Design Framework Research Articles

Design and implementation of VGD6-NET framework for waste segregation using 3-tier convolutional neural networks

Design and implementation of VGD6-NET framework for waste segregation using 3-tier convolutional neural networks

Effect of high-flow nasal therapy on patient-centred outcomes in patients at high risk of postoperative pulmonary complications after cardiac surgery: update to the statistical analysis plan for NOTACS, a multicentre adaptive randomised controlled trial

BackgroundThe NOTACS trial will assess the efficacy, safety and cost-effectiveness of high-flow nasal therapy (HFNT) compared to standard oxygen therapy (SOT) on the outcomes of patients after cardiac surgery.Methods/designNOTACS is an adaptive, international, multicentre, parallel group, randomised controlled trial, with a pre-planned interim sample size re-estimation (SSR). A minimum of 850 patients will be randomised 1:1 to receive either HFNT or SOT. The primary outcome is days alive and at home in the first 90 days after the planned surgery (DAH90), with a number of secondary analyses and cost-effectiveness analyses also planned. The interim SSR will take place after a minimum of 300 patients have been followed up for 90 days and will allow for the sample size to increase up to a maximum of 1280 patients.ResultsThis manuscript provides detailed descriptions of the design of the NOTACS trial and the analyses to be undertaken at the interim and final analyses. The main purpose of the interim analysis is to assess safety and to perform a sample size re-estimation. The main purpose of the final analysis is to examine the safety, efficacy and cost-effectiveness of HFNT compared to SOT on the outcomes of patients after cardiac surgery.DiscussionThis manuscript outlines the key features of the NOTACS statistical analysis plan and was submitted to the journal before the final analysis in order to preserve scientific integrity under an adaptive design framework. A previous version of this SAP was published prior to the interim analysis (Dawson, 2022). The NOTACS SAP closely follows published guidelines for the content of SAPs in clinical trials (Gamble, 2017).Trial registrationISRCTN14092678. (13 May 2020).

Lubrication Prediction of Sphere‐Gradient Coated Half Space Interfaces

ABSTRACTFunctionally graded coating (FGC) has played a pivotal role in numerous engineering applications owing to its exceptional properties. This work proposes a novel elastohydrodynamic lubrication (EHL) model with FGC, in which the elastic deformation and stress are computed using influence coefficients (ICs) and discrete convolution‐fast Fourier transform (DC‐FFT) algorithm. Comparisons are made with homogeneous EHL solutions and finite element analysis (FEA) to validate its accuracy. The study systematically explores how coating elastic modulus, coating thickness and substrate elastic modulus influence contact and lubrication behaviours. The developed model is expected to establish a theoretical framework for FGC material design, enhancing the performance of friction pairs.

Design of a 3D Intelligent Building Management Framework that Integrates Geographic Information Systems and Building Information Models

Design of a 3D Intelligent Building Management Framework that Integrates Geographic Information Systems and Building Information Models

Autonomous ecologies of construction: Collaborative modular robotic material eco-systems with deep multi-agent reinforcement learning

To address the unprecedented challenges of construction pressurized by the global climate crisis, housing shortage, and growing shortage of skilled labor, this research presents a radical shift in the construction lifecycle of buildings, from linear processes that produce static continuous buildings to interrelational processes linking adaptative eco-systems of collaborative robots and reconfigurable building parts. Inspired by natural builders, the interdisciplinary field of collective robotic construction (CRC) offers the potential for scalable, adaptive, and resilient construction with simple robots. We establish a design framework for autonomous collaborative robotic construction (ACRC) through modular robotic material eco-systems (MRMES) trained with deep multi-agent reinforcement learning (DMARL). This involves the integration of three core aspects: (1) modular robotic material eco-systems (2) cyber-physical simulation and control with bidirectional feedback (3) adaptive intelligence through deep multi-agent reinforcement learning. The framework is implemented through three comparable case studies for collaborative modular robotic assembly of reconfigurable building parts.

A multiple biomolecules-based rapid life detection protocol embedded in a rover scientific subsystem for soil sample analysis

The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.

Psychometric properties of the Pediatric Evaluation of Disability Inventory - Patient Reported Outcome: A cognitively accessible measure of functional performance.

