Network System Research Articles

Study on the sports biomechanics prediction, sport biofluids and assessment of college students’ mental health status transport based on artificial neural network and expert system

Based on the theories of artificial neural networks and expert systems, this study constructs a model for assessing the sports biomechanics’ mental health of college students using artificial neural networks and expert systems. It assesses the sports biomechanics’ mental health status of college students who had their yoga classes suspended during the COVID-19 pandemic. Meanwhile, A mobile questionnaire was used to collect information on students’ personal circumstances, sport biofluids and yoga exercise routines, as well as data from the Depression Anxiety Stress Scales (DASS-21). This study presents a novel approach to assessing the intersection of sports biomechanics and mental health by employing Artificial Neural Networks (ANNs) and expert systems. Unlike previous research in this domain, this study offers an extensive review of the literature, highlighting both the distinctive contributions of ANNs and expert systems and the existing gaps in current methodologies. Similarly, a univariate analysis method was utilized to quantitatively assess the impact of yoga interventions and other factors on college students’ sports biomechanics and mental health. Building on this analysis, an artificial neural network (ANN) model was developed to predict mental health outcomes and sport biofluid conditions. The model focused on evaluating the significance of various variables, with particular attention to the contribution of yoga exercise routines. In short, this approach aims to enhance the understanding and support for utilizing yoga interventions to improve college students’ mental health within the context of sports biomechanics, especially in the post-pandemic era. The findings should make an important contribution to the field of integrating ANNs with expert systems and sports biomechanics improves mental health prediction accuracy.

Fiber‐Integrated Diamond Quantum Sensor for High‐Voltage Current Measurements

AbstractIn power network systems, there is an urgent demand for highly accurate and miniaturized sensors, owing to their high safety level and limited installation space. Current sensors in high‐voltage grids are required to accommodate harsh environments and provide accurate measurements of several kiloamperes. Thus, this study proposed an integrated quantum diamond sensor to facilitate high‐accuracy, large‐dynamic‐range current measurements. The design incorporated optical fiber and directional microwave (MW) antennas to drive the diamond sensor, which significantly reduced the size and power consumption on the high‐voltage side. Remote‐control and demodulation systems are installed more than 10 m away from the low‐voltage side. The proposed approach achieved zero power consumption on the high‐voltage side and ensured efficient signal transmission. A passive diamond probe manufactured using microfabrication processes facilitated miniaturization and practical deployment. Through parameter optimization, a magnetic detection sensitivity of 4.86 nT·Hz−1/2 is achieved at a safe distance of 11 m, which can be further optimized to 0.77 nT·Hz−1/2 with enhanced MW power. This sensor achieved a current measurement error of ±0.4% in the 1000 A measurement range. Thus, this study provides a new solution for the application of diamond quantum sensors in power systems.

Large Language Models Meet Next-Generation Networking Technologies: A Review

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

Analytical Neural Network System for the Helicopter Turboshaft Engines Operating Modes Classification

A novel neural network-based analytical system has been developed for classifying helicopter turboshaft engine operating modes during flight operations. This system utilizes a neural network architecture comprising an input layer with three neurons, two fully connected hidden layers, and an output layer with two neurons. The proposed approach demonstrates an exceptional recognition accuracy of 0.997 (99.7%) across steady-state, unsteady, and transient operating modes of helicopter turboshaft engines. A new method for training this neural network has been introduced, employing forward propagation, loss calculation, backpropagation, and weight updates, enhanced by an adaptive learning rate and the cross-entropy function as the loss criterion. The method also incorporates a novel modified Smooth ReLU activation function for hidden layer neurons. This innovation led to a near-perfect accuracy in network training and reduced the loss to 0.025 (2.5%), highlighting the high quality and reliability of the neural network in classifying engine operating modes during flight. Furthermore, it has been empirically shown that the application of this neural network significantly reduces type I errors by a factor of 2.09 to 2.14 and type II errors by 2.05 to 2.21 times compared to traditional classifiers based on ART-1 and BAM networks. This advancement marks a substantial improvement in classification accuracy and error minimization for helicopter turboshaft engine operating modes.

