Software Design Research Articles

An original medical logistics robot system design for the complex environment of hospitals with dynamic obstacles

Purpose COVID-19 has aggregated the need for a non-contact medical logistic system. A non-contact robot with self-navigation ability has greatly enhanced the efficiency of the medical logistic system. This paper aims to design a new medical logistics robot system for the complex environment of hospitals with dynamic obstacles. Design/methodology/approach Targeting a medical logistics robot system for a large-scale hospital environment, this study proposed a dynamic obstacle avoidance system to reduce the robot’s delay time as well as frequent route switching. In the algorithm, this study proposed a new loop closure detection with an artificial correction factor. Moreover, this study presented enhanced 3D object detection, improving detection accuracy in hospital environments. Findings Experimental results confirm that the robot can move along its global path and reach its destination without colliding with stationary or moving obstacles. Originality/value The medical logistics robot system has safe and stable performance in real hospital scenarios. The implementation verifies that the robot has effectiveness and reliability in both hardware and software design.

The life cycle of Schistosoma mansoni and schistosomiasis disease: an educational approach

Schistosoma mansoni, a digenetic trematode, is the causative agent of intestinal schistosomiasis, an infectious parasitic disease that affects approximately 54 million people annually and has a high mortality rate. Disease control involves treatment with the anthelmintic praziquantel, environmental sanitation, access to treated water, and population management of Biomphalaria snails, the intermediate hosts of the parasite. Understanding the life cycle of S. mansoni is an educational challenge due to the complexity of its multiple biological and ecological stages, requiring knowledge in parasitology, molecular biology, ecology, and public health. This difficulty affects students in both basic and higher education. In this context, the creation of illustrative diagrams and educational videos of the parasite’s life cycle proves to be an effective approach. Visual representations facilitate the understanding of the life cycle stages and their relationship to the disease, promoting more accessible theoretical and practical learning. Furthermore, these materials strengthen schistosomiasis control actions, especially in endemic regions, and contribute to the training of public health professionals. This study, therefore, aims to present an illustrative diagram and an educational video that address the different stages of S. mansoni’s life cycle. For the illustrations and graphic element assemblies, digital software and platforms such as Inkscape, Microsoft PowerPoint, Canva, and the GNU Image Manipulation Program were used. It is concluded that the use of graphic design software to create high-quality visual materials contributes to improving education and knowledge dissemination.

Utilizing Platinum(II)-Based Cross-Linker and Two-Stage Data Analysis Strategy to Investigate the Allosteric in Glycogen Phosphorylase.

Cross-linking mass spectrometry (XL-MS) is widely used in the analysis of protein structure and protein-protein interactions (PPIs). Throughout the entire workflow, the utilization of cross-linkers and the interpretation of cross-linking data are the core steps. Cisplatin, as a well-known anticancer drug, has been previously demonstrated for its capability and advantages as a cross-linker. However, the complexity of platinum(II) cross-linked products and the lack of suitable software for data interpretation have hindered its further application. In this work, a two-stage data analysis strategy for platinum(II) cross-linked peptides has been developed and demonstrated on a pair of phosphorylation-induced allosteric systems, glycogen phosphorylase (GP) b and a. This two-stage data analysis strategy takes into account the identification of various types of Pt(II)-containing fragment ions and incorporates the unique isotope distribution properties of Pt(II)-based cross-linkers to eliminate false-positive data and achieve accurate identification of Pt(II)-based cross-linked peptides. The Pt(II)-based cross-linking results allow the capture of structural differences between GPb and GPa at the N-terminus and the tower-tower helices interface, which is consistent with the X-ray crystallography structure as well as our previous HDX-MS results. In addition, it also complements the structure of noncrystallizable regions. Finally, through discussion of existing data search engines and issues in spectral analysis of Pt(II)-based cross-linked peptides, we put forward proposals for follow-up software design, cross-linker developments, and guidance for the application of platinum(II)-based drugs. Overall, Pt(II)-based XL-MS can be a useful tool to complement both experimental and computational structural biology.

