Efficient Data Processing Research Articles

Aqua-stream: an IoT based smart water management system for sustainable living

<p class="03IJISAEAbstract">Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.</p>

Imaging Enhancement of Gas Reservoir via Full-Waveform Inversion Imaging: Case Study, Gas Field

Abstract Seismic data are essential for analyzing and characterizing reservoir distributions in oil and gas exploration. While many advances have been made to obtain accurate seismic data, conventional methods still have limitations. The main limitations include the inability to use the full wavefield and the time-consuming sequential process of preprocessing, velocity model building, and migration. These challenges have highlighted the need for more efficient and accurate data processing. Full-waveform inversion (FWI) and FWI imaging have been developed to address these drawbacks. FWI uses the full wavefield, including diving and multi-scattered waves, to predict subsurface properties. It uses an optimization that iteratively updates the model to minimize the residual between the observed and the modeled data. Moreover, FWI imaging derives seismic images in a short time by taking the directional derivative of the FWI results. Compared with conventional methods, FWI imaging, since it is based on FWI, improves the signal-to-noise ratio and mitigates migration artifacts. In this study, a case study was conducted using 3D streamer data from a deepwater gas field to demonstrate the effectiveness of FWI imaging. As the gas field contains shallow gas, conventional methods have limitations in restoring subgas zones. To overcome this, FWI imaging was introduced, and to verify its effectiveness, reverse time migration (RTM) was used for comparison. After updating the velocity model with FWI up to 25 Hz, FWI imaging and RTM were applied using the same velocity model. Compared with RTM, FWI imaging exhibited significant improvements in resolution and structural detail at various depths. Additionally, it improved lateral resolution and structural continuity and effectively suppressed noise. Furthermore, FWI imaging significantly reduces processing time by eliminating the need for preprocessing and migration. These findings suggest that FWI imaging is a promising alternative to conventional methods by providing high-quality subsurface information while reducing the processing time.

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

Efficient Data Processing Pipeline for Event-Based Vision Datasets: Techniques and Insights

Abstract Event-based vision datasets have emerged as a critical asset in advancing the capabilities of real-time perception systems, particularly in fields such as robotics, autonomous vehicles, and human-computer interaction. These datasets enable low-latency processing by capturing asynchronous, pixel-level changes in the scene, providing a distinct advantage over traditional frame-based systems. However, the diverse formats and characteristics of event-based datasets pose significant challenges for efficient processing and analysis, hindering their broader adoption and integration. In this paper, we present a versatile and comprehensive data processing pipeline designed to address these challenges by supporting multiple event-data formats, including newer formats such as EVT2 and EVT3. This pipeline not only converts data into widely supported formats like AEDAT and NPZ, but also ensures that the unique characteristics of event-based data—such as temporal precision and sparse event representation—are preserved throughout the conversion process. By applying this pipeline to several open-source datasets, we establish a standardized, efficient methodology for dataset manipulation that enhances compatibility and reproducibility in event-based vision research. Additionally, we introduce a novel high-resolution event-based action dataset, converted into various formats using our pipeline, which opens new avenues for exploring event-based techniques in action recognition. This dataset and our pipeline serve as valuable resources for the research community, enabling advancements in real-time vision applications and fostering greater collaboration and standardization across studies.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR.

Developing remote patient monitoring infrastructure using commercially available cloud platforms

Wearable sensor devices for continuous patient monitoring produce a large volume of data, necessitating scalable infrastructures for efficient data processing, management and security, especially concerning Patient Health Information (PHI). Adherence to the Health Insurance Portability and Accountability Act (HIPAA), a legislation that mandates developers and healthcare providers to uphold a set of standards for safeguarding patients’ health information and privacy, further complicates the development of remote patient monitoring within healthcare ecosystems. This paper presents an Internet of Things (IoT) architecture designed for the healthcare sector, utilizing commercial cloud platforms like Microsoft Azure and Amazon Web Services (AWS) to develop HIPAA-compliant health monitoring systems. By leveraging cloud functionalities such as scalability, security, and load balancing, the architecture simplifies the creation of infrastructures adhering to HIPAA standards. The study includes a cost analysis of Azure and AWS infrastructures and evaluates data processing speeds and database query latencies, offering insights into their performance for healthcare applications.

Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System

Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems’ ability to process information and learn locally on the device. Local processing and learning of time-series information typically demand intensive computations and massive storage as the process involves retrieving information and tuning hundreds of parameters back in time. In this work, we developed a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. The experimental results illustrate that the hardware model experiences a marginal degradation in performance as compared to the software counterpart. This is mainly attributed to the limited precision and dynamic range of network parameters when emulated using memristor devices. The proposed system is evaluated for lifespan, robustness, and energy-delay product. It is observed that the system demonstrates reasonable robustness for device failure below 10%, which may occur due to stuck-at faults. Furthermore, 247 × reduction in energy consumption is achieved when compared to a custom CMOS digital design implemented at the same technology node.