Networked Control of a Small Drone Resilient to Cyber Attacks

With increasing advances in networked systems and networked control systems in everyday life, the problem of cybersecurity becomes crucial. Moreover, in some applications like small UAVs, the safety and integrity of the system and its surroundings are highly susceptible to cyberattacks. In this context, the current paper proposes a resilient networked control approach. The control structure is split into an inner and an outer loop. The outer position control loop uses measurements from motion cameras connected to a remote computer, while the commands are sent through the network. We consider the resilience problem for two types of cyberattacks: denial of service (DoS), emulated as an increase in the network transmission delay, and man in the middle (MitM), emulated as additive input disturbances. The mitigation for the DoS attack is performed through the help of a reference governor (RG), which uses the delay estimates and the system’s model to predict future safety violations and adapts the reference accordingly. The MitM attack is mitigated by an unknown input disturbance observer (UIDO) together with a RG. Experimental results on a Parrot Mambo drone show that both types of attacks are rejected successfully, ensuring a safe and stable flight.

AI-based modeling studies for dye removal using mixed biomass composites from algae and plant seeds: Isotherm, kinetics, and mechanistic insights

Biomass based remediation have emerged as a promising solution for the economic and sustainable pollutant removal from environment. In this study, novel mixed biomass derived from algae and manila tamarind seeds was synthesized and extensively characterized for application in dye adsorption. Mesoporous structure with pore diameter of 4.006 nm was observed from Brunauer-Emmett-Teller (BET) characterization. Best fit for isotherm was identified with Langmuir model indicating the monolayer nature of dye removal. Artificial intelligence (AI) based models – artificial neural network (ANN) and adaptive neuro fuzzy interference (ANFIS) system were employed for the prediction of dye removal under different conditions, a unique approach which has not been explored in previous studies based on mixed biomass adsorption. In addition, embedding particle swarm optimization (PSO) had a significant improvement in prediction of dye removal. For both Eriochrome black (EB) dye and Brilliant Orange (BO) dye adsorption employing mixed biomass, the ANN-PSO model yielded the best correlation of 0.9999 and 0.9994. For eriochrome black and brilliant orange dye, the maximum adsorption of 79.95 mg/g and 102.4 mg/g for the mixed biomass composite was noted, highlighting the adsorption efficiency of biomass. The uniqueness of the study lies in the integration of mixed biomass adsorption with AI driven modeling, providing a dual approach to enhance dye removal and adsorption efficiency. In addition, the regenerated mixed biomass can be reused for five consecutive cycles, highlighting its practical application, distinguishing from existing research that focused on single use biomass systems.

Event-triggered and quantised feedback stabilisation of switched networked control systems

In order to conserve network resources, this paper investigates the event-triggered control and quantised feedback stabilisation of switched networked control systems by introducing the vector quantizer technique. The state information undergoes quantisation and is transmitted to the controller after being sampled by the event-triggering technology. The designed event-triggered mechanism relies on quantised state values, and the generation of each quantised value also depends on the event-triggered condition. Addressing the asynchronous issue between the controller and the subsystem, we provide a sufficient condition for ensuring exponential stability of the closed-loop system and outline the controller gain design method by employing the multiple Lyapunov function and average dwell time methods. Besides, we demonstrate the explicit exclusion of the Zeno phenomenon in the event-triggered mechanism. Finally, the viability of our approach is demonstrated through simulation examples.

Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve ablation.

