Key Performance Metrics Research Articles

Infection control practices in public sector hospitals of Punjab: a critical analysis

IntroductionInfection prevention and control (IPC) is imperative towards patient safety and health. The Infection Prevention and Control Assessment Framework (IPCAF) developed by WHO provides a baseline assessment at the acute...

Residential long-span timber floor typologies: a comprehensive performance assessment and opportunities for value adding to plantation hardwood

ABSTRACT High-performance engineered wood products (EWPs) and composite mass timber products (CMTPs) are being employed more frequently in residential projects with increasing interest in more sustainable systems that achieve long-spans. The relative performance of these systems is not readily apparent, with individual manufacturers offering proprietary products assembled from specific timber resources. In addition, the suitability of producing these long-span systems using plantation hardwoods is currently unknown. This research investigated the comparative performance of four typical EWPs and CMTPs; 1. solid slab, 2. thin-walled cassette, 3. T-sections, and 4. slab on beam. Key performance metrics of depth, mass, stiffness, vibration response, fire performance and global warming potential were assessed. The mechanical performance of two high-strength plantation hardwood varieties (Eucalyptus nitens and Eucalyptus globulus) were determined experimentally and subsequently used to re-calculate performance criteria for the previously assessed typologies. Substantial improvements over the typical softwood varieties were identified, particularly in structural efficiency, global warming potential and fire performance. This highlights the potential for value adding to plantation hardwoods by using them in high-performance long-span engineered floor products.

Performance Evaluation of VGG_19 Model for Brain Tumor Datasets

This paper presents a comprehensive assessment of the VGG19 model for detecting brain tumors using deep learning methods, particularly convolutional neural networks (CNNs). The study evaluates key performance metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). The investigation delves into the influence of different activation functions, learning rates, and epochs on the VGG19 model's performance. Activation functions are crucial for feature extraction and model convergence, with a comparison made between ReLU and Leaky ReLU to determine their effectiveness in enhancing brain tumor detection accuracy. Additionally, the study examines the impact of various learning rates (0.01, 0.001, 0.0001) on the model's convergence speed and overall performance. Different numbers of training epochs (15, 25, 50) are also considered to strike a balance between computational efficiency and model effectiveness. Evaluation metrics encompass accuracy, precision, recall, F1 score, and AUC, providing a comprehensive assessment of the model's performance across different configurations. The goal is to identify optimal combinations of activation functions, learning rates, and epochs to maximize the VGG19 model's accuracy in detecting brain tumors. This research contributes to advancing efficient and accurate methodologies for brain tumor detection, facilitating early diagnosis and treatment planning. The insights gained from this study can guide the development of more effective deep learning-based systems for medical image analysis, benefiting both patients and healthcare professionals.

Improving wave energy conversion performance of a floating BBDB-OWC system by using dual chambers and a novel enhancement plate

In this study, a novel floating dual-chamber backward bent duct buoy oscillating water column (BBDB-OWC) wave energy converter (WEC) is introduced, featuring a horizontal plate at the bottom of the front chamber to act as an enhancement plate. A three-dimensional computational fluid dynamics (CFD) model is developed and validated by comparing its results with existing experimental measurements. The validated model is employed to investigate the hydrodynamic performance and power generation characteristics of the dual-chamber BBDB-OWC WEC under various conditions, including variations in the length of the horizontal plate (lp/lf) and different regular wave conditions. Key performance metrics, including peak to average ratio of power (PTARP), wave energy capture width ratio (ξtotal), and its wave period respond bandwidth (indicated by Pξtotal > 0.5 and Pξtotal > 0.7), are analyzed and compared with those of a traditional single-chamber BBDB-OWC WEC. The results reveal that, compared to the single-chamber WEC, the dual-chamber WEC with a specific horizontal plate length reduces the average PTARP from 2.88 to a minimum value of 1.82 for lp/lf = 0.5, improves the average ξtotal from 0.55 to a maximum value of 0.64 for lp/lf = 2.5, and increases Pξtotal > 0.5 and Pξtotal > 0.7 from 71 % and 14 % to maximum values of 86 % and 43 % for lp/lf = 2.5, respectively. An explanation for these observations is also provided in the context of structure motion and flow fields.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.    

Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism

In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors.

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

Accelerate vaccine development using seamless phase 2/3 trial designs.

Traditional vaccine development, often a lengthy and costly process of three separated phases. However, the swift development of COVID-19 vaccines highlighted the critical importance of accelerating the approval of vaccines. This article showcases a seamless phase 2/3 trial design to expedite the development process, particularly for multi-valent vaccines. This study utilizes simulation to compare the performance of seamless phase 2/3 design with that of conventional trial design, specifically by re-envisioning a 9-valent HPV vaccine trial. Across three cases, several key performance metrics are evaluated: overall power, type I error rate, average sample size, trial duration, the percentage of early stop, and the accuracy of dose selection. On average, when the experimental vaccine was assumed to be effective, the seamless design that performed interim analyses based solely on efficacy saved 555.73 subjects, shortened trials by 10.29 months, and increased power by 3.70%. When the experimental vaccine was less effective than control, it saved an average of 887.73 subjects while maintaining the type I error rate below 0.025. The seamless design proves to be a compelling strategy for vaccine development, given its versatility in early stopping, re-estimating sample sizes, and shortening trial durations.

Medicinal Plant Identification using Machine Learning Algorithm

The identification of medicinal plants plays a crucial role in various fields including pharmacology, traditional medicine, and biodiversity conservation. Traditional methods of plant identification are often time-consuming, labor-intensive, and require expertise in botanical taxonomy. In recent years, the application of Convolutional Neural Networks (CNNs) has shown promising results in automating the process of plant species recognition. This paper provides a comprehensive review of recent advances in medicinal plant identification using CNNs. This study discuss the methodology, challenges, and opportunities associated with CNN-based approaches, as well as their potential applications in pharmacological research and healthcare. Furthermore, we highlight key datasets, architectures, and performance metrics used in CNN-based plant identification systems. Finally, we identify future research directions and potential areas for improvement in this rapidly evolving field

Deutsche Bank Restart: Goodbye Goldman Sachs of Europe?

This public-sourced case describes the latest restructuring efforts by Deutsche Bank (DB) and gives a short history of prior restructuring efforts from the decade before. In July 2019, Christian Sewing, the new CEO of DB, announced a series of measures that included, among others, the elimination of global equity trading, the layoff of 18,000 employees, the creation of a “bad bank” to transfer noncore assets, and the suspension of dividends until 2022. The case describes key decisions a bank CEO makes when a bank needs to change course to return to profitability and growth. The case offers an opportunity to debate these key decisions, as well as discuss some of the prior ones during earlier restructuring efforts, and put the students in the CEO's shoes: What would you do and why? The case also describes key banking performance metrics (e.g., ROE, ROA) and other critical variables such as those reflecting capital health (Tier 1 ratio), as well as gives an overview of the bank business model and factors impacting bank profitability and value.

GPT for medical entity recognition in Spanish

AbstractIn recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.

Car Parking Occupancy Detection Using Deep Learning

It can be difficult to find a parking space in a busy neighborhood because you never know when one will become available. Due to the rise of automobiles and fall in parking spots, this task even more challenging. To increase the dependability of smart parking systems, numerous researchers have sought to improve them. In smart parking systems, computer vision is a more advantageous method for detecting parking spaces than sensors. Many parking spots may be monitored by a single smart camera, which is also reasonably easy to install and maintain. To identify parking occupancy in camera-captured photos, a CNN model is employed. The CNN model will be trained using the CNRpark+Ext and PKLot datasets, which contain 4081 and 12,416 parking lot photos under various weather scenarios, respectively. The CNN architectures, CmAlexNet and mAlexNet, are five-layer designs built on the eight-layer AlexNet architecture, in which processing speed is increased by removing the completely connected layer and the third and fourth convolutional layers. To increase accuracy, the first convolutional layer of CmAlexNet's filter is resized from 11x11 to 13x13, and batch normalization [1] is used instead of the original method. Three key performance metrics are evaluated between CmAlexNet and mAlexNet: recall, precision, and accuracy in classification. Keywords: Car Parking , Computer Vision , Deep learning , CNN, mAlexnet , cmAlexnet , CNRpark dataset , PKLot dataset.

