Access Technique Research Articles

In-House Immunoglobulin Y-Based Immunoassay for Detecting Benzo[a]pyrene in Grilled Pork Samples

Benzo[a]pyrene (B[a]P) is a hazardous polycyclic aromatic hydrocarbon that accumulates in several environmental matrices as a result of incomplete combustion. Its presence, carcinogenic properties, and tendency for bioaccumulation provide significant risks to human health and the environment. The objective of this study is to create an immunoassay for the detection of benzo[a]pyrene utilizing immunoglobulin Y antibodies. An indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was utilized to develop a speedy, straightforward, sensitive, and economical approach for detecting B[a]P residues. Following the immunization of hens with the hapten pyrenebutyric acid-bovine serum albumin (PyBA-BSA), the IgY antibody extracted from egg yolk was utilized to identify B[a]P residues. To evaluate antibody specificity, six PAH derivatives—PyBA, B[a]P, Chrysene, Benzo[b]fluoranthene, Benzo[a]anthracene, and Benzo[k]fluoranthene—were examined in the experiment to compete for binding with PyBA. The findings indicate that the antibody had considerable affinity for Chrysene (1.15%), Benzo[b]fluoranthene (311.32%), Benzo[k]fluoranthene (10.62%), Benzo[a]anthracene (22.82%), and PyBA (9.55%). Nonetheless, its affinity for B[a]P remained at 100%. The recovery range for grilled pork samples spiked with B[a]P doses of 10.00–0.1 μg/mL was 74.99% to 143.11%. This study utilized a polyclonal antibody, employing the IgY antibody for the inaugural development of an immunoassay to detect benzo[a]pyrene. The ELISA had a higher IC50 value compared to the other immunoassays; however, it yielded good results. This immunoassay signifies a substantial progression in environmental analytical chemistry, offering a cost-effective and accessible technique for the detection of B[a]P to protect human health and the environment.

Red, green, and blue model assessment and AQbD approach to HPTLC method for concomitant analysis of metformin, pioglitazone, and teneligliptin

BackgroundThe CDSCO of India has authorized a combination of metformin hydrochloride, teneligliptin hydrochloride, and pioglitazone hydrochloride for the treatment of insulin-independent diabetes. For the purpose of estimating metformin, teneligliptin, and pioglitazone combinations as well as individual commercial formulations, there are a plethora of publicly accessible chromatographic techniques. More importantly, the development of these chromatographic procedures has included the use of chemical solvents that are dangerous to both animals and the environment.ObjectivesHowever, to date, there has been no documented chromatographic technique that can concomitantly estimate various commercial formulations of drugs under study employing a uniform chromatographic condition and environmentally friendly solvents. In order to concomitantly estimate drugs under study utilizing unified chromatographic conditions, a green HPTLC method was developed.MethodThe AQbD approach was used to carry out the method development. To determine the most important method parameters and response variables, the analytical risk assessment was conducted using the risk priority number ranking and screening approach. Critical method parameters and response variables were modeled using the response surface modeling approach, which relies on the central composite design. Optimal ranges for the intended method operable design region were determined, and control strategy was framed. The chromatographic separation was carried out on preparative TLC plate precoated with silica gel G-60 F254 using 1.0%W/V ammonium acetate in ethanol: water: triethylamine (6.5:0.4:0.6, V/V) as mobile phase. The detection of the anti-diabetic drugs under study was carried out at 267 nm wavelength.ResultsThe linearity of metformin, teneligliptin, and pioglitazone was found to be 5000–25000 ng/band, 200–1000 ng/band, and 150–750 ng/band, respectively. The %RSD for robustness and precision study was found to be less than 2.0%. The %recovery of method was found to be 98–102%. The assay results were shown to be in compliance with respective labeled claims of anti-diabetic medications when the suggested method was used for concurrent analysis of several formulations and combinations of drugs under study.ConclusionThe suggested technique was evaluated utilizing red–green–blue model scoring tools. The suggested technique was determined to be precise, accurate, rapid, cost-effective, and easy to apply for the estimation of drugs under study.