To build, train, and assess the artificial neural network (ANN) system in estimatingthe residual valve rate after endoscopic valve ablationand compare the data obtained with conventional analysis. In a retrospective cross-sectional study between June 2010 and December 2020, 144 children with a history of posteriorurethral valve (PUV) who underwent endoscopic valve ablation were enrolled in the study. MATLAB software was used to design and train the network in a feed-forward backpropagationerror adjustment scheme. Preoperative and postoperative data from 101 patients (70%) (training set) were utilized to assessthe impact and relative significance of the necessity for repeatedablation. The validated suitably trained ANN was used to predict repeated ablation in the next 33 patients (22.9%) (test set)whose preoperative data were serially input into the system. To assess system accuracy in forecasting the requirement for repeatablation, projected values were compared to actual outcomes. The likelihood of predicting the residual valve was calculatedusing a three-layered backpropagating deep ANN using preoperativeand postoperative information. Of 144 operated cases, 33 (22.9%) had residual valvesand needs to repeated ablation. The ANN accuracy, sensitivity, and specificity for predicting the residual valve were 90.75%,92.73%, and 73.19%, respectively. Younger age at surgery, hyperechogenicity of the renal parenchyma, presence of vesicoureteralreflux (VUR), and grade of reflux before surgery were among the most significant characteristics that affectedpostoperative outcome variables, the need for repeated ablation, and were given the highest relative weight by the ANN system. Conclusions: The ANN is an integrated data-gathering tool for analyzing and finding relationships among variables as a complex non-linear statistical model. The results indicate that ANN is a valuable tool for outcome prediction of the residualvalveafter endoscopic valve ablation in patients with PUV.

BIDS: An efficient Intrusion Detection System for in-vehicle networks using a two-stage Binarised Neural Network on low-cost FPGA

Automotive networks are crucial for ensuring safety as the number of Electronic Control Units (ECUs) grows to support vehicle intelligence. The Controller Area Network (CAN) is commonly used for efficient in-vehicle communication among ECUs. However, its broadcast nature and lack of a dedicated security layer make it vulnerable to attacks. This paper proposes a novel CAN bus Intrusion Detection System (IDS), named BNN-based IDS (BIDS), which efficiently provides both unknown attack detection and known attack classification using a hierarchical two-stage Binarised Neural Network (BNN) and Generative Adversarial Network (GAN). BIDS was validated on three datasets, and its implementation achieves an average inference time of less than 0.170 ms with minimal resource utilisation on a low-cost Field Programmable Gate Array (FPGA). This rapid inference speed enables real-time inference on individual CAN messages using a sliding window technique, eliminating the need to wait for multiple accumulated CAN messages required for data preprocessing. Evaluation metrics demonstrate that our IDS achieves high accuracy in both identifying unseen attacks and categorising known attacks. Furthermore, our FPGA implementation consumes merely 2.09 W, which is a 57% reduction compared to a cutting-edge FPGA-based IDS that is capable of detecting unknown attacks using the same dataset.

Online Monitoring System of Electromechanical Transient Simulation Data of Distribution Network Based on Edge Computing

The current distribution network is developing toward the direction of information and intelligent distribution Internet of Things (IoT). In order to explore the online monitoring system for transient electromechanical simulation of distribution networks, this paper is based on the equivalent model of generator sets. Firstly, it describes the relevant theoretical knowledge of edge computing, designs the distribution IoT based on edge computing, and briefly introduces its network architecture. Secondly, based on the discrete-time domain equivalent model of generators, the electromechanical transient simulation distribution network is constructed by introducing the machine network division of the power network. Finally, the Western System Coordinating Council (WSCC)-3 units and 9 nodes are taken as an example, and simulation experiments are conducted under two fault simulation conditions to verify the effectiveness of the simulation model. The results show that under the two fault simulation conditions, the results of equivalent model simulation of generator terminal voltage and relative power angle change are like those of the Power System Analysis Software Package (PSASP). The changing trend of the two is similar and stable. After the PSASP results are stabilized at a value, the simulation results fluctuate extremely up and down the value. For example, under fault condition 1, the changing trend of the relative power angle of the No. 2 generator is first fluctuating and then becomes stable. After violent fluctuation, the relative power angle tends to be stable from about 20s to -12.35°. The result of PSASP software is stable at -12.35°. The transient electromechanical simulation of the generator equivalent model can provide some ideas for the online monitoring system of the distribution network.

