (MTJ) and carbon nanotube field-effect transistor (CNTFET) devices. Using the proposed resource-sharing gate for hardware implementation of XNOR-Net minimizes the number of flip-flops, power consumption, PDP, and area by at least 75%, 75%, 60%, and 42%, respectively, as compared to its state-of-the-art counterparts. Also, using the proposed task-scheduling design to implement the XNOR-Net on 2, 4, and 8 datasets, the power consumption is reduced by at least 24%, 54%, and 65%, respectively, compared to its state-of-the-art counterparts .

Parallel ensemble Kalman method with total variation regularization for large-scale field inversion

Field inversion is often encountered in data-driven computational modeling to infer latent spatial–varying parameters from available observations. The ensemble Kalman method is emerging as a useful tool for solving field inversion problems due to its derivative-free merits. However, the method is computationally prohibitive for large-scale field inversion with high-dimensional observation data, which necessitates developing a practical efficient implementation strategy. In this work, we propose a parallel implementation of the ensemble Kalman method with total variation regularization for large-scale field inversion problems. It is achieved by partitioning the computational domain into non-overlapping subdomains and performing local ensemble Kalman updates at each subdomain parallelly. In doing so, the computational complexity of the ensemble-based inversion method is significantly reduced to the level of local subdomains. Further, the total variation regularization is employed to smoothen the physical field over the entire domain, which can reduce the inference discrepancy caused by missing covariances near subdomain interfaces. The capability of the proposed method is demonstrated in three field inversion problems with increasing complexity, i.e., the diffusion problem, the scalar transport problem and the Reynolds averaged Navier-Stokes closure problem. The numerical results show that the proposed method can significantly improve computational efficiency with satisfactory inference accuracy.

Efficient implementations of CRAFT cipher for Internet of Things

The rapid growth of the Internet of Things (IoT) highlights the importance of lightweight cryptography in maintaining security. However, enhancing performance while ensuring the same level of security remains a significant challenge. This paper presents two innovative architectures for the CRAFT lightweight block cipher, aiming to enhance performance without compromising security. The novel Serial and Unrolled architectures are introduced to achieve low area usage and high throughput, respectively. Specifically, the Serial architecture reduces the datapath from 64-bit to 4-bit, significantly decreasing the area. The Unrolled architecture, on the other hand, minimizes latency from 32 to 16. Additionally, Boolean satisfiability (SAT) solvers are employed to identify a lower-cost area implementation of the S-Box. The proposed designs underwent evaluation on three distinct FPGA platforms: Artix-7, Kintex-7, and Spartan-7. The results show that the low area design reduces area usage by 10.16% compared to the previous design. Additionally, the S-Box implementation achieves a significant area reduction of 28.9%. On the other hand, the unrolled design enhances the maximum throughput by 40.53% compared to the previous design. Therefore, the proposed designs could offer enhanced performance while maintaining security for IoT devices.

Analytical approximation of the definite Chapman integral for arbitrary zenith angles

Abstract. This study presents an analytical approximation of the definite Chapman integral, applicable to any zenith angle and finite integration limits. The author also presents the asymptotic expression for the definite Chapman integral, which enables an accurate and efficient implementation free of numerical overflows. The maximum relative error in our analytical solution is below 0.5 %.

The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks.

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

Physically‐based analytical erosion for fast terrain generation

AbstractTerrain generation methods have long been divided between procedural and physically‐based. Procedural methods build upon the fast evaluation of a mathematical function but suffer from a lack of geological consistency, while physically‐based simulation enforces this consistency at the cost of thousands of iterations unraveling the history of the landscape. In particular, the simulation of the competition between tectonic uplift and fluvial erosion expressed by the stream power law raised recent interest in computer graphics as this allows the generation and control of consistent large‐scale mountain ranges, albeit at the cost of a lengthy simulation. In this paper, we explore the analytical solutions of the stream power law and propose a method that is both physically‐based and procedural, allowing fast and consistent large‐scale terrain generation. In our approach, time is no longer the stopping criterion of an iterative process but acts as the parameter of a mathematical function, a slider that controls the aging of the input terrain from a subtle erosion to the complete replacement by a fully formed mountain range. While analytical solutions have been proposed by the geomorphology community for the 1D case, extending them to a 2D heightmap proves challenging. We propose an efficient implementation of the analytical solutions with a multigrid accelerated iterative process and solutions to incorporate landslides and hillslope processes – two erosion factors that complement the stream power law.