Addressing technology-mediated stigma in sexual health-related digital platforms: Insights from design team members.

Digital health technologies are increasingly used as complementary tools in accessing sexual health-related services. At the same time, there are concerns regarding how some interface features and content of these technologies could inadvertently foment stigma among end users. In this study, we explored how design teams (i.e., those involved in creating digital health technologies) might address stigmatizing components when designing sexual health-related digital technologies. We interviewed 14 design team members (i.e., software engineers, user interface and user experience (UI/UX) designers, content creators, and project managers) who were involved in digital health design projects across two universities in western Canada. The interviews sought to undersand their perspectives of how to create destigmatizing digital technologies and were centered on strategies that they might adopt or the kind of expertise or support they might need to be able to address stigmatizing features or content on sexual health-related digital technologies. The findings revealed two overarching approaches regarding how digital health technologies could be designed to prevent the unintended effects of stigma. These include functional design considerations (i.e., pop-up notifications, infographics, and video-based testimonials, and avoiding the use of cookies or other security-risk features) and non-functional design considerations (i.e., adopting an interprofessional and collaborative approach to design, educating software designers on domain knowledge about stigma, and ensuring consistent user testing of content). These findings reflected functional and non-functional design strategies as applied in software design. These findings are considered crucial in addressing stigma but are not often apparent to designers involved in digital health projects. This suggests the need for software engineers to understand and consider non-functional, emotional, and content-related design strategies that could address stigmatizing attributes via digital health platforms.

Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network.

Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a progressive neurodegenerative condition that predominantly impacts the elderly population, although it can also manifest in younger people through the impairment of cognitive functions, such as memory, cognition, and behaviour. Donepezil HCl and Memantine HCl are encapsulated in Nanostructured Lipid Carriers (NLCs) to prolong systemic circulation and minimize the systemic side effects. This work explores the use of data mining tools to optimize the formulation of NLCs comprising of Donepezil HCl and Memantine HCl for transdermal drug delivery. Neuroprotective drugs and excipients are utilized for protecting the nervous system against damage or degeneration. The NLCs were formulated using a high-speed homogenization technique followed by ultrasonication. NLCs were optimized using Box Behnken Design (BBD) in Design Expert Software and artificial neural network (ANN) in IBM SPSS statistics. The independent variables included the ratio of solid lipid to liquid lipid, the percentage of surfactant, and the revolutions per minute (RPM) of the high-speed homogenizer. The NLCs that were formulated had a mean particle size ranging from 67.0±0.45 to 142.4±0.52nm. Both drugs have a %EE range over 75%, and Zeta potential was determined to be - 26±0.36mV. CryoSEM was used to do the structural study. The permeation study showed the prolonged release of the formulation. The results indicate that NLCs have the potential to be a carrier for transporting medications to deeper layers of the skin and reaching systemic circulation, making them a suitable formulation for the management of Dementia. Both ANN and BBD techniques are effective tools for systematically developing and optimizing NLC formulation.

Adsorption and eliminating of diquat herbicide using layer double hydroxide enclosed in double layer hydrogel beads of carboxymethyl cellulose and alginate: Synthesis, characterization, adsorption isotherm, kinetics, thermodynamics and optimization via box-behnken design.

