Connectivity Constraints Research Articles

Evaluation of the feasibility of a midwifery educator continuous professional development (CPD) programme in Kenya and Nigeria: a mixed methods study

BackgroundMidwifery education is under-invested in developing countries with limited opportunities for midwifery educators to improve/maintain their core professional competencies. To improve the quality of midwifery education and capacity for educators to update their competencies, a blended midwifery educator-specific continuous professional development (CPD) programme was designed with key stakeholders. This study evaluated the feasibility of this programme in Kenya and Nigeria.MethodsThis was a mixed methods intervention study using a concurrent nested design. 120 randomly selected midwifery educators from 81 pre-service training institutions were recruited. Educators completed four self-directed online learning (SDL) modules and three-day practical training of the blended CPD programme on teaching methods (theory and clinical skills), assessments, effective feedback and digital innovations in teaching and learning. Pre- and post-training knowledge using multiple choice questions in SDL; confidence (on a 0–4 Likert scale) and practical skills in preparing a teaching a plan and microteaching (against a checklist) were measured. Differences in knowledge, confidence and skills were analysed. Participants’ reaction to the programme (relevance and satisfaction assessed on a 0–4 Likert scale, what they liked and challenges) were collected. Key informant interviews with nursing and midwifery councils and institutions’ managers were conducted. Thematic framework analysis was conducted for qualitative data.Results116 (96.7%) and 108 (90%) educators completed the SDL and practical components respectively. Mean knowledge scores in SDL modules improved from 52.4% (± 10.4) to 80.4% (± 8.1), preparing teaching plan median scores improved from 63.6% (IQR 45.5) to 81.8% (IQR 27.3), and confidence in applying selected pedagogy skills improved from 2.7 to 3.7, p < 0.001. Participants rated the SDL and practical components of the programme high for relevance and satisfaction (median, 4 out of 4 for both). After training, 51.4% and 57.9% of the participants scored 75% or higher in preparing teaching plans and microteaching assessments. Country, training institution type or educator characteristics had no significant associations with overall competence in preparing teaching plans and microteaching (p > 0.05). Qualitatively, educators found the programme educative, flexible, convenient, motivating, and interactive for learning. Internet connectivity, computer technology, costs and time constraints were potential challenges to completing the programme.ConclusionThe programme was feasible and effective in improving the knowledge and skills of educators for effective teaching/learning. For successful roll-out, policy framework for mandatory midwifery educator specific CPD programme is needed.

Shaking Table Tests and Numerical Analysis Conducted on an Aluminum Alloy Single-Layer Spherical Reticulated Shell with Fully Welded Connections

Aluminum alloy offers the advantages of being lightweight, high in strength, corrosion-resistant, and easy to process. It has a promising application prospect in large-span space structures, with its primary application form being single-layer reticulated shells. In this study, shaking table tests were conducted on a 1/25 scale aluminum alloy single-layer spherical reticulated shell structure. A finite element (FE) model of the reticulated shell structure was established in Ansys. Compared with the experimental results, the deviation in natural frequency, acceleration amplitude, and displacement amplitude was less than 20%, confirming the validity of the model. An extensive analysis of the various rise–span ratios and connection constraints of a single-layer spherical reticulated shell structure was carried out using the proposed FE model. The experimental and simulation results showed that as the rise–span ratio of the aluminum alloy reticulated shell increases, the natural frequency of the reticulated shell structure also increases while the dynamic performance decreases. The connection of the circumferential members changes from a rigid connection to a hinged connection. The natural frequency of the reticulated shell structure is reduced by about 40% while the acceleration and displacement response values are decreased by approximately 15%.