Legal analysis of the merits of electronic transferable records: toward cross-border trade digitalization

Purpose The basis for this study on electronic transferable records (ETRs) in the context of digitalizing cross-border trade is established in this chapter. It illustrates how the growing trend toward paperless trade and technological improvements is changing trade operations. This chapter focuses on the need to look into ETRs because of their capacity to influence business transactions while navigating complex legal issues. The specific goals of the study are outlined, including a review of the advantages, legality, difficulties and best practices of ETRs. This study aims to shed light on the possible advantages and disadvantages of ETRs, the legal framework that controls their use and the best practices for their efficient implementation. This study also seeks to provide informative recommendations for businesses and people that are considering using ETRs. Design/methodology/approach This study explores the evolving world of ETRs and their crucial function in international trade. Multidimensional technique is used to examine the transformative potential of ETRs from a variety of research angles. The research design is based on a comprehensive evaluation of the literature that includes a wide range of reliable sources, including academic papers, business reports and legal documents. The comprehensive retrieval of essential material is ensured through keyword searches in renowned academic databases and industry resources. The qualitative synthesis of secondary sources further enhances this methodology and allows for a complex examination of the implications of ETRs. The case study analysis provides practical information on the benefits, hazards and practical applications of ETRs. Multifaceted aspects are uncovered via a thematic approach and qualitative investigation, including potential advantages, hazards, implementation plans and regulatory frameworks. Findings ETRs offer a range of potential advantages for cross-border trade, encompassing augmented efficiency, reduced costs and heightened security. Nonetheless, their implementation also presents legal challenges and risks, spanning security and privacy concerns, legal ambiguities and technical complexities. Consequently, it is crucial for individuals and businesses to meticulously assess and mitigate these risks through the integration of robust security protocols, staying informed about legal developments and adhering to pertinent regulatory stipulations. In spite of these hurdles, the trajectory of ETR adoption is anticipated to remain on an upward trajectory, driven by increasing recognition of their potential benefits and the concurrent evolution of legal frameworks and technical standards. Research limitations/implications Research limitations included the following: lack of adoption of ETRs internationally; and legal diversity and different legal systems results in different consideration of the ETRs. It makes reaching a unified ETR system more difficult. Practical implications It is necessary to develop clear policies and procedures and establish well-defined policies and procedures governing ETR use. These should encompass security guidelines, data protection measures and adherence to legal mandates. Regular review and updates are imperative. Stay current on legal developments: In light of the continuously evolving legal and regulatory landscape pertaining to ETRs, businesses and individuals must stay abreast of pertinent changes and seek professional counsel when necessary. Collaborate with partners and stakeholders: To ensure harmonization and standardization in ETR deployment, active collaboration with partners, regulators and industry associations is vital. Social implications Enhance awareness and education: Investment in awareness and educational initiatives is crucial. Decision-makers should organize training programs, workshops and seminars to enhance understanding of ETRs’ potential benefits in cross-border trade among stakeholders. Socially, the use of ETR can achieve several political advantages for the society. It minimizes risks of corruption through enhancing tracing and auditing abilities for relevant authorities making it more difficult to engage in corrupt practices. That can promote integrity within government and public procurement system. Originality/value The development of standardized technical frameworks and interoperable platforms for ETRs could enhance their seamless integration into existing trade systems. Additionally, investigating the integration of emerging technologies like blockchain, IoT and AI into ETR ecosystems could unlock innovative solutions to security, authenticity and data management concerns. This study examines how ETRs can radically alter how trade is conducted on a global scale. This paper examines ETRs’ role in improving cross-border trade digitization by examining their advantages, legal difficulties and implementation techniques. The conclusions will aid firms, decision-makers and attorneys in navigating the constantly changing world of trade agreements. The study’s ultimate goal is to offer takeaways that support effective, secure and legally compliant integration of ETRs, ensuring that they operate as a catalyst for improved global trade efficacy and efficiency.

Knowledge, attitude, and practices of stakeholders involved in healthcare financing programs on economic evaluations in Cameroon.