This study involved the creation of AlCu-layered double hydroxide (LDH) encapsulated in carboxymethyl cellulose (CMC) and alginate (Alg), which was then crosslinked with epichlorohydrin to form hydrogel beads (AlCu-LDH/CMC-Alg hydrogel beads) used for the removal of diquat (DQ) herbicide. The resulting material, AlCu-LDH/CMC-Alg hydrogel beads, underwent a comprehensive analysis of its properties using XRD, FT-IR, XPS, EDX, N2 adsorption/desorption isotherm, and FESEM to determine its textural characteristics. The AlCu-LDH/CMC-Alg hydrogel beads was analyzed using nitrogen adsorption/desorption isotherms to assess its textural properties. The hydrogel beads of AlCu-LDH/CMC-Alg showed a surface area of 114.22 m2/g, a pore volume of 0.35 cc/g, and a pore radius of 3.62 nm, indicating a mesoporous structure with a notable adsorption capacity. Following the adsorption of DQ, these measurements decreased to 65.145 m2/g, 0.2 cc/g, and 2.4 nm, respectively, suggesting that the DQ had filled or blocked the pores. This study also analyzed the impact of dose, pH, temperature, and initial concentration on the adsorption process. Equilibrium and adsorption kinetics were used to examine the adsorption characteristics. The process followed the pseudo-second-order and Langmuir isotherm models. The primary adsorption mechanism identified was chemisorption, with an adsorption energy of 29.6 kJ.mol-1. The rise in DQ absorption at higher temperatures suggests an endothermic and spontaneous adsorption process. The optimal adsorption parameters, as determined by the Box-Behnken design software, are a pH of 8, a dosage of 0.02 g of AlCu-LDH/CMC-Alg hydrogel beads per 25 mL, and an adsorption capacity of 302.6 mg/g for the DQ solution. Through careful testing and utilization of the Box-Behnken design and response surface technique in the Design-Expert software, significant enhancements were made to the adsorption process. The stability of the adsorbent was assessed by conducting six successive cycles of adsorption and desorption, revealing that its reusability remained steady with no noticeable decline in removal efficiency. Furthermore, it preserved its original chemical makeup both before and after being reused, demonstrated steady effectiveness, and kept consistent X-ray diffraction (XRD) results.

Clinical evaluation of 3D PEEK implants for skull bone defects repair: a single center case serious

BackgroundCustom-designed 3D-printed polyetheretherketone (PEEK) implants have emerged as a promising option for skull bone defect repair. This study aimed to evaluate the clinical outcomes, feasibility, and safety of using 3D-printed PEEK implants in patients with skull defects.MethodsA retrospective case series was conducted at Menoufia University Hospital, reviewing the medical records of patients who underwent cranioplasty between January 2022 and December 2023. Patients aged 18–65 years with skull bone defects due to trauma, surgical resection, or congenital anomalies were included. All patients underwent preoperative CT scans for custom implant design using computer-aided design (CAD) software. The 3D-printed PEEK implants were fabricated and implanted during surgery. Primary outcome measures included successful implant integration and structural stability, while secondary outcomes assessed complications and patient satisfaction.ResultsThirteen patients (76.9% male, mean age 45.8 years) were included. The majority of defects were caused by trauma (76.9%). The mean time to cranioplasty was 6 ± 2 months, and the average operative time was 143 ± 34 min. Mean blood loss was 265 ± 97 cc, and the average hospital stay was 2.85 ± 0.9 days. Complications included dural tears in 38.5% of cases, while no immediate postoperative complications were reported. Late complications included one case of convulsions (7.7%) and one superficial infection (7.7%). Radiographic follow-up confirmed successful implant integration in all patients.Conclusion3D-printed PEEK implants provide a feasible and effective solution for skull bone defect repair, with favorable outcomes and manageable complication rates. These custom implants offer a personalized approach to cranioplasty, but larger studies with longer follow-up are needed to validate these findings.

Enhancing Security in Software Design Patterns and Antipatterns: A Framework for LLM-Based Detection

The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to missed vulnerabilities and high rates of false positives. This paper introduces a novel framework leveraging Large Language Models (LLMs) to detect and mitigate security risks in design patterns and antipatterns. By analyzing relationships and behavioral dynamics in code, LLMs provide a nuanced, context-aware approach to identifying issues such as unauthorized state changes, insecure communication, and improper data handling. The proposed framework integrates key security heuristics—such as the principles of least privilege and input validation—to enhance LLM performance. An evaluation of the framework demonstrates its potential to outperform traditional tools in terms of accuracy and efficiency, enabling the proactive detection and remediation of vulnerabilities in real time. This study contributes to the field of software engineering by offering an innovative methodology for securing software systems using LLMs, promoting both academic research and practical application in industry settings.