Needling Technique and Management for Hemodialysis access

Needling Technique and Management for Hemodialysis access

An Accessible Surgical Technique for Corneal Dermoid Removal as an Alternative to Enucleation: A Case Report

An Accessible Surgical Technique for Corneal Dermoid Removal as an Alternative to Enucleation: A Case Report

TMET-21. METABOLIC PROFILING OF MENINGIOMA REVEALS NOVEL SUBGROUP-SPECIFIC BIOLOGIC INSIGHTS AND OUTCOME DEPENDENCIES

Abstract BACKGROUND Prior studies have elucidated the presence of four consensus molecular groups (MGs) of meningioma, with unique underlying biology and outcomes. The hyperactivation of metabolic pathways may be associated with tumour growth in so-called hypermetabolic (MG3) tumours, and there is a need to better understand the metabolic profiles of these tumours. This study is the first to study the global metabolon of meningioma in the context of modern molecular subgroups. METHODS We performed untargeted metabolic profiling of 53 meningiomas representing each MG and WHO grade. Prognostic biochemicals were identified using Cox regression and further investigated using RNA and protein-based pathway analyses. A larger cohort with available RNA sequencing (n=121) was used to further explore the prognostic influence of relevant pathways, and biochemicals of interest were validated on a subset of these samples (n=35) using targeted high performance liquid chromatography (HPLC). RESULTS Our untargeted approach identified 560 unique biochemicals for downstream analysis. The abundance of N6-trimethyllysine was associated with significantly earlier time to recurrence highly prognostic on our whole cohort (HR [95%CI] = 3.18 [1.45-26.23], p = 0.004) and within hypermetabolic (MG3) tumours (HR [95%CI] = 6.73 [1.72-6.97], p = 0.006); pyruvate was with worse outcomes in proliferative (MG4) tumours specifically (HR [95%CI] = 5.65 [1.073-29.71], p = 0.041). Analysis of implicated gene pathways demonstrated that upregulation of the oxidative phosphorylation pathway portends worse outcomes in the hypermetabolic subgroup but better outcomes in the proliferative subgroup. By contrast, upregulated lactate transporters were associated with worse outcomes in proliferative, but not hypermetabolic, meningiomas. CONCLUSIONS This is the first study to demonstrate a subgroup-specific prognostic role of N6-trimethyllysine and pyruvate in meningioma, offering increasingly granular outcome predictions using a widely accessible technique (HPLC). In addition, we demonstrated key differences in energy utilization between hypermetabolic and proliferative tumours suggesting fundamental differences in preferred energy utilization and reinforcing a need for subgroup-specific therapies.

A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges

Accessibility in online platforms is a critical concern in our increasingly digital world, where information and services are predominantly accessed through the Internet. The purpose of this systematic review is to provide a comprehensive overview of the current state of the art in online accessibility technologies, and it is focused on key tools such as sign language recognition, speech-to-text, text-to-speech, and voice recognition. Despite advancements in digital inclusivity, numerous technical limitations persist, which limit the accessibility of online content for individuals with disabilities. Our findings indicate that while speech and voice technologies have achieved good accuracies and low word error rates, further research is needed to improve the accuracy and usability of sign language recognition systems, especially for continuous sign language recognition, as they have low accuracy. In this review, we analyzed research articles and publications from well-known databases, including Google Scholar, Elsevier, IEEE Xplore, and Springer. In order to ensure a high standard of quality, we applied the PRISMA 2020 and PEDro methodologies to quantitatively and qualitatively filter the thousands of articles provided by these databases, and we selected only studies that were related to our study. Key areas of investigation included the performance and accuracy of sign language interfaces, speech-to-text, text-to-speech, and speech recognition applications and the compatibility of these technologies with different platforms and devices. This review also explores the role of emerging technologies such as artificial intelligence (AI) and machine learning (ML) in enhancing accessibility and personalizing user experiences. Through a critical analysis of current solutions and a discussion of existing gaps, this paper offers insights into potential improvements and future directions for creating more accessible online environments. The findings might be valuable to researchers and developers dedicated to promoting digital inclusivity and equality.

