Modern Systems Research Articles

Reinforcement Learning for Intra- & Inter-Node Recommender Data Pipeline Optimization

Deep learning-based recommender models (DLRMs) have become an essential component of many modern recommender systems. Several companies are now building large compute clusters reserved for DLRM training, driving new interest in cost- & time- saving optimizations. The systems challenges faced in this setting are unique; while typical deep learning (DL) training jobs are dominated by model execution times, the most important factor in DLRM training performance is often online data ingestion. In this paper, we study real-world DLRM data processing pipelines taken from our compute cluster at Netflix to observe the performance impacts of online ingestion and identify shortfalls in existing data pipeline optimizers. Our studies lead us to design a new solution for data pipeline optimization, InTuneX . InTuneX is designed for production-scale multi-node recommender data pipelines. It unifies & tackles the challenges of both intra- and inter- node pipeline optimization. We achieve this with a multi-agent reinforcement learning (RL) design, simultaneously optimizing node assignments at the cluster level & CPU assignments within nodes. Our experiments show that InTuneX can build optimized data pipeline configurations within minutes. We apply InTuneX to our cluster, and find that it increases single-node data ingestion throughput by as much as 2.29X versus state-of-the-art optimizers, while improving the cost-efficiency of multi-node pipelines by 15-25%.

Digital forensics architecture for real-time automated evidence collection and centralization: Leveraging security lake and modern data architecture

Abstract In the face of escalating cyber threats, a real-time automated security evidence collection system for cloud-based digital forensics investigations is essential for identifying and mitigating malicious activities. However, the substantial volumes of data generated by modern cloud-based digital systems pose difficulties in collecting and analyzing evidence promptly and systematically. To address these challenges, this research introduces an architecture that combines a security lake and a modern data lake. The primary objective of this architecture is to overcome the obstacles associated with gathering evidence from multiple cloud-based accounts and regions while ensuring the flexibility and scalability required to manage the ever-expanding data volumes encountered in cloud-based digital forensics investigations. This work focuses on gathering security events from multiple accounts and regions within a cloud environment in real-time while maintaining the integrity of the evidence and storing them in lakes, providing investigators with the flexibility to move between these lakes for analysis to get quick results. This is achieved through the utilization of security lake and modern data architecture. To validate the system, we tested it within a university system comprising numerous accounts spread across different regions within an AWS environment. Overall, the proposed system effectively gathers evidence from various sources and consolidates all data lakes into a single account. These lakes were then utilized for analyzing the evidence using Athena and Wazuh.

Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning

Virtual power plants (VPPs) have emerged as an innovative solution for modern power systems, particularly for integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), a Bi-directional Gated Recurrent Unit (BiGRU), and an attention mechanism to enhance the forecasting accuracy of wind and photovoltaic power generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared to traditional models, achieving high accuracy and robustness. These results provide a reliable basis for optimizing VPP operations and integrating renewable energy sources effectively.

Mixed Reform of State-owned Enterprises and High-quality Development of Enterprises

The reform of mixed ownership in SOEs advances their high-quality development through deepened reforms, optimized state economic layouts, and enhanced capital allocation efficiency. Utilizing A-share listed company data from 2008 to 2022, this study employs a two-way fixed effects model for empirical analysis. The findings reveal a notable positive correlation between SOE mixed-ownership reforms and improvements in corporate governance, incentive mechanisms, and constraints. Moreover, the role of party building plays a crucial moderating function in these reforms. This research elucidates the integration of public ownership with the market economy for listed firms, identifies fresh strategies to boost SOE vitality, efficiency, and competitiveness, and supports the establishment of modern enterprise systems. It also offers theoretical underpinning for the transformation, upgrade, and sustainable growth of state-owned enterprises.

Optimal power flow solution incorporating hybrid conventional and renewable resources using electric eel foraging optimization algorithm

In recent years, metaheuristic algorithms have become the main tool in solving the Optimal Power Flow (OPF) problem due to their effectiveness in addressing complicated modern power systems. This complexity is fueled by the rise of Renewable Energy Resources (RERs) and the need to decrease greenhouse emissions. This research presents a comprehensive approach that aims to optimize the performance of power networks in the presence of thermal, wind, and Solar Photovoltaic (SPV) units. The algorithm implemented is named Electrical Eel Foraging Optimization (EEFO). It is carried out using the modified IEEE 30-bus test system. EEFO is compared alongside Kepler Optimization Algorithm (KOA) and Self-adaptive Bonobo Optimizer (SaBO). Two cases were taken into consideration. The first one is minimizing the Total Generation Cost (TGC); the second is minimizing generation cost, including the emission effects. The results show a reduction in TGC at 781.1981 $/h and 792.6531 $/h for the first and second cases, respectively; emissions were also decreased compared with previous studies. The findings obtained in this research show the validity of the proposed EEFO algorithm.

