Average Delay Research Articles

The analysis of educational informatization management learning model under the internet of things and artificial intelligence

This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model’s performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.

Research on paging enhancements for 5G-A downlink transmission energy saving

5G-Advanced (5G-A), an evolutionary iteration of 5G, effectively enhances 5G services. The increasing complexity in downlink services scenarios stresses the necessity for research into the integration of efficient communication with low-carbon solutions. Historically, there has been an emphasis on reliability and precision, at the expense of power consumption. Although energy-saving technologies like Idle mode-Discontinuous Reception (IDRX) and Paging Early Indication (PEI) have been introduced to reduce power consumption in UE, they have not been fully tailored to the paging characteristics of 5G-A downlink services. In this paper, we take full account of the impact of paging message density on energy saving measures and propose an enhanced paging technology, termed Predictive-PEI (PPEI), which is designed to reduce UE overhead while minimizing latency whenever possible. Towards this end, we design a dual threshold decision framework founded on machine learning, mainly involving two steps. We first use the LSTM-FNN neural network to forecast the arrival moment of upcoming paging messages based on past real information. Then, the output of the initial prediction is as the input of the next dual threshold decision algorithm, to determine the optimal moment for transmitting the PEI. The restrictive factors, encompass average delay threshold and cache capacity threshold, playing a role in decisions regarding paging message caching and decoding. Compared to the existing schemes, our PPEI scheme flexibly sends efficient PEI according to the actual paging characteristics by introducing machine learning, resulting in substantial power savings of up to 38.89% while concurrently ensuring effective latency control.

CEREBRAL RADIONECROSIS IN PATIENTS IRRADIATED FOR CAVUM CANCER: RETROSPECTIVE STUDY OF 15 CASES

Introduction: Cerebral radionecrosis is a rare but a serious complication induced by radiotherapy of the nasopharyngeal cancer. Since nasopharyngeal cancer is common to have skull base infiltration, radiation fields cover inevitably the temporal lobes despite the use of the more advanced intensity-modulated radiotherapy (IMRT). Even though the development of imaging means and the systematic practice of CT and MRI in post-therapeutic monitoring, the diagnosis and treatment of cerebral remain challenging. Materiel and Method:A retrospective study including fifteen cases of cerebral radionecrosis occurring after radiotherapy of cavum cancer, based on data from 382 irradiated for cavum cancer during the period from January 2017 to January 2022 and who were regularly followed in post-therapeutic monitoring. Results: The incidence of cerebral radionecrosis in our patients was 3,9%. The average age of patients was 47.5 years (range 28 - 62 years), with a female predominance (sex ratio: 4M/11F = 0.36). All our patients were followed for locally advanced UCNT of the cavum (73% stage IVA and 27 % stage III). 80% of cases have benefited from conformal radiotherapy technique by intensity modulation (VMAT archtherapy). And all cases have had concomitant chemotherapy. Cerebral radionecrosis occurred after an average delay of 31.6 months (11-77 months) from the end of irradiation. The location of the cerebral radionecrosis lesions was temporal in all our patients. Exploring dosimetric data of our patients, we noted that in 58% of cases the dose constraint at the level of the temporal lobe affected by radionecrosis was not respected. Cerebral radionecrosis was initially discovered on MRI of the cavum, a brain MRI was subsequently requested to show areas of cerebral radionecrosis. Spectroscopy was carried out in 33% of patients. Cerebral radionecrosis was treated with corticosteroid therapy in 53% of cases. After a mean follow-up of 19.8 months (2-63 months) the evolution of cerebral radionecrosis in patients irradiated fornasopharyngeal cancer, was marked by radiological stability without the appearance of clinical symptoms in 53% of cases. Conclusion: Cerebral radionecrosis of radiation-treated cavum patients could be a devastating complication. Furthermore, the differential diagnosis remains challenging and may require the use of advanced imaging modalities. The intricate relationship between dose parameters and radionecrosis incidence requires large-scale longitudinal studies. Despite the fact that there is no definitive treatment for cerebral radionecrosis, bevacizumab has proven to be an encouraging treatment option.