Hydrogen Bond Network Regulation in Electrolyte Structure for Zn‐based Aqueous Batteries

AbstractElectrolyte regulation in Zn‐based aqueous batteries (ZABs) has been extensively reported, and a broad range of strategies has been proposed. However, there is currently a lack of systematic summaries and a comprehensive understanding of the impact of hydrogen bond (H‐bond) networks on electrolyte performance. This work presents the electrolyte structure model, encompassing solvation structure, electrolyte/Zn anode interface, and H‐bond network. Through emphasizing and summarizing the reconstruction, strengthening, and breaking of the H‐bond network within the electrolyte structure, various specific regulation strategies are identified, such as high Gutmann donor number solvent, organic co‐solvent, molecular crowding additives, structure‐breaking ions, and solid‐state design. A critical appraisal is then provided on the key performance metrics influenced by these regulation methods, including Coulomb efficiency, voltage hysteresis, freezing point, and lifespan. This work is expected to illustrate electrolyte structure design to improve the comprehensive performance of ZABs. Last, a data‐driven summary and outlook on hydrogen bond network regulation in electrolyte structure is provided, to objectively evaluate the overall performances of ZABs.

Short waits, happy patients and expert care, moving basic musculoskeletal care from the emergency department to a physiotherapist-led diversion pathway.

Patients with musculoskeletal conditions (MSKCs) are highly prevalent in ED. This project explores the impact of the pilot phase of a 'diversion pathway', which directed patients with MSKCs from the ED waiting room to an outpatient clinic led by advanced-scope physiotherapists. A prospective intervention study comparing care outcomes between patients in the 'diversion pathway' with usual ED care. The characteristics of patients considered eligible and non-eligible are described. Between May and December 2022, 1099 patients were diverted. For diverted patients, mean length of stay (LOS) in ED was reduced by 110 (95% confidence interval [CI]: 99-120) min and 4 h rule compliance improved by 19.3% compared to usual ED care. There were fewer patients who 'did not wait' (DNW) with the diversion pathway. The diverted group was young (median age 22 years and 41% paediatric), mostly low urgency, self-referred and arrived by private transport with minor limb trauma. The diversion pathway triage process appropriately identified 182 patients ineligible for diversion. 96.7% of patients reported satisfaction with care received from the diversion pathway. There was no change in ED representation rates for diverted patients. A new pathway resulted in reduced LOS, reduced DNW, high patient satisfaction and more people being discharged within 4 h for diverted patients compared to usual ED care. The pathway increased ED capacity, improved key ED performance metrics and safely expedited care delivery for patients.

A Multifaceted Approach for Enhancing Anomaly Detection in Industrial Systems with Adaptive Synthetic Sampling and Machine Learning Evaluation

Anomalies in industrial systems refer to deviations from expected behaviour, indicating potential malfunctions, faults, or security breaches. These anomalies can disrupt operations, compromise safety, and lead to costly downtime, making their detection and mitigation critical for industrial reliability and security. This research addresses the imperative task of enhancing anomaly detection within industrial systems by integrating a multifaceted approach encompassing various techniques and methodologies. A primary challenge in anomaly detection, the imbalanced distribution of data, is tackled by employing Adaptive Synthetic Sampling (ADASYN), which dynamically generates synthetic samples for minority classes. This effectively mitigates the impact of class imbalance and substantially enhances detection performance. Additionally, filter-based feature selection methods are utilized, incorporating statistical measures like the Chi-Square Test and ANOVA F-Value to identify pertinent features essential for anomaly detection. The study further delves into supervised anomaly detection approaches, leveraging machine learning algorithms such as Support Vector Machines (SVM) and Gradient Boosting (GB) to differentiate between normal and anomalous activities. These algorithms undergo rigorous evaluation within a Virtual Testing Environment (VTE) simulator, meticulously replicating industrial processes for comprehensive assessment. By leveraging the VTE simulator, this research ensures thorough evaluations across diverse operational scenarios, thereby fortifying anomaly detection systems and advancing the security of industrial systems. The research evaluates anomaly detection models for industrial systems using performance metrics like precision, recall, F1-score, and AUC-ROC. It compares Support Vector Machine (SVM) and Gradient Boosting (GB) models, finding GB outperforms SVM in discrimination ability. Both models show effective anomaly detection with minimal false positives and false negatives. A comparative analysis of key performance metrics aids in selecting the most suitable approach for enhancing system reliability and safety.