A Study to Understand Miasmatic Evaluation of Bronchial Asthma and Its Homeopathic Management

Asthma as a clinical entity has been known to the medical world since antiquity. Many physicians have described its exact symptomatology with aggravating and ameliorating factors, its epidemiology at that time and some other factors, but they could not know the exact cause of this condition. Many hypotheses were formed for the causes of this condition, some of which include effects of evil eye, wrath of devils, bad effects of some food etc., even till date, there are many etiologies of this condition has been noted, the exact cause of asthma is not determined. There are many influences that act with each other to produce this condition. Modern system of medicine believes in local concept of disease. They believe in materialistic concept of disease. They believe that in any disease condition the organs and tissues affected are affected solely, and they don’t have any correlation with other parts. So they try to manage bronchial asthma with anti-inflammatory and anti-histaminic medicines as maintenance therapy and bronchodilators to curb the acute exacerbation. Steroidal agents which are used are the potent anti-inflammatory agents that suppress the inflammatory reaction, but in a way only suppressing manifestation of disease, weakening the immunity and constitution of the patient and thus making him prone to more life threatening conditions as manifested by airway remodeling, fibrosis of airway basement membrane and decreased responsiveness of the airway smooth muscles leading to emphysematous changes superadded to asthma. Same condition happens with anti-histaminics. These drugs act at local level and do not give permanent relief. These drugs give temporary symptomatic relief to the patient and do not remove the disease, i.e. the cause for hypersensitivity. Among all medical systems for treating diseases, homeopathic system is most efficacious. It is based on natural observations, rational experiments, and logical inferences. It works on the basis of similia similibus curenture, the nature’s law of cure. In homeopathic system of medicine we consider disease as a whole, with its causes, the process, along with constitutional totality of the person and not the local manifestations of disease. Mind, body and spirit, these are called trinity of life. When considered together, they form the constitution of the person. They are in interaction with one another and responsible for maintaining life. Any defect in function of any of them cause dis-ease. Medicine selected on these bases removes the disease permanently and corrects the constitutional deficit/ miasm from the person, as an ideal cure preached by our master, Dr. Hahnemann. Miasms are the fundamental cause of the chronic diseases. There are basically 3 miasms described by Dr. Hahnemann, namely psora, sycosis, and syphilis. Initially, during the early days of practice, Dr. Hahnemann faced some obstacles to the permanent cure in chronic cases. This miasmatic block can be removed with proper anti-miasmatic remedies.Thus, we have undertaken Asthma as our topic of study.

Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.

Simulation of Voltage and Frequency Stabilization in PV-Based Microgrids Using Virtual Synchronous Generator Control

In recent years, distributed generation technologies and micro-grids have garnered increasing attention, particularly with the rapid growth of renewable energy sources (RESs) integrated via power electronic converters. As the penetration of RESs in modern power systems rises, voltage and frequency support, traditionally provided by synchronous generators, can be effectively managed by Virtual Synchronous Generator (VSG) control. This paper presents the modeling and analysis of voltage and frequency stability enhancement in a PV-based micro-grid system using VSG control. The micro-grid system under study operates at 400V, 50Hz, and is integrated with a 0.4kV power distribution line. Mathematical models of the VSG control strategy are developed in MATLAB/Simulink. Simulation results demonstrate that VSG control significantly improves the voltage and frequency stability of the PV-based micro-grid system.

Optimization of Delay and Area for Approximate Radix-8 Booth Multiplier

This paper investigates the optimization of Radix-8 Booth Multipliers, which are essential for efficient arithmetic operations in modern digital systems, particularly in applications such as digital signal processing, telecommunications, and image processing where rapid and accurate calculations are crucial. The study aims to enhance performance by focusing on reducing both delay and area while ensuring that acceptable accuracy levels are maintained for error-tolerant applications. To achieve these optimization goals, we compare three methodologies: the Carry Save Adder (CSA), the Kogge Stone Adder (KSA), and the Carry Look-Ahead Adder (CLA), each of which presents unique advantages and trade-offs in terms of speed, area utilization, and power consumption. The results of our analysis demonstrate that the Kogge Stone Adder provides the best overall performance in terms of speed and area efficiency, making it the most suitable choice for optimizing Radix-8 Booth Multipliers, particularly in scenarios where high performance and efficient resource use are critical. By emphasizing these findings, this study contributes valuable insights into the design of more efficient multipliers that can meet the increasing demands of contemporary digital applications. Key Words: Radix-8 Booth Multiplier, approximate computing, delay optimization, area optimization, Carry Save Adder (CSA), Kogge Stone Adder (KSA), Carry Look-Ahead Adder (CLA), digital systems, arithmetic operations, performance enhancement, error-tolerant applications, partial product generation, hardware description languages (HDLs), digital signal processing, machine learning, approximation techniques, simulation and testing, power consumption, ASIC design.