Proton‐Modulated Resistive Switching in a Synapse‐Like Tyrosine‐Rich Peptide‐Based Memristor

AbstractArtificial intelligence has become an essential part of the daily lives and has revolutionized various sectors, including healthcare, finance, transportation, and entertainment. With a substantial increase in processed data, neuromorphic devices that replicate the operation of the human brain have been emphasized owing to their superior efficiency. Typical neuromorphic devices focus on constructing synapse‐like structures. However, biological synapses have more complex mechanisms for efficient data processing. One of the most prominent mechanisms is proton activation, which forms an ion concentration gradient prior to the transmission of neurotransmitters and plays a key role in efficient computation. In this study, proton‐mediated signaling at biological synapses is successfully replicated by fabricating a proton‐modulated memristor device using a tyrosine‐rich peptide film. The ionic input of the memristor is controlled by applying a voltage to proton‐permeable PdHx contacts in a hydrogen atmosphere, thus successfully adjusting the resistive switching behavior. Remarkable improvements in resistive switching and computing performance are observed through proton injection, analogous to “proton‐mediated signaling” at the actual synapse. It is believed that this study proposes a new paradigm for designing biorealistic devices and provides inspiration for precisely controllable ion‐based neuromorphic devices.

Investigating the Safety Profile of Fast-Track COVID-19 Drugs Using the FDA Adverse Event Reporting System Database: A Comparative Observational Study.

The US Food and Drug Administration (US FDA) granted emergency use authorization (EUA) for multiple coronavirus disease 2019 (COVID-19) drugs as a medical countermeasure during the COVID-19 pandemic. Despite these drugs' fast-track nature, concerns persist regarding their efficacy and potential adverse effects. Thus, the continuous surveillance and understanding of these drugs' safety profiles are crucial in such scenarios. Using the FDA Adverse Event Reporting System (FAERS) database, we aimed to compare the adverse drug reactions (ADRs) of four fast-track COVID-19 drugs to explore the potential of real-world data for providing prompt feedback in clinical settings. To evaluate the post-marketing safety of fast-track COVID-19 drugs, we descriptively evaluated the ADRs of four COVID-19 drugs (bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir) using FAERS data reported from January 2020 to June 2022. We examined FAERS case records of COVID-19 drugs reported as the "primary suspect drug" as a case group and the records of other drugs as the control. "Serious adverse drug reactions (SADRs)" were defined based on FDA guidelines. Using reporting odds ratios, disproportionality analysis was conducted to determine significant signals for ADRs related to each of the four drugs compared with those of others, both at the preferred term (PT) and system organ class (SOC) levels. To explore the occurrence of reporting each serious outcome reported to the four drugs, we fitted logistic regression models, adjusting for age and sex. During the study period, 5 248 221 cases were submitted to FAERS, including 17 275 cases of the four COVID-19 drugs: bebtelovimab (532 cases), molnupiravir (1106 cases), nirmatrelvir/ritonavir (9217 cases), and remdesivir (6420 cases). A total of 64, 46, 116, and 207 PTs with significant disproportionality were identified for each drug, respectively. "Infusion-related reaction" (18.4%), "diarrhea" (7.4%), "dysgeusia" (11.4%), and "increased alanine aminotransferase" (14.5%) were the most frequently reported SADRs for bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir, respectively. Among the 27 SOCs, statistically significant signals were observed in 10, 3, 0, and 8 SOCs for bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir, respectively. Remdesivir showed a higher occurrence for the reporting of death or life-threatening ADRs compared with the control (adjusted odds ratio (OR) = 2.44, 95% confidence interval (CI) = 2.23-2.59; adjusted OR = 1.82, 95% CI = 1.64-2.02, respectively). We identified potential ADRs associated with COVID-19 drugs and provided insights into their real-world safety. This study demonstrated that real-world data and real-time safety reviews could be effective methods for the timely detection of ADR signals of drugs that have received fast-track approval, as exemplified by COVID-19 drugs. These findings underscore the importance of the continued surveillance, efficient data processing, and establishment of automated pipelines for real-time safety reviews.

Automated identification of soil functional components based on NanoSIMS data

NanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic matter (OM) and mineral phases in the heterogeneous soil microstructure. Investigating the spatial arrangement of distinct organic and mineral functional components is necessary to understand how these components interact and contribute to biogeochemical processes in soil systems. Identifying soil functional components within NanoSIMS measurements necessitates advanced and efficient data processing tools capable of accessibility and automation. We have developed a pre-processing tool to streamline NanoSIMS data preparation and handling. The tool is provided as an open-source software toolbox (NanoT). In addition, a two-step unsupervised segmentation method was developed to identify soil functional components based on NanoSIMS analyses. To illustrate the segmentation method, here we describe its application to two exemplary NanoSIMS measurements. This allows to distinguish mineral- and OM-dominated regions, as well as different mineral phases. To improve the detection of iron oxides and aluminosilicates, the 56Fe16O− channel was separately processed. The presented NanoSIMS-based processing workflow helps to disentangle functional components within a biogeochemically-diverse microstructure in soils and further warrants applications to a wide range of complex environmental samples.