Enhancing Anaerobic Performance in Kickboxers: The Strategic Role of Short-Duration Napping

The intricate relationship between sleep and athletic performance has long been a subject of interest in sports science. This study delves into the specific impact of short-duration napping on anaerobic performance parameters in the study of kickboxing, an intense combat sport demanding both physical prowess and strategic acumen. We conducted a controlled investigation with 14 young elite male kickboxers, characterized by an average age of 20.29 ± 0.80 years, a height of 174.48 ± 4.11 cm, body masses of 70.46 ± 7.78 kg, and a body mass index (BMI) of 23.12 ± 2.02 kg/m². The experimental design encompassed three distinct conditions: no napping (N0), a 25-minute nap (N25), and a 45-minute nap (N45). The evaluation focused on several key performance metrics, including countermovement jump (CMJ), mean power, and peak power outputs. Intriguingly, our findings indicated that while CMJ values remained relatively unaffected by the napping conditions, significant variations were observed in both mean and peak power outputs, particularly among participants who napped. These variations suggest that napping, even for short durations, can significantly influence certain anaerobic performance parameters in kickboxers, with a marked improvement in power-related aspects. This research contributes to the expanding body of literature on the role of sleep and rest in athletic performance, specifically highlighting the potential of napping as an effective strategy for enhancing physical capabilities in combat sports. The implications of these findings extend beyond the realm of kickboxing, offering valuable insights and practical applications for athletic training and performance optimization across various sports disciplines where anaerobic capabilities are crucial.

Enhancing Magnetic Material Data Analysis with Genetic Algorithm-Optimized Variational Mode Decomposition

As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications.

Impact of product family complexity on process performance in electronic component assembly

AbstractWith the advent of Industry 4.0 and the impending shift towards Industry 5.0, the integration of human–robot collaboration (HRC) into production systems has become increasingly widespread. This paradigm shift leverages collaborative robots, or cobots, to mitigate physical and mental strain on human workers, thereby increasing productivity and improving overall quality performance. This paper investigates the interplay of productivity and quality factors with assembly complexity in both manual and collaborative assembly systems. The focus is placed on a product family of electronic boards, with varying levels of assembly complexity, to provide a comprehensive comparison between manual assembly and two different collaborative assembly scenarios. Key performance metrics such as assembly time and total defects are evaluated. This case study, rooted in the electronics industry, seeks to provide a valuable perspective on how assembly complexity influences productivity and quality in product family assembly systems. The results of this study aim to contribute to the growing body of knowledge on the implementation of HRC in manufacturing, facilitate informed decision-making and encourage further advances in this rapidly evolving field.

Examining the Influence of Risk Management Committee Dynamics on Financial Performance: A Case Study of Listed Insurance Companies in Nigeria

The Nigerian insurance industry has experienced substantial growth due to factors like increased awareness, regulatory reforms, and technological advancements. This research aimed to explore how the composition, structure, and activities of risk management committees influence key financial performance metrics, focusing on return on assets (ROA). Data from annual reports and audited financial statements of selected insurance companies listed on the Nigerian Exchange Group from 2013 to 2022 were analyzed using both descriptive and inferential statistics. The study revealed that risk management committee size and meeting frequency positively impact financial performance, suggesting that larger committees and more frequent meetings correlate with better financial outcomes. However, factors like committee independence and gender diversity had insignificant effects on financial performance. In conclusion, the presence and effectiveness of risk management committees significantly affect the financial performance of listed insurance companies in Nigeria. The study recommends that insurance companies prioritize diverse committee compositions to benefit from a wider range of expertise and perspectives.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.