Pendidikan Islam Buya Hamka

In order to address the problem of value and moral imbalance that plagues the modern education system, this study aims to define the theory of Islamic education as understood by Buya Hamka. Hamka's books are collected as the main source for this study, and an assessment of him and his works is drawn from an analysis of other books. A qualitative research approach was used in this study. There is no other topic that can be explored and researched other than Hamka's views on education, particularly Islamic education in Indonesia. Maximum efforts in strengthening and developing individuals are emphasized in Buya Hamka's thought system on Islamic education. The findings of this study organize the manner in which Hamka's ideas developed, showing the relationship between his personal experiences and the common thread of his educational philosophy. To determine the applicability of Hamka's philosophy of Islamic education to current conditions, the author then conducts an analytical analysis of Hamka's thoughts in relation to these conditions. If this is done, it has the potential to improve people's lives in this country

A Hybrid Optimization Algorithm Based on Cascaded (1+PI)-PI-PID Controller for Load Frequency Control in Interconnected Power Systems

Electric power generation is crucial for electrical systems, and load frequency control (LFC) ensures system synchronization and reliability. Modern systems use well-designed controllers, with selecting the right type and design approach being essential for a good LFC controller design. This study introduces a cascaded (1+PI)-PI-PID controller optimized using the improved Artificial Bee Colony-Particle Swarm Optimization (IABC-PSO) algorithm for LFC in interconnected power systems, aiming to address complex challenges associated with LFC in modern power systems. The research examines a two-area power system model with hydro and thermal power plants and a capacitive energy storage (CES) device. It proposes a (1+PI)-PI-PID controller structure that integrates traditional PID controllers, enhancing performance and debugging capabilities. The study uses integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), and integral time square error (ITSE) as objective functions. The IABC-PSO algorithm introduces a novel decision block to increase scout bee (SB) occurrences to 10% for each employed bee (EB), and a new control search phase to optimize SBs' exploration capabilities in a controlled manner, enhancing their efficiency. The algorithm's performance was assessed using four benchmark functions, revealing superior results compared to the Hybrid ABC-PSO algorithm. The study examined six experimental configurations of the controller, varying the sequence of (1+PI), PI, and PID components. The proposed strategy was tested under 1% and 5% step load perturbations in area-1 and area-2 to evaluate dynamic performance. This study conducts a sensitivity analysis by adjusting hydro parameters within a ±25% range, and considers generation rate constraint (GRC) and boiler dynamics (BD) in a two-area reheat thermal-hydro-power system model for reliability and credibility. The proposed IABC-PSO algorithm outperforms traditional methods (ABC, PSO, and hybrid ABC-PSO, WOAw, GWO, hSFS-LUS, and FA) in optimizing parameters, achieving shortest settling times and minimal ITAE values. Its performance is evaluated under random step load perturbations (SLP) and its internal robustness is demonstrated through stability analysis using the Lyapunov method. In conclusion, the hybrid IABC-PSO algorithm-based cascaded (1+PI)-PI-PID controller offers a robust and efficient solution for LFC in interconnected power systems. It ensures enhanced performance, stability, and adaptability, making it a valuable contribution to modern power system management.

State estimation of DC microgrids using manifold optimization and semidefinite programming

DC microgrids are becoming more common in modern systems, so computation methodologies such as the power flow, the optimal power flow, and the state estimation require being adapted to this new reality. This paper deals with the latter problem, which consists of reconstructing the state variables given voltage and power measurements. Although the model of DC grids is undoubtedly less complicated than its counterpart AC, it is still a nonlinear/non-convex optimization problem. Our approach is based on the idea of solving the problem in a matrix space. Although it may be counter-intuitive to transform from Rn to Rn×n, a matrix space exhibits better geometric properties that allow an elegant formulation and, in some cases, an efficient form to solve the optimization problem. We compare two methodologies: semidefinite programming and manifold optimization. The former relaxes the problem to a convex set, whereas the latter maintains the geometry of the original problem. A specialized gradient method is proposed to solve the problem in the matrix manifold. Extensive numerical experiments are conducted to showcase the key characteristics of both methodologies. Our study aims to shed light on the potential benefits of employing matrix space techniques in addressing operation problems in DC microgrids and power system computations in general.