“Yes, I Am More Confident with My Avatars”: Integrating EFL Students’ Speaking Practice into Metaverse

This article presents an innovative practice that demonstrates the use of the metaverse to enhance English speaking skills in an undergraduate English drama course in South Korea. The innovation stemmed from the common situation that English-language learners feel self-conscious when speaking in front of their peers. To overcome this problem, the metaverse was used to provide a virtual space for students to practice dialogues and speeches using avatars. The two-phase implementation involved practicing dialogues from the American sitcom Friends and celebrity speeches in the metaverse space. Surveys and reflections showed that this approach significantly improved students’ confidence, motivation and English speaking skills by reducing the anxiety associated with physical presence. Nevertheless, some technical limitations, such as connectivity issues and constraints on virtual expression, were observed. Based on the findings, this study suggests the metaverse as a powerful tool for overcoming affective barriers in language learning, providing a safe and engaging platform for students to practice speaking skills. Furthermore, the study underscores the importance of selecting appropriate metaverse platforms and considering technical factors such as device compatibility and internet stability.

DIGITAL INITIATIVES OF HARYANA GOVERNMENT AND FARMER'S WELFARE

The agricultural landscape in Haryana, India, has undergone a transformative shift with the advent of digital technologies, specifically E-Portals encompassing social media and mobile applications. Digitalization presents the opportunity to assist in tackling the agricultural sector's challenges and improve productivity and sustainability. Digital initiatives (E-commerce and E-Portals) in agriculture have a significant positive impact on agricultural production efficiency. It encourages rational allocation of agricultural factors and promotes sustainable development.Based on Available data from national surveys and existing literature on the implementation and effects of digital portals, Mobile applications, and social media in agriculture utilization of digital technologies. The study adopted a mixed-methods approach, data taken from different sources like combining surveys, national surveys, existing literature, and data analytics to evaluate the adoption and utilization patterns of E-Portals, mobile applications and social media among farmers. The study aims to analyze the impact of E-Portals on farmers lives in Haryana. The research paper will focus on the E-Portals, Mobile apps and social media initiatives run by Government of Haryana. This research paper explores the multi-sided impact of these technological involvements on farmers in the region. Through a comprehensive study, we investigate how E-Portals have influenced various facets of farming practices, socio-economic conditions, and overall welfares of farmers in Haryana.Findings of this study reveal the extent to which farmers have integrated digital platforms into their daily routines for tasks such as market information access, crop management, financial transactions, and community engagement. Furthermore, the research explores the implications of E-Portals on improving market linkages, enhancing agricultural productivity, and contributing to sustainable farming practices.In addition to the positive outcomes, challenges and barriers faced by farmers in adopting and adapting to E-Portals are also analyzed. Issues such as digital literacy, connectivity constraints, and the socio-economic divide are examined to provide a holistic understanding of the technology's impact on different segments of the farming community.The research contributes valuable insights for policymakers, technology developers, and stakeholders aiming to enhance the effectiveness of E-Portals in agriculture. By understanding the dynamics of technology adoption among farmers in Haryana, this study aims to inform strategies that can bridge existing gaps, promote inclusivity, and ensure sustainable development in the agricultural sector. The study finds that regional digitalization can significantly raise agricultural total factor productivity, particularly in economically underdeveloped areas.

Connectivity Constraints Ensuring Continuous Electrodes in Topology Optimization of Electroactive Polymer

Abstract Electroactive polymers (EAPs) deform when subject to an electric field, which is generated by two or more electrodes. To ensure proper function of the EAP, these electrodes are connected to a source and they are therefore required to be continuous such that no isolated islands exist. Increasing an EAP’s performance using topology optimization while ensuring electrode connectivity is the goal of this work. A topology optimization formulation is introduced where electrode connectivity is ensured using the virtual temperature method. Numerical experiments demonstrate that this is an efficient method to guarantee connectivity.

Centering healthcare workers in digital health design: Usability and acceptability of two-way texting to improve retention in antiretroviral therapy in a public HIV clinic in Lilongwe, Malawi.