Dynamic Event-Triggered Control for Sensor–Controller–Actuator Networked Control Systems

We consider the problem of output feedback stabilization of LTI systems under event-triggering implementation. In particular, we assume that both the plant output and the control input are both transmitted over the network in an asynchronous manner. To that end, two independent event-triggering rules are constructed to generate the transmission instants of the submitted signals. The proposed approach is dynamic in the sense that the triggering rules involve internal dynamical variables to allow for further reduction in the communication load. Moreover, the inter-transmission times for both sides of the channel are lower bound by enforced dwell times to prevent the occurrence of Zeno phenomena. The problem is challenging due to mutual interactions between the sampling errors of the plant output and the control input, which requires careful handling to ensure closed-loop stability. The triggering mechanisms are designed by emulation as we first ignore the effect of the network and stabilize the plant in continuous-time. Then, the communication constraints are taken into account to derive the triggering conditions such that the stability of the networked control system is preserved. The required conditions are formulated in terms of a linear matrix inequality. The effectiveness of the technique is demonstrated by numerical simulations.

NeSt: A QoS differentiating end-to-end networked storage simulator

The emerging high-speed storage technologies increasingly use Nonvolatile Memory Express (NVMe) protocol to meet their high throughput and low latency needs. In a datacenter environment, applications accessing multiple such devices over the fabric (i.e. the network) tend to have Quality of Service (QoS) requirements pertaining to offered throughput and experienced latency. In this paper we describe a networked storage system simulator called NeSt that supports end-to-end (E2E) QoS differentiation across multiple classes of service. This is done by conveying the class designation end to end and using it to consistently but independently apply the differentiation in each segment of the path. We demonstrate the ability of NeSt to provide end-to-end QoS differentiation under a variety of situations. To the best of our knowledge, NeSt is the first simulator of networked storage (consisting of multiple NVMe SSDs) that supports E2E QoS differentiation.

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach

PenGym: Realistic training environment for reinforcement learning pentesting agents

Penetration testing, or pentesting, refers to assessing network system security by trying to identify and exploit any existing vulnerabilities. Reinforcement Learning (RL) has recently become an effective method for creating autonomous pentesting agents. However, RL agents are typically trained in a simulated network environment. This can be challenging when deploying them in a real network infrastructure due to the lack of realism of the simulation-trained agents.In this paper, we present PenGym, a framework for training pentesting RL agents in realistic network environments. The most significant features of PenGym are its support for real pentesting actions, full automation of the network environment creation, and good execution performance. The results of our experiments demonstrated the advantages and effectiveness of using PenGym as a realistic training environment in comparison with a simulation approach (NASim). For the largest scenario, agents trained in the original NASim environment behaved poorly when tested in a real environment, having a high failure rate. In contrast, agents trained in PenGym successfully reached the pentesting goal in all our trials. Even after fixing logical modeling issues in simulation to create the revised version NASim(rev.), experiment results with the largest scenario indicated that agents trained in PenGym slightly outperformed, and were more stable, than those trained in NASim(rev.). Thus, the average number of steps required to reach the pentesting goal was 1.4 to 8 steps better for PenGym. Consequently, PenGym provides a reliable and realistic training environment for pentesting RL agents, eliminating the need to model agent actions via simulation.