Detection of microplastics in fish using computed tomography and deep learning

Marine organisms have been observed ingesting microplastic particles, with field analyses indicating fibers and fragments as prevalent forms. Current microplastic detection methods are mainly time-consuming, susceptible to cross-contamination, and expensive. Furthermore, these techniques, being disruptive, do not allow for the exact localization of the microplastic in the sample. This study proposes a new approach using Computed Tomography (CT scan) and Artificial Intelligence for the automatic and non-destructive detection of microplastics in fishes and other species based on the combination of several factors, such as density and shape. The advantages of this methodology include accurate identification of plastic localization, a low risk of cross-contamination, rapid processing, automatic tomographic measurement, efficient data processing, cost-effectiveness, and a high cost-benefit ratio. The herein results highlight how artificial intelligence applied to conventional techniques can significantly improve precision and efficiency in microplastic research. Indeed, the semantic segmentation model clearly recognized the presence of 100 % of the plastic particles, both in their location and in their volume, accelerating the identification process and surpassing the limitations of traditional spectral analysis methodologies.

Rancang Bangun Sistem Administrasi Tata Usaha Barang Milik Negara Di Lembaga Layanan Pendidikan Tinggi Wilayah Iii Jakarta Berbasis Website

Current information technology shows rapid growth and requires devices such as computers orapplications to manage data and information to ensure efficient data processing. Informationsystems are crucial in operating institutional activities such as operational control, strategicplanning, and performing other managerial functions (Zulkarnain et al., 2020). The HigherEducation Service Institution Region III is a government agency in Jakarta aimed at improvinghigher education standards under the Ministry of Education, Culture, Research, and Technology.The State-Owned Goods Administration is a department or work group within the HigherEducation Service Institution Region III Jakarta that is fully responsible for all operationalactivities within the Higher Education Service Institution Region III. Operational activities in theState-Owned Goods Administration of the Higher Education Service Institution Region IIIJakarta are still carried out manually without an application system to record all operationalactivities. Currently, the State-Owned Goods Administration of the Higher Education ServiceInstitution Region III is striving for digitalization through the SATU project, which involvesdesigning an application to support operational activities. However, the current design has notyet fully met the operational needs of the State-Owned Goods Administration, thus requiring refinement in the form of the design and development of the State-Owned Goods AdministrationSystem for the Higher Education Service Institution Region III Jakarta

Rapid advancements in large language models for quantitative remote sensing: The case of water depth inversion

This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Multi-function vortex array radar

In the realm of automation systems, multi-function radars serve as essential sensory components for self-driving vehicles and airbornes. Effective resource allocation management is crucial, requiring a high level of versatility to accomplish multiple tasks, especially, for increasingly miniaturized hardware. Here, we advance a balanced protocol for detecting, positioning, and tracking moving targets in real-time. Our protocol integrates efficient data processing methods with robust hardware. Specifically, detection signals are modulated by optical vortices for imaging, and real time processing of the image field facilitates target positioning and tracking. Moreover, the protocol extends its utility to serve as a topographic laser profiling system for natural landscapes, highlighting its adaptability. This adaptability and versatility well position the proposed protocol to support a wide range of applications, spanning self-driving vehicles and aerial systems, underscoring its potential significance across multiple platforms.

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient data processing capabilities is paramount. The unstructured environments encountered by robots, coupled with complex missions assigned to them, present numerous challenges necessitating diverse visual functionalities, and consequently, the development of multifunctional robotic vision systems has become indispensable. Meanwhile, rich diversity inherent in animal vision systems, honed over evolutionary epochs to meet their survival demands across varied habitats, serves as a profound source of inspirations. Here, recent advancements in multifunctional robotic vision systems drawing inspiration from natural ocular structures and their visual perception mechanisms are delineated. First, unique imaging functionalities of natural eyes across terrestrial, aerial, and aquatic habitats and visual signal processing mechanism of humans are explored. Then, designs and functionalities of bio-inspired electronic eyes are explored, engineered to mimic key components and underlying optical principles of natural eyes. Furthermore, neuromorphic image sensors are discussed, emulating functional properties of synapses, neurons, and retinas and thereby enhancing accuracy and efficiency of robotic vision tasks. Next, integration examples of electronic eyes with mobile robotic/biological systems are introduced. Finally, a forward-looking outlook on the development of bio-inspired electronic eyes and neuromorphic image sensors is provided.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.