New initiates on antiretroviral therapy (ART) are at high risk of treatment discontinuation, putting their health at risk. In low- and middle-income countries, like Malawi, appropriate digital health applications (apps) must fit into local clinic, connectivity and resource constraints. We describe the human centered design (HCD) and development process of an open-source, hybrid, two-way texting (2wT) system to improve ART retention. We detail the critical role of diverse healthcare workers (HCWs) in the HCD process to inform app usability, create buy-in, and ensure appropriate optimization for the local context. We optimized 2wT usability and acceptability over three HCD phases: 1) informal feedback sessions with diverse 2wT stakeholders, 2) a small pilot, and 3) key informant interviews. Phase one included four sessions with diverse HCWs, including "expert ART clients", clinical, technical, supervisory, and evaluation teams to inform 2wT design. In phase 2, a small pilot with 50 participating ART clients aimed to inform implementation improvement. Phase three included interviews with ten HCWs to deepen understanding of 2wT acceptability and usability, documenting strengths and weaknesses to inform optimization. Multi-phase feedback sessions with HCWs helped refine 2wT language and message timing for both weekly and tailored client-specific visit reminders. The pilot led to improvements in educational materials to guide client responses and ease interaction with HCWs. In interviews, the HCWs appreciated the HCD co-creation process, suggested ways to increase access for low-literacy clients or those without consistent phone access, and felt integrating 2wT with other eHealth platforms would improve scalability. Inclusion of HCWs across phases of HCD design, adaption, and optimization increased 2wT usability and acceptability among HCWs in this setting. Engaging HCWs into 2wT co-ownership from inception appears successful in co-creation of an app that will meet HCW needs, and therefore, enhance support for 2wT clients to attend visits and remain in care.

Erratum: More Applications of the \(d\)-Neighbor Equivalence: Acyclicity and Connectivity Constraints

Erratum: More Applications of the \(d\)-Neighbor Equivalence: Acyclicity and Connectivity Constraints

ND-MRM: Neuronal Diversity Inspired Multisensory Recognition Model

Cross-sensory interaction is a key aspect for multisensory recognition. Without cross-sensory interaction, artificial neural networks show inferior performance in multisensory recognition. On the contrary, the human brain has an inherently remarkable ability in multisensory recognition, which stems from the diverse neurons that exhibit distinct responses to sensory inputs, especially the multisensory neurons with multisensory responses hence enabling cross-sensory interaction. Based on this neuronal diversity, we propose a Neuronal Diversity inspired Multisensory Recognition Model (ND-MRM), which, similar to the brain, comprises unisensory neurons and multisensory neurons. To reflect the different responses characteristics of diverse neurons in the brain, special connection constraints are innovatively designed to regulate the features transmission in the ND-MRM. Leveraging this novel concept of neuronal diversity, our model is biologically plausible, enabling more effective recognition of multisensory information. To validate the performance of the proposed ND-MRM, we employ a multisensory emotion recognition task as a case study. The results demonstrate that our model surpasses state-of-the-art brain-inspired baselines on two datasets, proving the potential of brain-inspired methods for advancing multisensory interaction and recognition.

Experimental study on the effect of partial shear studs layout on flexural behavior of steel-concrete composite beams

In steel-concrete composite beams, partial shear connection offers advantages including greater ductility and lower cost. However, partial shear connection constraints on composite beam structural performance, such as reduced beam stiffness, increased steel-concrete interface slip, etc., should not be neglected. Several researches have revealed that shear connector layout is one of the main approaches to limit partially composite beam vulnerability. Shear connector layout optimization has not been completely investigated experimentally, and its effect on composite beam structural performance is still unknown. This paper focusses on the experimental study of the effect of shear connectors layout on the flexural performance of composite beams. It reports the optimization of the layout of shear connectors based on blocked distribution of shear connectors laid in a single row, while concentrating the highest number of shear studs towards the supports of a simply supported composite beam. A fully composite beam with normal layout of shear studs was tested as control specimen, while the rest of the beams used partial shear connection (η = 0.5). Beams with partial shear connection had 3 different layouts of shear studs. The first had uniformly distributed shear studs, and the rest had shear studs laid out in 2 and 3 blocks each of uniform spacing respectively. The results showed that the beam with 3 blocks layout each of equal spacing of shear studs had the closest best structural performance to the fully composite beam and had better ductility than the latter.

Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism

Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling algorithms usually cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. To address these two problems, we propose an Automated Peripancreatic vEssel Segmentation and lAbeling (APESA) framework, to not only highly improve the segmentation performance for PPV, but also efficiently identify the peripancreatic artery (PPA) branches. There are two core modules in our proposed APESA framework: iterative trunk growth module (ITGM) for vein segmentation and weakly supervised labeling mechanism (WSLM) for artery labeling. The ITGM is composed of a series of iterative submodules, each of which chooses the largest connected component of the previous PPV segmentation as the trunk of a tree structure, seeks for the potential missing branches around the trunk by our designed branch proposal network, and facilitates trunk growth under the connectivity constraint. The WSLM incorporates the rule-based pseudo label generation with less expert participation, an anatomical labeling network to learn the branch distribution voxel by voxel, and adaptive radius-based postprocessing to refine the branch structures of the labeling predictions. Our achieved Dice of 94.01% for PPV segmentation on our collected dataset represents an approximately 10% accuracy improvement compared to state-of-the-art methods. Additionally, we attained a Dice of 97.01% for PPA segmentation and competitive labeling performance for PPA labeling compared to prior works. Our source codes will be publicly available at https://github.com/ZouLiwen-1999/APESA.

Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging

The controlled-swap and controlled-controlled-not gates are at the heart of the original proposal of reversible classical computation by Fredkin and Toffoli. Their widespread use in quantum computation, both in the implementation of classical logic subroutines of quantum algorithms and in quantum schemes with no direct classical counterparts, has made it imperative early on to pursue their efficient decomposition in terms of the lower-level gate sets native to different physical platforms. Here, we add to this body of literature by providing several logically equivalent circuits for the Toffoli and Fredkin gates under all-to-all and linear qubit connectivity, the latter with two different routings for control and target qubits. Besides achieving the lowest cnot counts in the literature for all these configurations, we also demonstrate the remarkable effectiveness of the obtained decompositions at mitigating coherent errors on near-term quantum computers via equivalent circuit averaging. We first quantify the performance of the method in silico with a coherent-noise model before validating it experimentally on a superconducting quantum processor. In addition, we consider the case where the three qubits on which the Toffoli or Fredkin gates act nontrivially are not adjacent, proposing a novel scheme to reorder them that saves one cnot for every swap. This scheme also finds use in the shallow implementation of long-range cnots. Our results highlight the importance of considering different entangling gate structures and connectivity constraints when designing efficient quantum circuits.

An iterative method for updating finite element models with connectivity constraints

It is well known that the analytical matrices arising from the discretization of distributed parameter systems using the finite element technique are usually symmetric and banded. How to preserve the coefficient matrices of the updated model being of the same band structure is an important yet difficult challenge for model updating in structural dynamics. In this paper, an iterative method for updating mass, damping and stiffness matrices simultaneously based on partial modal measured data is provided. By the method, the optimal updated matrices can be obtained within finite iteration steps by choosing a special kind of initial matrix triplet. The proposed approach not only preserves the physical connectivity of the original model, but also the updated model reproduces the measured modal data, which can be utilized for various finite element model updating problems. Numerical examples confirm the effectiveness of the introduced method.

Power system differentiation planning based on an improved marine predator algorithm

Abstract Effective power system differentiation planning is crucial for enhancing the resilience of power grid infrastructure and bolstering the ability of power systems to manage blackouts. At the heart of power system differentiation planning lies core backbone grid planning. This study involves modeling core backbone grid planning as a multi-objective 0–1 planning problem, which enables the formulation of a multi-objective function that incorporates various factors such as operational and maintenance costs, the significance of nodes and transmission lines, as well as compliance with connectivity and security operation constraints. Moreover, the basic marine predator algorithm was upgraded into a multi-objective optimization algorithm for core backbone grid planning by implementing file management and enhancing the top predator selection mechanism, which managed to fulfill the multi-objective function optimization standards. The results show that in the IEEE 39-node system, the algorithm successfully forms a core backbone grid comprising 22 nodes and 19 transmission lines, achieving economic feasibility with a node-to-line ratio of 1.158. Similarly, for the IEEE 300-node system, the algorithm constructs a larger core backbone grid consisting of 81 nodes and 80 transmission lines, maintaining economic efficiency with a node-to-line ratio of 1.0125. This expanded grid covers a significant number of critical nodes and transmission lines, ensuring optimal network connectivity. Furthermore, the algorithm’s load satisfaction analysis showcases its ability to effectively balance active and reactive power demands, with maximum outputs meeting the respective load demands.