The Role of Stress Management with dhikr-based relaxation techniques in Reducing Teacher Burnout in the Era of Independent Learning Curriculum

This study investigates the effectiveness of a stress management technique using Islamic spiritual practices, known as dzikir, to reduce burnout among high school teachers in the context of an increasing administrative workload under the implementation of the new curriculum. Teachers frequently face high stress levels due to administrative demands, leading to emotional exhaustion, reduced job satisfaction, and lower teaching quality. This research aims to assess the impact of dzikir on alleviating stress and improving emotional and spiritual well-being among teachers. The study employed a quasi-experimental design with a pretest-posttest control group involving 60 high school teachers divided into experimental and control groups. Quantitative data were collected through standardized burnout inventories, and qualitative data were obtained through in-depth interviews. Results indicated that the experimental group, which practiced dzikir, experienced a significant reduction in emotional exhaustion and depersonalization, as well as improved personal accomplishment and job satisfaction compared to the control group. This study concludes that integrating spiritual-based stress management practices is highly effective in addressing burnout among educators, contributing to healthier teaching environments. By emphasizing culturally and spiritually relevant approaches, this research offers a practical solution for enhancing teacher well-being and supports the broader goals of improving educational quality in modern learning systems. These findings underline the importance of incorporating holistic, culturally sensitive interventions into education policy and teacher training programs.

A Compact X and Ku Band Quad Port MIMO Antenna with Path Loss aModel for Urban Macro and Micro Environments

The prospective of multiple-input multiple-output (MIMO) technology to increase network reliability and enhanced data throughput has made it essential in modern wireless communication systems. In this study, a new X and Ku band MIMO antenna with improved isolation is designed and analyzed using a shared radiator. To improve system performance, the MIMO antenna system was built to provide greater isolation between antenna elements by inserting inverted H-shape parasitic components within the radiators, resulting in an out-of-phase current of a comparable magnitude to the coupling current. The antenna achieves an isolation level greater than 15 dB across the operational frequency of 8.5-18.5 GHz with overall dimensions of 25 × 22 × 0.5 mm3 on RT duroid 5880 substrate. Furthermore, the developed MIMO antenna is investigated for radiation characteristics, with a maximum gain of 5.8 dB and acceptable diversity features. A path loss study for the designed antenna is performed using the ABG and CI models under the urban macro and micro model scenarios. The proposed shared radiator quad port MIMO antenna is optimized to realize compactness, wider impedance bandwidth, and inter-port isolation. The antenna design outcomes from simulation and experimental data make it a good contender for use in contemporary communication systems.

IoT-based Ubiquitous Healthcare System with Intelligent Approach to an Epidemic

Background:: The recent pandemic has shown its different shades across various solicitations, especially in the healthcare sector. It has a great impact on transforming the traditional healthcare architecture, which is based on the physical approaching model, into the modern or remote healthcare system. The remote healthcare approach is quite achievable now by utilizing multiple modern technological paradigms like AI, Cloud Computing, Feature Learning, the Internet of Things, etc. Accordingly, the pharmaceutical section is the most fascinating province to be inspected by medical experts in restoring the evolutionary healthcare approaches. COVID-19 has created chaos in the society for which many unexpected deaths occur due to delays in medication and the improper prognosis at an irreverent plan. As medical management applications have become ubiquitous in nature and technology-oriented, patient monitoring systems are getting more popular among medical actors. Method: The Internet of Things (IoT) has achieved the solution criteria for providing such a huge service across the globe at any time and in any place. A quite feasible and approachable framework has evolved through this work regarding hardware development and predictive patent analysis. The desired model illustrates various approaches to the development of a wearable sensor medium that will be directly attached to the body of the patients. These sensor mediums are mostly accountable for observing body parameters like blood pressure, heart rate, temperature, etc., and transmit these data to the cloud storage via various intermediate steps. The storage medium in the cloud will be storing the sensor-acquired data in a time-to-time manner for a detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Results and Conclusion: The model with the best accuracy will be treated as the resultant model among the numerous predictive models deployed in the cloud. During the hardware development process, several hardware modules are discussed. After receiving sensor-acquired data, it will be processed by the cloud's multiple machine-learning models. Finally, thorough analytics will be developed based on a meticulous examination of the patients' cardinal, essential, and fundamental data and communicated to the appropriate physicians for action. This model will then be used for the data dissemination procedure, in which an alarm message will be issued to the appropriate authorities.