There are many healthcare financing programs (HFPs) in Cameroon; however, there is a lack of information on these programs' economic effectiveness and efficiency. Involvement of local stakeholders in the economic evaluations (EEs) of HFPs is critical for ensuring contextual factors are considered prior to program implementation. We conducted a cross-sectional study to assess the need for EEs of Cameroonian HFPs. Regular staff in supervisory roles aged 18 years and above were recruited in four Cameroonian cities. Data were collected via face-to-face surveys between June 15 and August 1, 2022. Descriptive analyses summarized participants' knowledge, attitudes, and practices in relation to performing EEs of HFPs. Principal component analyses identified organizational, individual, and contextual factors that could influence participants' involvement. The total sample included 106 participants. On average, 65% of participants reported being aware of the listed HFPs; however, of these, only 28% said that they had been involved in the HFPs. Of the 106 participants, 57.5% knew about EEs; yet, almost 90% reported that the HFP in question had never been subject to an EE, and 84% had never been involved in an EE. Most participants indicated that they had intended or would like to receive EE training. Using principal component analyses, the organizational factors were classified into two components ('policy and governance' and 'planning and implementation'), the individual factors were classified into two components ('training' and 'motivation'), and the contextual factors were classified into three components ('funding,' 'political economy,' and 'public expectations'). The findings of this study highlight the need to invest in EE training to improve participation rates of Cameroonian stakeholders in the EEs of HFPs. Improved knowledge, diversified skills, and increased participation of stakeholders from all levels of the Cameroonian healthcare system are critical to the effective and efficient development, implementation, and EE of the country's HFPs.

Robust and efficient implementation of finite strain generalized continuum models for material failure: Analytical, numerical, and automatic differentiation with hyper-dual numbers

Generalized continuum models for representing nonlinear material behavior including material failure in the finite strain regime are commonly formulated based on scalar elastic and dissipation potential functions. The evolution of stresses and internal variables, i.e., the material state, is governed by partial derivatives of the potential functions with respect to deformation and stress measures. Furthermore, for application of such models in implicit nonlinear finite element analysis tangent operators, consistent with the numerical integration algorithm, are required. In this work, we study analytical, numerical and automatic differentiation schemes for the implementation of coupled, generalized continuum models considering finite inelastic deformations and fracture. This includes the application of finite difference approximations, complex-step derivative approximations and automatic differentiation based on hyper-dual numbers. For the use of automatic differentiation, a semi-analytical split approach is introduced for increasing the computational efficiency. Based on a comprehensive 2D and 3D finite element study, we demonstrate the superior properties of automatic differentiation compared to numerical differentiation methods with regards to accuracy and robustness. Furthermore, the additional computational cost for automatic differentiation using the proposed split approach becomes negligible for large problems.

Edge Federated Optimization for Heterogeneous Data

This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase the effective operations of deep learning models on resource-constrained edge devices. Experimental results show that at a pruning rate of 10%, the FedProx with structured pruning in the MIT-BIH and ST databases achieved the best F1 scores, reaching 96.01% and 77.81%, respectively, which achieves a good balance between system efficiency and model accuracy compared to those of the FedProx with the original configuration, reaching F1 scores of 66.12% and 89.90%, respectively. Similarly, with layer freezing technique, unstructured pruning method, and a pruning rate of 20%, the FedAvg algorithm effectively balances classification performance and degradation of pruned model accuracy, achieving F1 scores of 88.75% and 72.75%, respectively, compared to those of the FedAvg with the original configuration, reaching 56.82% and 85.80%, respectively. By adopting model optimization strategies, a practical solution is developed for deploying complex models in edge federated learning, vital for its efficient implementation.

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.

DuMato: An efficient warp-centric subgraph enumeration system for GPU

Subgraph enumeration is a heavy-computing procedure that lies at the core of Graph Pattern Mining (GPM) algorithms, whose goal is to extract subgraphs from larger graphs according to a given property. Scaling GPM algorithms for GPUs is challenging due to irregularity, high memory demand, and non-trivial choice of enumeration paradigms. In this work we propose a depth-first-search subgraph exploration strategy (DFS-wide) to improve the memory locality and access patterns across different enumeration paradigms. We design a warp-centric workflow to the problem that reduces divergences and ensures that accesses to graph data are coalesced. A weight-based dynamic workload redistribution is also proposed to mitigate load imbalance. We put together these strategies in a system called DuMato, allowing efficient implementations of several GPM algorithms via a common set of GPU primitives. Our experiments show that DuMato's optimizations are effective and that it enables exploring larger subgraphs when compared to state-of-the-art systems.