Network Data Intrusion Detection and Data Feature Extraction of Electromechanical Facilities from Machine Learning

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset’s detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

Adaptive control of networked control systems under DoS attacks

This research introduces a new adaptive control approach tailored for networked control systems, aimed at addressing the challenges of Denial of Service (DoS) attacks and round-trip time delays. Initially, a compensation mechanism based on estimated values is introduced to counter DoS attacks; subsequently, for round-trip time delay issues, a predictive delay compensation control strategy is designed. The proposed control strategy does not rely on the specific structure or a particular model of the system, but instead solely updates the controller using the system's input and output values. Moreover, we successfully demonstrate the bounded stability of the system using the direct Lyapunov method. Ultimately, the effectiveness and practicality of this method are confirmed through simulation results.

Seroconversion Toxoplasmosis as a predictor for gestational diabetes mellitus among pregnant ladies in Iraq

Background. Toxoplasma gondii (T.g.) can reach many host organs including the pancreas and pancreatic tissue may be directly attacked and compromised by the organisms. Aim. This study aimed to evaluate the risk of toxoplasmosis infestation as a predictor of gestational diabetes mellitus (GDM) among pregnant ladies in Thi-Qar, Southern Iraq. Methodology. A case-control study was conducted on 151 pregnant ladies classified (61) with GDM and (89) healthy pregnant ladies attending a tertiary Endocrine Center. All patient data were gathered from direct interviews and the digital records of the tertiary center, which used an internal network system and Microsoft Access program. Each pregnant woman was diagnosed with new GDM according to the American Diabetes Association (ADA) definition criteria. Results. The mean ages of the whole participants were 28 ±6 years old and more than half of them were in the second decade of life, their mean weight was 75.38±13.29) kg and their body mass index was 29.82 ± 5.23 (Kg/m2). Family history of DM, history of toxoplasmosis infestation, and positive serological test of IgG for toxoplasmosis was significantly predominant among women with GDM rather than those without (p-value= 0.007, 0.002, 0.004 respectively). Conclusions. The second decade of age is the independent risk factor for developing new GDM, while toxoplasmosis infestation and positive IGG were additive-dependent risk factors for developing new GDM in collaboration with other patients' profiles. Pregnant women with GDM were significantly older, heavier in body weight, and obese than those without GDM making them at higher risk factors for getting hyperglycemia.

Quantifying the Ecological Performance of Migratory Bird Conservation: Evidence from Poyang Lake Wetlands in China

Protected areas are essential for the conservation of biodiversity. However, the rapid expansion of urbanization and the intensification of human activities have significantly disrupted environmental integrity, leading to a continuous deterioration in both the quantity and quality of large ecological patches. This has further diminished the connectivity among ecological patches, leading to significant consequences for regional biodiversity conservation. Taking Poyang Lake as a case study, which serves as a crucial wintering habitat for migratory birds along the East Asia–Australasia flyway, this research employs ArcMap technology. It considers various factors including land use type, slope, and elevation to evaluate habitat quality and degradation through the application of the InVEST model. Additionally, the study utilizes the minimum cumulative resistance (MCR) model alongside circuit theory to delineate ecological corridors within the area and to establish a comprehensive ecological network system. The research results in this paper are as follows. (i) During the period from 2000 to 2020, there was an overall decline in habitat quality within the study area, indicating a clear trend of habitat degradation. However, it is worth noting that there was an increase in habitat quality in certain local areas within the protected area. (ii) The ecological resistance values in the core area of the migratory bird reserve in Poyang Lake are generally low. However, the ecological resistance values of the habitats have shown a consistent increase from 2000 to 2020. Additionally, there has been a significant decrease in the density of ecological corridors during this time period. (iii) Over the period from 2000 to 2020, both the number and connectivity of ecological corridors decreased and their integrity and functionality degraded. Consequently, this weakened role of the ecological network has had implications for maintaining regional biodiversity and ecosystem service functions. The findings indicate two conclusions. (i) Ecological connectivity is essential for the conservation of migratory bird habitats. Strengthening control measures aimed at expanding ecological corridors can effectively safeguard flagship and umbrella species, thereby promoting biodiversity conservation. (ii) The establishment of ecological corridors can help reconcile conflicts between conservation efforts and development objectives. This reconciliation carries significant theoretical implications for fostering a harmonious coexistence between humans and birds in Poyang Lake’s migratory bird sanctuary.