66kv Modular Composite Material Emergency Repair Tower Simulation of Mechanical Performance and Experimental Study of Components

Abstract This paper details the design and performance evaluation of a 66kV modular insulated tower, aimed at enhancing the resilience and operational flexibility of power systems in extreme environments. The tower employs advanced composite materials, providing excellent mechanical strength. The design addresses various conditions, including line breaks, high wind speeds, and icing scenarios. Finite element analysis conducted using ANSYS software showed that the safety factor for the pole body is 2.14, indicating that the designed tower meets the safety factor requirements. Additionally, its excellent mechanical properties have been verified through mechanical property tests. The modular design facilitates rapid assembly and disassembly as needed across different contexts, significantly improving the efficiency and flexibility of emergency repair work, thus offering considerable benefits for power grid upgrades, maintenance, and disaster recovery. In summary, the 66kV modular insulated tower demonstrates its broad potential applications within modern power systems. The results of this study provide valuable references for power system design, especially in seeking more efficient and adaptable transmission repair solutions.

Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks

In the realm of modern communication systems, antennas are crucial components, with the microstrip patch antenna being particularly notable for its low profile and seamless integration. Despite its widespread use, designing this antenna involves complex simulations to optimize parameters, requiring significant expertise and consuming considerable time and energy. To streamline this process, machine learning (ML) algorithms are being utilized. This paper introduces an innovative approach that employs ML techniques to design a rectangular microstrip patch antenna operating within the sub-6 GHz frequency range (1-6 GHz) and the millimeter frequency range (28-40 GHz). The antenna design maintains consistent patch dimensions positioned strategically at the center, with a thorough examination of patch length and width to enhance performance. Datasets are meticulously prepared, covering output parameters such as beam area, directivity, gain, and radiation efficiency across the specified frequency ranges. By employing various ML algorithms, this study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. The K-nearest neighbor (KNN) algorithm achieved high accuracy across all parameters: gain at 94.23% under sub-6 GHz and 95.93% under millimeter frequency range, directivity at 99.02% and 98.59%, radiation efficiency at 93.94% and 94.28%, and beam area at 99.07% and 98.59% respectively. These results optimize microstrip antenna designs and enhance understanding of the relationship between design parameters and performance outcomes with ML.

Understanding hybridity governance in Africa: A theoretical framework for hybrid structures, policies, and practices

The amalgamation of traditional and modern governance systems represents a transformative advancement in contemporary African government. This study examines the concept of "hybridity governance" within the African context, providing a comprehensive theoretical framework that integrates modern institutional procedures with traditional governance systems. The paper examines the evolution of African governance from pre-colonial to post-colonial eras, illustrating the emergence of hybrid governance as a response to internal dynamics and external influences. It accomplishes this by utilising historical studies. The philosophical underpinnings are investigated, highlighting organisational and social concepts that elucidate the acceptance of hybrid models. The paper elucidates the applications, accomplishments, and challenges of hybrid government in several African nations through various empirical case studies. A critical examination examines the consequences of these governance paradigms for public administration in Africa. It underscores opportunities for innovation and enhanced flexibility, while also emphasising intrinsic challenges such as job ambiguity and conflict. The report concludes with forecasts regarding the future trajectory of hybrid governance in Africa, considering the interplay of local socio-political dynamics, global interconnectedness, and technological advancements. This article serves as a vital resource for policymakers, researchers, and stakeholders interested in the evolving governance paradigm in Africa, providing valuable applications and recommendations.

Long-term deep reinforcement learning for real-time economic generation control of cloud energy storage systems with varying structures

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures.

Secure Supply Chain Information Interchange using Distributed Trust Backbone

International trade requires transparent visibility of the goods transportation. High-quality data related to containers is essential for container movement across the border speed. However, customs and port authorities face information incorrectness and inconsistency, which are significant determinants that decrease the performance of container clearance in supply chain activities. The Seamless Integrated Data Pipeline principle has been proposed to overcome the mentioned data quality shortcomings and enhance supply chain visibility. Based on the Data Pipeline idea, we proposed the Distributed Trust Backbone (DTB) as a model of secure information exchange between parties within the supply chain activity. However, the supply chain data is highly dynamic. Access control on dynamic resources is the key to enabling secure data exchange and clear visibility. We take this challenge up in this paper. We propose an access control mechanism based on the supply chain Data Pipeline concept and apply it to the DTB model. The elaboration on the concrete detail of the system is presented in this paper. The prototype has been developed and performed in the simulation tests. It reduces 58% of requesting data for supply chain activities. The results of the experiments show that our proposed method performs 100% access control to data with BigO(1) accessing the Access Control List. It can ensure that the information for decision-making in the supply chain is of high quality. The supply chain visibility is clearer and speeds up a modern information exchange system of supply chains.