Pro-Con Debate: Air or Saline for Loss-of-Resistance Technique of Interlaminar Epidural Access with Fluoroscopy-Guided Pain Medicine Procedures

In this Pro-Con commentary article, the Pro side advocates that using the loss of resistance (LOR) to air technique for interlaminar epidural access is safe as the risk of pneumocephalus is exceptionally low. They note that case reports detailing complications typically utilize at least 1 mL of air, frequently exceeding this amount. Even a 1-mL dosage surpasses the requirement, particularly when fluoroscopy is applied in the context of chronic pain management. The proponents also suggest a modified technique, where a syringe containing a drop of saline or contrast acts as a visual cue, minimizing air injection into the epidural space to a negligible quantity. Moreover, they emphasize that air facilitates the identification of any fluid as an indicator of unintended intrathecal access. The Con perspective argues against the utilization of air for epidural access and advocates for the use of LOR with saline instead. This stance is supported by evidence pointing to a greater occurrence of adverse events, such as postdural puncture headache (PDPH) and pneumocephalus, associated with LOR to air compared to LOR with saline. They also argue that saline’s physical properties enhance tactile feedback, potentially contributing to its increased safety. Both saline and air have been used for many years for epidural access via the interlaminar approach. Although both are generally accepted in practice, neither has been formally established as the standard of care. It would benefit the medical community to further delineate the pros and cons of each medium, enabling practitioners to make more informed decisions. This Pro-Con debate will provide 2 perspectives (Table) on the evidence and rationales for the use of air or saline for the LOR technique in interlaminar epidural access with fluoroscopy.

A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Three-dimensional deformation imaging to detect myocardial scar

Abstract Introduction Myocardial scar or fibrosis has significant implications in patient management and prognosis. Currently, cardiovascular magnetic resonance (CMR) is the reference technique for detection and quantification of scar though other imaging modalities have been proven to be useful as well. Purpose This study aims to investigate the usefulness of 3D strain echocardiography to identify myocardial scar in patients with ischaemic and non-ischaemic myocardial fibrosis using late gadolinium enhancement (LGE) CMR as a reference. Methods 149 individuals with ischaemic (89), non-ischaemic scar (26) and no scar (34) on LGE CMR underwent 3D transthoracic echocardiography. The 3D data sets were processed with the 4D LV analysis tool by TOMTEC Imaging Systems GmbH, Germany. The longitudinal, circumferential, radial, and principal tangential strain were calculated. Using a 16 LV-segment model the areas of LGE were assessed on CMR images, and a correlation between 3D strain values and LGE was investigated. Results A multivariate binary logistic regression model was built with the conditional backward elimination method using LGE as the dependent variable. The predictors made a significant contribution to the model (Wald test: 49.985, p<0.001). The Cox & Snell R-square was 0.141, and Nagelkerke R-square was 0.232, suggesting that the final model explains 14.1% or 23.2% of LGE variability. By logistic regression analysis, less negative peak principal tangential strain (P-PTS), peak circumferential strain (P-SC), and end-systolic circumferential strain (ES-CS) increased the odds, whereas increased peak radial strain (P-RS) decreased the likelihood of LGE (Table 1). The LGE extent was calculated as the average LGE transmurality of all 16 segments and was used as the dependent variable in linear regression models. The LGE extent was significantly correlated with all strain indices (Table 2). By stepwise linear regression analysis, P-PTS (B=0.029, 95%CI:0.019-0.040, P<0.001) outperformed the remaining strain variables in terms of linear association with LGE extent. Association between strain indices and the presence or absence of myocardial fibrosis in each of the 16 segments was tested by point biserial correlation (Table 3). PTS and CS indices were associated with myocardial fibrosis in 10/16 segments; RS indices were associated with LGE in 9/16 segments, and LS was not related to the presence of LGE in 15/16 segments. Septal (basal-mid-apical) and mid/apical anterior and inferior segmental strain showed more consistent associations with corresponding myocardial fibrosis. Conclusion 3D strain echocardiography is a promising and easily accessible technique, that can be used to identify areas of myocardial scar as it correlates well with LGE in many LV segments. Echocardiography is a bedside and widely available technique, and it could be used in patients with contraindications or no access to CMR or myocardial perfusion scan.Table 3