Neurophysiological assessment of cortical motor function: a direct comparison of methodologies

ObjectiveAssessment of cortical function with threshold tracking transcranial magnetic stimulation (TT-TMS) has developed as a biomarker to inform disease pathophysiology, particularly in neurodegenerative disease and dementia. At present, a fully integrated testing system does not exist. To advance clinical utility, and to streamline software design to integrate with diagnostic approaches in an outpatient setting, the present series of studies assessed the effects of altering diagnostic paradigms to measure interstimulus interval (ISI) including serial ascending [T-SICIs] and parallel [T-SICIp] methodologies as measures of cortical motor function (the MagXite software). MethodsCortical excitability was assessed in 30 healthy controls with a figure-of-eight coil, using an integrated approach compared to previously established experimental paradigms. Motor evoked responses were recorded over the contralateral abductor pollicis brevis muscle. Short interval intracortical inhibition (SICI) was recorded with each testing paradigm and validated in a healthy control cohort. ResultsThe integrated system determined a robust measure of T-SICIs between ISI 1-to-7 ms (16.6±2.2%) that was comparable to previously established testing paradigms (P=0.34), but greater than T-SICIp (MagXite 10.7±1.5%, P=0.016; Sydney TT-TMS 8.7±1.4%, P=0.03). SICI peaks at ISI 1 and 2.5-to-3 ms were evident with both protocols. Significant correlations were evident between mean T-SICIs-MagXite and T-SICIp-MagXite (R=0.599, P<0.001). ConclusionThe present series validates a fully integrated motor cortical functional assessment to provide reproducible measures of SICI, with data obtained for intracortical inhibition that is more prominent when assessed using the method of serial ascending order. SignificanceAn integrated system for transcranial magnetic stimulation of the human motor system has been validated for clinical practice, suitable for the assessment of cortical function in neurological disease in an outpatient clinic setting.

Zygomatic Osteotomy surgery design software based on skull CT scans - Self-supervised algo reduces workload.

The morphology of the zygomatic complex significantly influences facial appearance, leading to a focus on zygomatic osteotomy. The current technique, the "L-shaped" zygomatic osteotomy, requires a small incision and preoperative osteotomy design for an osteotomy guide. However, the use of multiple software programs in the design process makes it time-consuming and clinically challenging. Artificial intelligence technology offers a solution by integrating digital medical technology into medicine. AI algorithms were developed based on point cloud models, using 2000 cases of three-dimensional CT data for training. Eighty CT data sets were randomly chosen for both AI and manual skull segmentation designs. The effectiveness, symmetry, safety, and aesthetic outcomes were compared. The AI zygomatic osteotomy showed superior performance in symmetry and aesthetics compared to manual zygomatic osteotomy. The complex structure of the zygomatic arch highlights the advantages of AI-driven osteotomy design, especially in intricate cases. Additionally, the AI osteotomy scheme demonstrated no compromise in safety indicators compared to the manual approach. AI zygomatic osteotomy proves to be a safe and effective alternative to manual zygomatic osteotomy, showcasing enhanced symmetry and aesthetic outcomes. The efficiency and precision of AI-driven design in complex zygomatic osteotomies make it a promising advancement in this field.

Comprehensive Guide to AI Software Design: From Conception to Implementation

This technical article presents a comprehensive examination of artificial intelligence software development, covering design principles, implementation strategies, and deployment practices. The article explores the convergence of traditional software engineering with advanced AI methodologies, addressing critical aspects including intelligence gathering, business process integration, and technology selection frameworks. The article delves into domain-specific applications across healthcare, financial services, and retail sectors while analyzing technical considerations in machine learning infrastructure, natural language processing, and computer vision systems. The article further examines bias mitigation strategies, system limitations, and best practices for production deployment, offering insights into monitoring, security, and scalability considerations for AI implementations.

A multi-level explainability framework for engineering and understanding BDI agents

As the complexity of software systems rises, explainability - i.e. the ability of systems to provide explanations of their behaviour - becomes a crucial property. This is true for any AI-based systems, including autonomous systems that exhibit decisionmaking capabilities such as multi-agent systems. Although explainabil- ity is generally considered useful to increase the level of trust for end-users, we argue it is also an interesting property for software engineers, developers, and designers to debug and validate the system’s behaviour. In this paper, we propose a multi-level explainability framework for BDI agents to generate explanations of a running system from logs at different levels of abstraction, tailored to different users and their needs. We describe the mapping from logs to explanations, and present a prototype tool based on the JaCaMo platform which implements the framework.