Association of HRS-AKI with Mortality in Patients with Cirrhosis Requiring Renal Replacement Therapy: Results from the HRS-HARMONY Consortium.

While AKI requiring renal replacement therapy (AKI-RRT) is associated with increased mortality in heterogeneous inpatient populations, the epidemiology of AKI-RRT in hospitalized patients with cirrhosis is not fully known. Herein, we evaluated the association of etiology of AKI with mortality in hospitalized patients with cirrhosis and AKI-RRT in a multicentric contemporary cohort. This is a multicenter retrospective cohort study using data from the HRS-HARMONY consortium, which included 11 U.S. hospital network systems. Consecutive adult patients admitted in 2019 with cirrhosis and AKI-RRT were included. The primary outcome was 90-day mortality, and the main independent variable was AKI etiology, classified as hepatorenal syndrome (HRS-AKI) vs. other (non-HRS-AKI). AKI etiology was determined by at least two independent adjudicators. We performed Fine and Gray sub-distribution hazard analyses adjusting for relevant clinical variables. Of 2,063 hospitalized patients with cirrhosis and AKI, 374 (18.1%) had AKI-RRT. Among these, 65 (17.4%) had HRS-AKI and 309 (82.6%) non-HRS-AKI, which included ATN in most cases (62.6%). Continuous RRT (CRRT) was used as the initial modality in 264 (71%) of patients, while intermittent hemodialysis (IHD) was utilized in 108 (29%). The HRS-AKI (vs. non-HRS-AKI) group received more vasoconstrictors for HRS management (81.5% vs. 67.9%), while the non-HRS-AKI group received more mechanical ventilation (64.3% vs. 50.8%) and more CRRT (vs. IHD) as the initial RRT modality (73.9% vs. 56.9%). In the adjusted model, HRS-AKI (vs. non-HRS-AKI) was not independently associated with increased 90-day mortality (sHR=1.36, 95% CI: 0.95-1.94). In this multicenter contemporary cohort of hospitalized adult patients with cirrhosis and AKI-RRT, HRS-AKI was not independently associated with an increased risk of 90-day mortality when compared to other AKI etiologies. The etiology of AKI appears less relevant than previously considered when evaluating the prognosis of hospitalized adult patients with cirrhosis and AKI requiring RRT.

INNOVATIVE STRATEGIES FOR MANAGING HYDROMETEOROLOGICAL DISASTER RISKS: OPTIMIZING ENVIRONMENTALLY FRIENDLY DRAINAGE SYSTEMS (ECO-DRAINAGE) TO PREVENT FLOODING AND OVERFLOW IN CLIMATE CHANGE ADAPTATION IN KARAWANG REGENCY

Urban drainage is a network system for waste disposal in urban areas that functions to manage or control surface water, so that it does not disrupt or harm the community and provides benefits for human activities. This research aims to create synergy and steps for the implementation of eco-drainage in the urban characteristic conditions of Karawang Regency. The method used involves data collection through observation, interviews, historical data analysis, and mapping based on SNI Number 02-2406-1991 regarding the General Planning Procedures for Urban Drainage. The research results indicate that the Eco drain infrastructure consists of 7 elements, namely rainwater harvesting, infiltration wells, retention ponds, trash filters, bioremediation, biofilters, and water quality treatment using constructed wetlands. Meanwhile, the local government of Karawang Regency does not yet have the infrastructure to implement eco-drainage. After conducting an analysis, it was determined that among the 7 elements of eco-drainage, rainwater harvesting is the most needed to address the issues of flooding and drought in the Karawang area. The research results also include calculations and explanations regarding the Eco drain facilities for rainwater collection on a land scale and environmental/community storage tanks, as well as the quality of rainwater in Indonesia.