Africa's Path to Sustainability: Harnessing Technology, Policy, and Collaboration

This paper explores the significant role of technological advancements, strategic policies, and collaborations in driving Africa towards a more sustainable future. It highlights how the continent's increasing adoption of innovative technologies, such as renewable energy solutions and digital infrastructure, coupled with well-crafted strategic policies and international collaborations, is transforming various sectors and fostering a sustainable future. These advancements have not only improved access to basic services like healthcare and education but have also created new opportunities for economic growth and job creation. The paper emphasizes the importance of ongoing collaborations between African countries and international partners in sharing knowledge, expertise, and resources to accelerate sustainable development efforts across the continent. The paper discusses different international organizations that have collaborated with and assisted Africa in the areas of technical innovation, finance, and knowledge exchange necessary to achieve a full-scale sustainable future. Despite their humanitarian efforts, Africa faces tremendous hurdles in attaining a sustainable future. These challenges range from a lack of access to technology and digital infrastructure in rural areas to difficulties in harnessing technological advancements due to infrastructure and connectivity constraints. These challenges have hindered Africa's ability to fully leverage the potential of technical innovation and digital solutions for a sustainable future. Limited financial resources and investment opportunities have further impeded progress in achieving the necessary infrastructure and connectivity upgrades. The continent is vulnerable to the impacts of climate change, which further hinders its development progress. Therefore, it is crucial for ongoing collaborations between African countries and international partners to address these challenges collectively and work towards long-term solutions for a sustainable future in Africa.

On the generation of discrete figures with connectivity constraints

This paper addresses a generalization of polyominoes called (a, b)-connected discrete figures, where a and b represent the connectivity of the foreground (i.e. black pixels) and background (i.e. white pixels), respectively. Formally, a finite set of pixels P is (a, b)-connected if P is a-connected and P is b-connected. By adapting a combinatorial structure enumeration algorithm by J. L. Martin and employing breadth-first search ordering on the pixels of the figures, we sequentially generate all (a, b)-connected discrete figures up to size n = 18, utilizing minimal storage space. This paper presents an extended version of the research presented at the 2022 GASCom conference.

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, crucial for the diagnostic and therapeutic processes in musculoskeletal diseases. In general, segmentation methods such as U-Net deep learning neural networks can achieve good Dice Similarity Coefficient (DSC) values, e.g. around 0.83 to 0.91 on various cohorts. Some generic post-processing strategies have been studied to incorporate connectivity constraints into the resulting masks for the purpose of further improving the segmentation accuracy. In this paper, a novel mean shape (MS) based post-processing method is proposed, utilizing Statistical Shape Modelling (SSM) to fine-tune the segmentation output, taking into consideration the muscle anatomical shape. The methodology was compared to existing post-processing techniques and a commercial semi-automatic tool on MRI scans from two cohorts of post-menopausal women (10 Training, 8 Testing, voxel size 1.0x1.0x1.0 mm3). The MS based method obtained a mean DSC of 0.83 across the different analysed muscles and the best performance for the Hausdorff Distance (HD, 20.6 mm) and the Average Symmetric Surface Distance (ASSD, 2.1 mm). These findings highlight the feasibility and potential of using anatomical mean shape in post-processing of human lower-limb muscle segmentation task and indicate that the proposed method can be popularized to other biological organ segmentation mission.

Wireless sensor network deployment optimization for a smart farming application: comparison of two Multi-Objective Evolutionary Algorithms

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.