Perioperative Point-of-Care Ultrasound (POCUS) for Anaesthetists: Is It Time to Introduce Formal Training?

This article reviews point-of-care ultrasound (POCUS) in the perioperative period. Ultrasound-guided techniques for regional anaesthesia and vascular access have been well-established in anaesthesia for many years. However, integration of whole-body POCUS into UK anaesthetic practice remains limited. There is a growing body of evidence highlighting the benefits of POCUS throughout the perioperative period. Widespread use may help to improve patient care in anaesthesia.

BER Analysis of Underlay Cooperative Cognitive Radio-based NOMA System

Background: The integration of Cooperative Communications (CC), Cognitive Radio (CR) technology, and Non-Orthogonal Multiple Access (NOMA) techniques, termed Cooperative Cognitive Radio used NOMA (CCR-NOMA) systems, has emerged as a promising solution to address spectrum scarcity and connectivity challenges anticipated in sixth generation (6G) networks. Aim: This studyaims to investigate the Bit Error Rate (BER) performance of underlay CCRNOMA systems. Methods: We derive precise closed-form expressions for BER at distant users under perfect and imperfect Channel State Information (CSI) conditions. These mathematical formulations are validated through Monte Carlo simulations. Results: Our results indicate that the near user CU! exhibits superior performance compared to the far user CU!. Additionally, distant users utilizing the CCR-OMA protocol demonstrate better BER performance than those employing the CCR-NOMA protocol. Conclusion: The presence of imperfect CSI adversely affects BER performance. Moreover, the derived closed-form expressions for BER in the investigated system align well with Monte Carlo simulations. These findings provide valuable insights for optimizing the performance of CCR-NOMA systems in real-world scenarios.

Detecting Amyloid Positivity Using Morphometric Magnetic ResonanceImaging.

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive. We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI). Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model. In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions). Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

NOMA Visible Light Communications with Distinct Optical Beam Configurations

Visible light communication (VLC) has been viewed as one promising candidate to mitigate the challenging spectrum crisis and radio frequency interference in future 6G mobile communications and networking. Due to the relatively limited baseband modulation bandwidth of VLC light sources—typically, light-emitting diodes—non-orthogonal multiple access (NOMA) techniques have been proposed and explored to enhance the spectral efficiency (SE) of VLC systems. However, almost all reported NOMA VLC schemes focus on well-discussed applications employing a Lambertian light beam configuration and ignore the potential applications with distinct non-Lambertian optical beam configurations. To address this issue, in this work, the performance of non-Lambertian optical beam configuration-based NOMA VLC is comparatively investigated for future 6G mobile networks. The numerical results demonstrate that, for a fundamental two-user application scenario with the far user located at the corner position, a maximum sum rate gain of about 15.6 Mbps could be provided by the investigated distinct non-Lambertian beam-based NOMA VLC, compared with the maximum sum rate of about 93.3 Mbps for the conventional Lambertian configuration with the same power splitting factor.