To evaluate the structural validity, internal reliability, and measurement invariance of the Pediatric Evaluation of Disability Inventory - Patient Reported Outcome (PEDI-PRO), a measure of functional performance of discrete tasks required to participate in everyday life situations important for adulthood. This was a cross-sectional study with 306 young people aged 14 to 22 years (mean 18 years 10 months, SD 2 years 5 months) with developmental disabilities (43.1% autism spectrum disorder only, 23.9% intellectual disability, 17.6% other disability, 11.4% both autism spectrum disorder and intellectual disability, 3.9% missing) completed the PEDI-PRO. Following COnsensus-based Standards for selection of health Measurement INstruments (COSMIN) criteria, we conducted a confirmatory factor analysis, applied a Rasch rating-scale model, examined Cronbach's alpha, Rasch person reliability and separation coefficients, and differential item functioning (DIF). Structural validity was good for the daily activities and mobility domains, and acceptable for the social/cognitive domain. The 3-point Likert response scale functioned as intended. All domains demonstrated acceptable internal consistency on all criteria. One or two items in each domain demonstrated DIF, but the impact on all domain scores was less than 1.0 threshold. The cognitively accessible design and innovative conceptual measurement framework probably contributed to these promising findings. The PEDI-PRO addresses a gap in high-quality patient-reported outcome measures that assess priority outcomes for young people with developmental disabilities.

Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time.

The dissociation rate constant (koff) significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for de novo drug design guided by koff. First, a comprehensive database containing 2,773 unique koff values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of koff with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low koff. Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/koff) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based de novo drug design framework in designing drug candidates with prolonged τ.

A Theoretical Design Framework of Contemporary Vernacular Architecture Based on a Scoping Review of the Best Practices Worldwide

This paper reviews the development of vernacular architecture design and practices worldwide and then focuses on Chinese rural development. It explores the evolution of vernacular architecture and related concepts from a longitudinal perspective, and indicates the dynamic adaptability of vernacular architecture. A scoping review based on archival materials and secondary source literature of the last 10 years is conducted. As a result, 41 design factors have been collected. With a cross-comparative study with related publications, five intangible design factors and ten key tangible design factors are discussed to build a theoretical design framework, which finally provides a foundation for related design practices and further research.

Building Engagement-Capable Environments for Health System Transformation: Development and Early Implementation of a Capability Framework for Patient, Family and Caregiver Engagement in Ontario Health Teams.

Despite widespread calls to involve patients, families and caregivers (PFCs) as partners at all levels of health system planning and design, there is unevenness in how engagement efforts are supported across these settings. The concept of 'engagement-capable environments' offers a way forward to uncover the key requirements for sustainable, high-quality engagement, but more work is needed to identify the specific competencies required to create these environments. We addressed this gap by developing a capability framework for Ontario Health Teams (OHTs), a newly established structure for planning, designing, organizing and delivering care in Ontario, Canada. The framework was co-developed by a Working Group of OHT staff and leaders, PFC partners, researchers and government personnel. Project activities occurred over four phases: (1) planning, (2) evidence review and surveying of intended users to identify key competencies, (3) framework design and (4) implementation. An evidence review identified more than 90 potential competencies for this work. These results were contextualized and expanded through a survey of OHT stakeholders to brainstorm potential competencies, supports and enablers for engagement. Surveys were completed by 69 individuals; 689 knowledge and skill competency statements, 462 attitude and behaviour competency statements and 250 supports and enablers were brainstormed. The statements were analysed and organized into initial competency categories, which were reviewed, discussed and iteratively refined by Working Group members and through broader consultations with the OHT community. The final framework includes six competency domains and four support and enabler domains, each with sub-domain elements, mapped across a three-stage maturity model. The framework has been disseminated across OHTs, and its adoption and implementation are now requirements within OHT agreements. The framework combines a strong conceptual foundation with actionable elements informed by the literature and consultations with the intended users of the framework. Although developed for OHTs, the framework should be broadly applicable to other health system organizations seeking similar health system transformation goals. Patient, family and caregiver partners were involved at all stages and in all aspects of the work. As end users of the framework, their perspectives, knowledge and opinions were critical.