Topology optimization of labyrinth seals using interface identification techniques

Labyrinth seals are mechanical devices that reduce the leakage between moving components of rotating machines. They are crucial for determining the performance of these machines and essential for reducing greenhouse gas emissions. Therefore, mathematical optimization algorithms for labyrinth seals are of high interest. This work concerns the topology optimization of labyrinth seals, which is a challenging task because the optimizer must distribute solids at different velocities to properly model the flow in the cavity of the labyrinth seal. Also, the model must not allow the optimizer to close the fluid channel or to leave free-floating islands of solid elements in the final design. Therefore, the interface identification method based on a series of erosion-dilate projection filters is used to model the fluid channel as the interface between the rotor and stator, allowing the representation of different velocities and avoiding the channel closure. The virtual temperature method is employed as a connectivity constraint to avoid free-floating islands of solid material. Still, thin structural members appear in the optimized designs, so geometric constraints are used to impose the minimum length scale on rotor and stator components. The final topology optimization results are labyrinth seals with contorted channels constricted to the minimum allowed gap size (as expected for low Reynolds flows), showing the success of the proposed formulation. The staggered and stepped labyrinth seal configurations are optimized to show the generality of the method.

Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned

Artificial Intelligence (AI) based chest X-ray (CXR) screening for tuberculosis (TB) is becoming increasingly popular. Still, deploying such AI tools can be challenging due to multiple real-life barriers like software installation, workflow integration, network connectivity constraints, limited human resources available to interpret findings, etc. To understand these challenges, PATH implemented a TB REACH active case-finding program in a resource-limited setting of Nagpur in India, where an AI software device (qXR) intended for TB screening using CXR images was used. Eight private CXR laboratories that fulfilled prerequisites for AI software installation were engaged for this program. Key lessons about operational feasibility and accessibility, along with the strategies adopted to overcome these challenges, were learned during this program. This program also helped to screen 10,481 presumptive TB individuals using informal providers based on clinical history. Among them, 2,303 individuals were flagged as presumptive for TB by a radiologist or by AI based on their CXR interpretation. Approximately 15.8% increase in overall TB yield could be attributed to the presence of AI alone because these additional cases were not deemed presumptive for TB by radiologists, but AI was able to identify them. Successful implementation of AI tools like qXR in resource-limited settings in India will require solving real-life implementation challenges for seamless deployment and workflow integration.

Optimal operation of coordinated multi-carrier energy hubs for integrated electricity and gas networks

"In the field of energy supply, coordinated multi-carrier systems, which include natural gas and electric energy, offer unique opportunities to enhance energy efficiency and flexibility. Nevertheless, the interdependence between electricity and natural gas networks poses various challenges related to the flow of electricity and gas in feeders, pipes, and connection points. To address these challenges, the concept of an energy hub emerges as an essential component in multi-carrier energy systems, serving as a connection point between the electricity grid and gas grids. This study introduces an optimal operation method for coordinating gas and electricity distribution networks through interconnected energy hubs. The infrastructure of the proposed energy hub comprises a combined heat and power unit, a boiler, an electric energy storage system, a heat pump, and a gas-burning unit, effectively meeting heating and electrical load requirements. The model formulation follows a stochastic approach based on a two-stage scenario. Considering uncertainties related to wind energy, electric load, and real-time energy prices, the primary objective of this study is to minimize the total operating cost of the energy system. The optimization problem is solved using Wall's optimization algorithm. To meet its energy requirements, the integrated energy system presented in this paper actively participates in the real and daily electric energy markets, as well as the natural gas market, providing the necessary energy resources. Additionally, the model incorporates realistic descriptions of electric power and gas flow in feeders and gas pipelines, considering AC load distribution and the Weymouth equation. With consideration of connectivity constraints, the proposed model offers a comprehensive representation of the integrated electricity and gas networks. The simulations conducted on the integrated energy system, comprising a 33-bus electric network and a 6-node gas network with interconnected energy hubs, highlight a remarkable 12 % decrease in electricity purchase costs and a substantial 18 % reduction in natural gas procurement expenses, underscoring the cost-efficiency and effectiveness of the stochastic planning approach.