Behavioural interventions to treat anxiety in adults with autism and moderate to severe intellectual disabilities: The BEAMS-ID feasibility study.

The aim of this feasibility study was to adapt and model a behavioural intervention for anxiety with autistic adults with moderate to severe intellectual disabilities. Twenty-eight autistic adults with moderate or severe intellectual disabilities, 37 carers, and 40 therapists took part in this single-group non-randomised feasibility study designed to test intervention feasibility and acceptability, outcome measures, and research processes. The intervention was judged as feasible and acceptable by autistic adults with intellectual disabilities, carers, and therapists. Minor intervention revisions were suggested. Carers completed 100% of outcome measures and the missing data rate was low. Complying with legislation governing the inclusion of participants who lack capacity to decide whether they wanted to take part in this study led to an average 5-week enrolment delay. The intervention and associated study processes were judged to be feasible and acceptable and should now be tested within a larger randomised trial.

Quantifying the vulnerability of road networks to flood-induced closures using traffic simulation

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

A Fault Recovery Method for Distribution Networks With Distributed Power Sources Based on 5G Smart Terminal

Firstly, a smart terminal is designed based on 5G communication technology, which is embedded with various protection measurement devices to form a 5G smart terminal. A distributed control model for the distribution network is constructed based on 5G smart terminals, and an objective function is designed taking into account factors such as restoring important loads, surplus power supply in the distribution network, network losses, and the number of switch actions. Finally, the Sparrow Search Algorithm (SSA) is improved using elite reverse learning strategy and dynamic sine perturbation strategy, and it is used to solve the objective function. Based on the IEEE33 distribution network node system, experimental tests are conducted, and the results show that the average delay of 5G smart terminals is about 30ms. Taking 14:00 as an example, the proposed method achieves network loss, number of switch actions, recovered important load power, and power supply surplus of 46.39 kW, 3 times, 185.65 kW, 240.28 kW, respectively. The fault recovery effect of the distribution network is ideal.

NUMERICAL-ANALYTICAL DELAY MODEL BASED ON QS WITHOPERATIONAL SHIFT OF DISTRIBUTION PRINCIPLES

This article demonstrates results for a queuing system formed by right-shifting an Erlang distribution and a second-order hyperexponential distribution. As is known, the first distribution provides a coefficient of variation less than one, and the second one – more than one. From the general queuing theory, it is known that the average delay of requests in the queue in the QS G/G/1 is related to the coefficients of variation of arrival intervals and service times by a quadratic dependence, and the system we are considering belongs precisely to this type. An operational shift in the distribution laws leads to a multiple reduction in delay compared to a conventional system, and this value depends on the value of the shift parameter. To construct a mathematical model of the delay, the method of spectral solution of the Lindley integral equation for the system under consideration was applied. For the practical application of the obtained results, the standard method of oprobability theory moments is used. The obtained results of numerical and analytical modeling in Mathcad clearly confirm the adequacy of the proposed mathematical delay model.

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

Optimizing Dijkstra's Algorithm for Managing Urban Traffic Using Simulation of Urban Mobility (Sumo) Software

Among the challenges of urbanization is traffic management as a measure of growth and progress. Recent population growth has resulted in a significant increase in vehicles, causing traffic jams that are challenging for the existing transportation networks. This congestion affects other services, including public transit, airports, road maintenance, and pollution caused by emissions of CO2 and other gases. Furthermore, it doubles the amount of fuel used. This has negative consequences for society as well as economic losses. This paper focuses on an improved Dijkstra algorithm based on traffic congestion levels to address the above problems. Improved Dijkstra algorithm can provide (a) real data collected from the map via OpenStreetMap, (b) Add four features to SUMO(Simulation of Urban Mobility) simulator software (time period, rush-hour, number of vehicles, and routing algorithm), (c) it could know congestion level for roads (d) rerouting vehicles to avoid traffic congestion. Based on the simulation results and analysis presented in the paper, it was found that the proposed improved Dijkstra algorithm increased the performance of the road traffic flow by reducing the number of related vehicles in traffic congestion and average delay time for experiment scenarios.