High-Affinity Fully Human Anti-EpCAM Antibody with Biased IL-2 Exhibits Potent Antitumor Activity

Monoclonal antibodies (mAbs) are widely used in cancer therapy but often show limited efficacy for solid tumors. Enhancing anti-tumor activity by fusing cytokines to tumor-targeting mAbs, which specifically activate immune cells within the tumor microenvironment, represents a promising strategy. However, the optimal design and therapeutic efficacy of antibody–cytokine fusion formats remain unclear. The epithelial cell adhesion molecule (EpCAM), frequently overexpressed in a variety of carcinomas, serves as the target for immunotherapies. In this study, we identified a fully human mAb targeting EpCAM, designated as m801, from a previously constructed phage-displayed fully human antibody library. By fusing m801 with an IL-2 variant (IL-2v) in two configurations, m801.2 (2 anti-EpCAM Fab + 1 IL-2v) and m801.3 (1 anti-EpCAM Fab + 1 IL-2v), we identified m801.2 as the lead candidate due to its superior biophysical properties, including high thermal stability, homogeneity, and low aggregation. Furthermore, m801.2 showed strong binding affinity to EpCAM, with KD values of 0.6 nM, and an EpCAM-expressing tumor cell line, comparable to the original IgG m801. Additionally, m801.2 exhibited IL-2 receptor β subunit (IL-2Rβ)-biased binding activity, with a KD of 27.3 nM, resulting in superior effective T cell activation. In an SW480 xenograft mice model, m801.2 significantly inhibited tumor growth and demonstrated high tolerability. These findings suggest a valuable framework for the future design of immunocytokine therapies.

Revisiting the shuffle of generalized Feistel structure

The Generalized Feistel Structure (GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document}) is one of the most widely used frameworks in symmetric cipher design. In FES 2010, Suzaki and Minematsu strengthened the cryptanalysis security of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} by searching for shuffles with the best diffusion property. In ASIACRYPT 2018, Shi et al. suggested a set of shuffles, which makes GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} a better resistance against Demirci–Selcuk meet-in-the-middle cryptanalysis. Since these shuffles are different from the currently known good ones and also different from the shuffles used in TWINE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{TWINE}$$\\end{document} and LBlock\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{LBlock}$$\\end{document}, our research focuses on a more comprehensive evaluation of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} with different shuffles, including diffusion property of shuffle, differential, linear, impossible differential, zero-correlation linear, integral and Demirci–Selcuk meet-in-the-middle cryptanalysis, to find the best one. Such evaluations entail significant time consumption. Thus, we utilize Mixed Integral Linear Programming models and introduce an evaluate-and-filter strategy to achieve it efficiently. Our results verify that the shuffles discovered by Suzaki and Minematsu and those used in TWINE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{TWINE}$$\\end{document} and LBlock\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{LBlock}$$\\end{document} are the best so far. We also find that the cryptanalysis resistances of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} are not necessarily consistent. It is this finding that makes the necessity of our more comprehensive evaluation self-evident.

IDM-Net: A Multi-Task Supported Encoder-Decoder Framework for Magnetic Field Inverse Design

IDM-Net: A Multi-Task Supported Encoder-Decoder Framework for Magnetic Field Inverse Design

NiS2/FeS2 binary nanoparticles confined in interconnected carbon framework with regulated pore structure enabled by citric acid for enhanced sodium storage properties

Recently, pyrite iron disulfide (FeS2) has emerged as a promising anode candidate for sodium-ion batteries (SIBs) due to its high theoretical capacity, affordability, non-toxicity and abundant resource in nature. However, the utilization of FeS2 still confronts the challenges of inferior rate capability and cycling instability for sodium storage, stemming from its low electronic conductivity and substantial volume changes during cycling. Herein, to address these obstacles, NiS2/FeS2 binary nanoparticles encapsulated within a network of interconnected N-doped porous carbon framework (NiS2/FeS2@NPC) are prepared by a successive solid-state ball milling, carbonization and sulfurization strategy with coordination complex of nickel iron Prussian blue analogue (NiFe-PBA) as precursor. The introduction of citric acid plays a critical role for the formation of interconnected carbon framework with regulated internal porous structure, thereby achieving heterostructured NiS2/FeS2@NPC with modulated specific surface area and pore volume. The rational design of interconnected N-doped porous carbon framework and heterogeneous structure guarantees rapid ion/electron transport kinetics, alleviated mechanical stress, and enhanced structural integrity. Benefitting from these advantages, the optimal NiS2/FeS2@NPC-1 electrode exhibits a high reversible capacity (545 mAh/g at 1 A/g), superior rate capability (267 mAh/g at 5 A/g) and ultrastable long-term cycling performance (98.5 % retention over 1000 cycles at 5 A/g). This study presents a novel and efficient design approach for creating durable and high-rate anode materials based on metal sulfides for SIBs.