A customizable cam-typed bistable nonlinear energy sink

A novel cam-typed bistable nonlinear energy sink (CBNES) is first proposed in this paper. The CBNES achieves a customized design of the ideal configuration of BNES by introducing a cam-roller-spring negative stiffness mechanism (CRS-NSM). The design criteria for continuous and smooth bistable cam profiles are studied, and the extreme values of dimensionless stiffness and pre-compression are predicted. Furthermore, the periodic and quasi-periodic dynamics of the system are studied by using the complexification-averaging (CA-X) method. By combining Slow Invariant Manifold (SIM) and phase trajectory, the energy dissipations of different response types of the CBNES are predicted. The SMR and chaotic response have the best and lowest targeted energy transfer (TET) efficiency, respectively. The saddle-node (S-N) and Holf bifurcation characteristics of the periodic fixed points are analyzed to predict the possibility of SMR occurrence. The analysis of the amplitude frequency curve with the variation of system parameters indicates that weak negative stiffness and intermediate cubic stiffness are more beneficial to vibration suppression. The highlight is that the excitation thresholds of SMR are predicted by using analytical and numerical methods, and the influences of system parameters on the thresholds are analyzed. The deviation between the analytical and numerical results will decrease with increasing damping. Finally, the correctness of the analytical results is verified through a series of fixed-frequency experiments. The proposed CBNES provides a valuable potential solution for designing efficient and robust mechanical BNES physical implementations.

How different attributes are weighted in professionals' decision-making in Pediatric Dentistry-a protocol for guiding discrete choice experiment focused on shortening the evidence-based practice implementation for dental care.

Important evidence has been constantly produced and needs to be converted into practice. Professional consumption of such evidence may be a barrier to its implementation. Then, effective implementation of evidence-based interventions in clinical practice leans on the understanding of how professionals value attributes when choosing between options for dental care, permitting to guide this implementation process by maximizing strengthens and minimizing barriers related to that. This is part of a broader project investigating the potential of incorporating scientific evidence into clinical practice and public policy recommendations and guidelines, identifying strengths and barriers in such an implementation process. The present research protocol comprises a Discrete Choice Experiment (DCE) from the Brazilian oral health professionals' perspective, aiming to assess how different factors are associated with professional decision-making in dental care, including the role of scientific evidence. Different choice sets will be developed, either focusing on understanding the role of scientific evidence in the professional decision-making process or on understanding specific attributes associated with different interventions recently tested in randomized clinical trials and available as newly produced scientific evidence to be used in clinical practice. Translating research into practice usually requires time and effort. Shortening this process may be useful for faster incorporation into clinical practice and beneficial to the population. Understanding the context and professionals' decision-making preferences is crucial to designing more effective implementation and/or educational initiatives. Ultimately, we expect to design an efficient implementation strategy that overcomes threats and potential opportunities identified during the DCEs, creating a customized structure for dental professionals. https://osf.io/bhncv .

Securing IoT devices with fast and energy efficient implementation of PRIDE and PRESENT ciphers

The rise of low-power, cost-efficient internet-connected devices has led to a need for lightweight cryptography. The lightweight block cipher PRIDE, designed by Martin R. Albrecht, is one of the most efficient ciphers designed for IoT-constrained environments. It is useful for connected devices, requires fewer resources to implement, and has high performance. PRIDE is a software-oriented lightweight cipher optimized for microcontrollers. This paper focuses on the FPGA implementation of the PRIDE cipher by keeping throughput, energy, and power consumption metrics focused. The paper also presents a novel and simpler diagrammatical view of a Matrix Layer implementation of the PRIDE cipher. We also implemented the PRESENT cipher using the same metrics. We analyzed different design metrics on Field Programmable Gate Arrays (FPGAs) and compared the metrics of the PRIDE implementation with the well-known cipher PRESENT. This gives us an insight into the efficiency and reliability of PRIDE in IoT-constrained environments. We also proposed different architectures of the PRIDE cipher for 16-bit and 32-bit datapaths.

“Director General Review” as a Viable Mechanism to Safeguard Malaysia’s Revenue Through Tax Justice

The Revised Kyoto Convention (RKC) serves as a guide for Customs procedures in the modern era, and Malaysia has signed on as a participating country. One crucial element of the RKC, outlined in Chapter 10 of the text of the convention, is the requirement for national legislation to include a right of appeal in Customs matters. Subsection 143(1) of the Customs Act 1967 empowers the Director General (DG) of Customs to review his own previous decisions, and the Royal Customs Malaysian Department (RMCD) has devised a systematic process referred to in this research as the "DG review." This provision was enacted to comply with the RKC's standards. Subsection 143(1) was amended in 2020 to allow aggrieved parties to submit their applications for “DG review” within 30 days from any DG decisions in the indirect taxes. Despite the amendment, no prior research has been conducted on its effectiveness. This study aims to evaluate the viability of the DG review mechanism in promoting fair and efficient tax implementation. The research employs both doctrinal legal analysis and qualitative methods to scrutinize the laws and regulations governing the right of appeal for indirect tax in Malaysia, as well as the fairness of the DG review process for taxpayers. The outcome of this study is pivotal in proposing legal solutions and validating the amended mechanism outlined in subsection 143(1) of the Customs Act 1967.