Investigating the Level of Experience in Using More Effective Software Design Tools for Enhancing Security among Federal College of Education, Gidan Madi Staff and Students

Investigating the Level of Experience in Using More Effective Software Design Tools for Enhancing Security among Federal College of Education, Gidan Madi Staff and Students

Comparison of biomechanical characteristics of the Schneiderian membrane with different transcrestal sinus floor elevation techniques using three-dimensional finite element analysis

ObjectiveThe aim of this study was to establish a three-dimensional finite element (FE) hydraulic pressure technique model and compare the biomechanical characteristics of the osteotome technique and the hydraulic pressure technique using three-dimensional finite element analysis (FEA).MethodsThree FE models were created: the hydraulic pressure technique (M1), the osteotome technique with a Ø 1.6-mm osteotome (M2), and the osteotome technique with a Ø 3.0-mm osteotome (M3) models. Three models were simulated via computer-aided design software, with the sinus membrane elevated to 1, 3 and 5 mm, after which the required loading force was recorded. Stress distribution, including the equivalent von Mises stress, tensile stress, compressive stress, shear stress, as well as strain (i.e., sinus membrane displacement in horizontal dimensions) of the three models were subsequently examined and statistically compared.ResultsOverall, the required loading force, stress and strain increased as the elevation height increased. The loading force required to elevate the sinus membrane to 1,3 and 5 mm in M1 was 24.9 kPa, 77.1 kPa and 130 kPa, comparing 32.5 kPa, 112. 9 kPa and 200.8 kPa in M2 as well as 54.5 kPa, 160.6 kPa and 273.2 kPa in M3. Under the same elevation height, M1 exhibited the least von Mises stress (P<0.001), as well as the largest horizontal sinus membrane displacement (P<0.001).ConclusionsIt can be seen from the FEA results that the hydraulic pressure technique enables a greater portion of the sinus membrane to detach from the sinus floor while exerting less stress on the mucosa when the sinus membrane is elevated up to 5 mm. Based on this study, the hydraulic pressure technique was found to be safer and more effective than the osteotome technique under the same elevation height.

Design of a mid/long-wave infrared dual-band panoramic optical system

In this paper, a dual-band and confocal panoramic annular lens for infrared detection is designed using ZEMAX optical design software to realize 360° panoramic monitoring. The working band covers the mid-infrared band (3–5 µm) and the long-infrared band (7–9.5 µm); the field of view angle is (28∘−80∘)×360∘; and the head unit of the lens is composed of two mirrors and a Ge protection window. The system’s aperture number is 1.3. The symmetrical aspherical surface is introduced into the system to correct the aberration, and the athermalization design is carried out to achieve good imaging quality in the working environment of −35−55∘C.

TLSynth: A Novel Blender Add-On for Real-Time Point Cloud Generation from 3D Models

Point clouds are a crucial element in the process of scanning and reconstructing 3D environments, such as buildings or heritage sites. They allow for the creation of 3D models that can be used in a wide range of applications. In some cases, however, only the 3D model of an environment is available, and it is necessary to obtain point clouds with the same characteristics as those captured by a laser scanner. For instance, point clouds may be required for surveys, performance optimization, site scan planning, or validation of point cloud processing algorithms. This paper presents a new terrestrial laser scanner (TLS) simulator, designed as a Blender add-on, that produces synthetic point clouds from 3D models in real time. The simulator allows users to adjust a set of parameters to replicate real-world scanning conditions, such as noise generation, ensuring the synthetic point clouds closely mirror those produced by actual laser scanners. The target meshes may be derived from either a real-world scan or 3D designs created using design software. By replicating the spatial distributions and attributes of real laser scanner outputs and supporting real-time generation, the simulator serves as a valuable tool for scan planning and the development of synthetic point cloud repositories, advancing research and practical applications in 3D computer vision.