Embedded State Estimation for Optimization of Cislunar Space Domain Awareness Constellation Design

The traffic in cislunar space is expected to increase over the coming years, leading to a higher likelihood of conjunction events among active satellites, orbital debris, and noncooperative satellites. This increase necessitates enhanced space domain awareness (SDA) capabilities that include state estimation for targets of interest. Both Earth surface-based and space-based observation platforms in geosynchronous orbit or below face challenges such as range, exclusion, and occlusion that hinder observation. Motivated by the need to place space-based observers in the cislunar space regime to overcome these challenges, this paper proposes a cislunar SDA constellation design and analysis framework that integrates state estimation into an optimization problem for determining the placement of observers for optimal state estimation performance on a set of targets. The proposed multiobserver placement optimization problem samples from a range of possible target orbits. Upon convergence, the optimized constellation is validated against a broader set of targets to assess its effectiveness. Two comparative analyses are presented to evaluate the effects of changes in the sensor tasking procedure and sensor fidelity on the optimized constellation, comparing these to a single observer baseline case. The results demonstrate that the optimized constellations can provide accurate state estimation for various orbit families.

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO & Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites

This study presents an innovative application of the Taguchi design of experiment method to optimize the structure of an Artificial Neural Network (ANN) model for the prediction of elastic properties of short fiber reinforced composites. The main goal is to minimize the computational effort required for hyperparameter optimization while enhancing the prediction accuracy. By utilizing a robust experimental design framework, the structure of an ANN model is optimized. This approach involves identifying a combination of hyperparameters that provides optimal predictive accuracy with the fewest algorithmic runs, thereby significantly reducing the required computational effort. The results suggested that the Taguchi-based developed ANN model with three hidden layers, 20 neurons in each hidden layer, elu activation function, Adam optimizer, and a learning rate of 0.001 can predict the anisotropic elastic properties of short fiber reinforced composites with a prediction accuracy of 97.71%. Then, external validation of the proposed ANN model was done using experimental data, and differences of less than 10% were obtained, indicating an appropriate predictive performance of the proposed ANN algorithm. Our findings demonstrate that the Taguchi method not only streamlines the hyperparameter tuning process but also substantially improves the algorithm's performance. These results highlight the potential of the Taguchi method as a powerful tool for optimizing machine learning algorithms, especially in scenarios where computational resources are limited. The implications of this study are far-reaching, offering insights for future research in the optimization of different algorithms for improved accuracies and computational efficiencies.

A Crashworthiness Design Framework based on Temporal-Spatial Feature Extraction and Multi-Target Sequential Modeling

A Crashworthiness Design Framework based on Temporal-Spatial Feature Extraction and Multi-Target Sequential Modeling

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning

In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels.

Design Of A Hybrid Power Generation System in Cipatujah Tasikmalya Regency

The escalating daily demand for electricity, propelled by technological advancements and the pervasive use of electronic devices, accentuates the imperative for a dependable and sustainable power generation system. Renewable energy sources emerge as a viable solution to address the electricity needs in Indonesia, particularly in remote areas where access to the main grid is challenging. Tasikmalaya Regency, situated in West Java Province and defined by its geographical coordinates between 7°02' 29" - 7°49' 08" South Latitude and 107°54' 10" - 108°25' 52" East Longitude, presents a promising setting for the implementation of such sustainable energy solutions. The coastal area of Tasikmalaya, specifically Cipatujah Subdistrict, exhibits substantial renewable energy potential, including wind, solar radiation, and ocean waves. This research endeavors to design an efficient hybrid power generation system in Cipatujah Subdistrict using HomerPro software. The methodology involves crafting various models to determine the most economically efficient generation strategy. The study utilizes electricity consumption data sourced from the Karangnunggal Tasikmalaya Substation, situated in the southern coastal region of Tasikmalaya, particularly in Cipatujah Subdistrict, with a daily consumption of 20.6917 MW. By addressing the specific energy needs of the region, the research aims to make a positive contribution to the development of renewable energy and, concurrently, serve as a guiding framework for similar system designs in locations sharing comparable characteristics. This multifaceted approach underscores the significance of tailoring sustainable energy solutions to the unique requirements of diverse regions, fostering a broader transition towards a reliable and environmentally conscious energy future.