Correction of a widespread bias in pooled chemical genomics screens improves their interpretability

Chemical genomics is a powerful and increasingly accessible technique to probe gene function, gene–gene interactions, and antibiotic synergies and antagonisms. Indeed, multiple large-scale pooled datasets in diverse organisms have been published. Here, we identify an artifact arising from uncorrected differences in the number of cell doublings between experiments within such datasets. We demonstrate that this artifact is widespread, show how it causes spurious gene–gene and drug–drug correlations, and present a simple but effective post hoc method for removing its effects. Using several published datasets, we demonstrate that this correction removes spurious correlations between genes and conditions, improving data interpretability and revealing new biological insights. Finally, we determine experimental factors that predispose a dataset for this artifact and suggest a set of experimental and computational guidelines for performing pooled chemical genomics experiments that will maximize the potential of this powerful technique.

Resuscitative endovascular balloon occlusion of the aorta in the patient with obesity.

Palpation of anatomic landmarks is difficult in patients with obesity, which could increase difficulty of achieving femoral access and resuscitative endovascular balloon occlusion of the aorta (REBOA) placement. The primary aim of this study was to examine the association between obesity and successful REBOA placement. We hypothesized that higher body mass index (BMI) would decrease first-attempt success and increase time to successful aortic occlusion (AO). A review of the Aortic Occlusion for Resuscitation in Trauma and Acute Care Surgery (AORTA) registry was performed on patients who underwent REBOA placement with initiation systolic blood pressure >0 mm Hg from years 2013-2022. Patients were excluded if they received cardiopulmonary resuscitation on arrival, underwent open AO, or missing data entries for variables of interest. Body mass index categorization was as follows: non-obese (<30), class I (30-34.9), class II (35-39.9), and class III (40+) obesity. Patients were also stratified by access technique, including use of palpation or ultrasound guidance. Inclusion criteria were met by 410 patients. On binary analysis, no primary outcomes of interest, including rate of success, time to placement, or mortality, were significantly impacted by BMI. Among BMI subgroups, there was no statistical difference in injury severity, admission systolic blood pressure (SBP), or augmented SBP. At initiation of aortic occlusion, patients with class II and class III obesity had higher median SBP compared with non- and class I obese patients (p = 0.03). Body mass index subgroup did not impact likelihood of first-attempt success or conversion to open procedure. When stratified by access technique, there was no difference in success rates, time to success or mortality between groups. Body habitus did not impact success of REBOA placement, time to successful AO, or mortality. Further, ultrasound guidance was not superior to landmark palpation for arterial access. Following traumatic injury without hemodynamic collapse, obesity should not deter providers from considering REBOA placement. Therapeutic/Care management, Observational, Cross-sectional; Level IV.

National Sentiments and Regional Flavour- A Socio-economic Study of Huvina Hadagali Jasmine

Background This socio-economic analysis studies the influence of jasmine production on the economic well-being of farmers in Huvina Hadagali, a region known for its high-quality jasmine flowers. The Vijaya Nagara district’s Havina Hadagali area is well known throughout the country for its jasmine flower farming. In addition to being referred to as Mallige Nadu, this location is also known as Malligeya Tavaru. The cultivation of the jasmine flower is protected by the Geographical Indication (GI) Tag, and this flower has been popular in this region for a substantial amount of time. Methods Data was collected from a sample of 364 jasmine growers using a structured questionnaire in Huvina Hadagali, Vijayanagar district. The data focused on different socio-economic factors such as income levels, employment, market access, and agricultural techniques. The study is analysed using IBM SPSS through frequency analysis and 2-step clustering. Result The results demonstrate that the cultivation of jasmine makes a substantial contribution to the local economy, serving as a main or additional source of income for numerous households. Jasmine farming often contributes 40% of the whole household income, and during peak seasons, it provides significant economic advantages. Nevertheless, the highlighted obstacles were volatile market pricing, pest infestations, and limited access to contemporary farming practices. The study emphasises the crucial significance of cooperative societies and local marketplaces in stabilising income and offering essential resources and training to farmers. Conclusion The research highlights the necessity of governmental interventions focused on developing market infrastructure, offering financial assistance, and improving access to agricultural innovations to maintain and augment the economic advantages of jasmine cultivation in Huvina Hadagali.

Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.