An efficient assembly method for a viral genome based on T7 endonuclease Ⅰ-mediated error correction

Gene synthesis is an enabling technology that supports the development of synthetic biology. The existing approaches for de novo gene synthesis generally have tedious operation, low efficiency, high error rates, and limited product lengths, being difficult to support the huge demand of synthetic biology. The assembly and error correction are the keys in gene synthesis. This study first designed the oligonucleotide sequences by reasonably splitting the virus genome of approximately 10 kb by balancing the parameters of sequence design software ability, PCR amplification ability, and assembly enzyme assembly ability. Then, two-step PCR was performed with high-fidelity polymerase to complete the de novo synthesis of 3.0 kb DNA fragments, and error correction reactions were performed with T7 endonuclease Ⅰ for the products from different stages of PCR. Finally, the virus genome was assembled by 3.0 kb DNA fragments from de novo synthesis and error correction and then sequenced. The experimental results showed that the proposed method successfully produced the DNA fragment of about 10 kb and reduced the probability of large fragment mutations during the assembly process, with the lowest error rate reaching 0.36 errors/kb. In summary, this study developed an efficient de novo method for synthesizing a viral genome of about 10 kb with T7 endonuclease Ⅰ-mediated error correction. This method enabled the synthesis of a 10 kb viral genome in one day and the correct plasmid of the viral genome in five days. This study optimized the de novo gene synthesis process, reduced the error rate, simplified the synthesis and assembly steps, and reduced the cost of viral genome assembly.

A Comprehensive Study of OpenTelemetry Collector: Architecture, Use Cases, and Performance

As microservices and distributed systems gain popularity, observability encompassing metrics, logs, and traces has emerged as a pivotal concern in modern DevOps environments. OpenTelemetry (often abbreviated OTel), an open- source observability framework, seeks to standardize the way telemetry data is collected, processed, and exported. Within OTel’s ecosystem, the OpenTelemetry Collector (OTel Collector) serves as a flexible, pluggable data pipeline, bridging instrumentation signals from multiple sources to various backends. This collector consolidates the ingestion of metrics, logs, and traces in a single, vendor-neutral solution. This paper provides a comprehensive study of the OpenTelemetry Collector, its core architecture, deployment patterns, typical use cases, and performance considerations. We begin by detailing how the Collector interacts with instrumentation libraries, potential data sources, and various backend analysis systems. We then highlight real-world usage scenarios, from sidecar deployments to central aggregator setups, and advanced configurations, including load balancing or pipeline concurrency. Along the way, we examine anti-patterns such as unbounded concurrency, over-collecting data, or ignoring security aspects for sensitive traces. Benchmark results and best practices gleaned from real deployments prior to August 2022 offer guidance on scaling the Collector effectively, ensuring minimal overhead while retaining robust data. Ultimately, this paper aims to equip DevOps engineers, SREs, and software architects with actionable insights to successfully integrate OpenTelemetry Collector as a unified approach to metrics, logs, and traces in cloud-native systems. Keywords OpenTelemetry, Observability, Collector, Tracing, Metrics, Logs, Distributed Systems, DevOps, Performance, Kubernetes

Interactive Automatic Travel Schedule Generation Based on Blog and GPT information

With the growth of population and economic development, tourism has become a popular activity. In this case, we have to know the importance of making a plan. However, making plans through various kinds of traveling applications and social media makes it difficult to identify information reliability and choose a suitable route for the journey. So we designed WonderTrip, an applet to be a plug-in program that appears in the form of a plan on the article browsing interface of social media platforms. Further, we used Figma, UI design software, to build our project. Through the combination of frames and text, we allocated pages in order to ensure the operation of the program. This applet is designed to have no advertisements or viruses, providing the simplest way of ordering tickets and checking out the recommended attractions. At the same time, providing schemes for people who don't want to take the time to make their own travel plans.

Ultrasonic Sensor Modeling with Support Vector Regression

This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor’s response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising 6.99% error percentage.