Epidemiology, outcomes and prognosis of infective endocarditis in Northern Morocco

IntroductionInfective endocarditis is a rare but potentially severe disease, associated with significant morbidity and mortality. Our study aims to describe the epidemiology and management aspects of endocarditis in northern Morocco and compare it with international management guidelines.Materials and MethodsThis is a retrospective study involving all patients hospitalized in the cardiology department of the University Hospital of Tangier for infective endocarditis over a period of 4 years and 7 months, from May 2019 to February 2024.ResultsEighty patients were hospitalized for IE during the study period. The average age of the patients was 46 years, with an even sex ratio. IE concerned native valves in 77% of cases, mechanical prostheses in 19% of cases, and on bio prostheses in 4%. The average diagnostic delay was 25 days. Blood cultures were negative in 59% of cases. The predominant infective microorganism was the bacteria Staphylococcus (65.6%).Imaging results showed vegetations in 76.3% of cases, predominantly on the mitral valve (39.3%), followed by the aortic valve (21.3%). The main complications included heart failure (51.2%), peripheral arterial embolisms (22.5%) and splenic infarction (17.5%).Management wise, the most commonly used antibiotic therapy was a combination of ceftriaxone and gentamicin. Clinical and biological improvement was observed in 70% of cases, with a mortality rate of 12.5%. Twelve patients underwent surgery (15%). Urgent surgery was indicated in 66,7% of the operated patients.ConclusionOur study highlights the challenges in managing infective endocarditis in northern Morocco. The prognosis of infective endocarditis can be improved through multidisciplinary management within the implementation of an Endocarditis Team.

Coordinated Control Method for Unequal-Cycle Adjacent Intersections Using Vehicle–Road Collaboration

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections.

CORE: Counteracting overwhelming requests effectively—A method for interest flooding attack mitigation in NDNoT

<p>The<strong> </strong>Named Data Network (NDN) is a promising architecture for the near future. NDN communicates by naming data, unlike IP-based Internet architecture. Architectural understandability, ease of use, security, content presentation, and simplicity of data exchange logic make this architecture preferable. The NDNoT approach, which recently combines IoT and NDN, enables Internet of Things applications using NDN naming conventions and basic data structure. However, increasing technological applications bring security vulnerabilities. In this study, we propose a new method called CORE that will secure the intended data transfer. The presented CORE mechanism was developed as a countermeasure against the Interest Flooding attack, one of the NDN security attacks. Tests were carried out in the Cooja simulation environment using three different topology scenarios. CORE’s performance metrics were evaluated based on success rate, average delay, and interest traffic. The results showed that when the CORE mechanism was active, there was an improvement in the success rate and average delay. In terms of interest traffic, at least a 70% success rate was achieved compared to scenarios in which the CORE mechanism was not operated.</p>

Bioinspired molecular communications system for targeted drug delivery with IoBNT-based sustainable biocyber interface

This paper presents a sustainable biocyber interface approach for an intelligent targeted drug delivery system (TDDS). The proposed approach employs a bioinspired molecular communications (MC) system in which molecules are used as information carriers and a multi-input multi-output (MIMO) Forster resonance energy transfer (FRET) nanorelay to efficiently transfer therapeutic drugs from extravascular (outside blood vessels) to intravascular (within blood vessels) sites that are targeting diseased tissue. The proposed approach consists of a number of bio-nano things (BNTs) implanted inside the human intra-body nanonetwork near the diseased site. The communication among BNTs is accomplished by a sequential multistep FRET, and the nanonetwork is controlled by the Internet of Bio-Nano Thing (IoBNT) paradigm sustained by an uplink/downlink biocyber interface. The bit error rate (BER) and molecular channel capacity are utilized to evaluate the performance of the proposed approach. In addition, a Markov model decision in terms of the number of nanomachines is presented to calculate the successful probability of drug delivery and the average delay time. The numerical results show how effective the suggested method is at transmitting therapeutic medication information with an attainable bit-rate error.

Pengaplikasian Data Analisis pada Data Penundaan Penerbangan dan Kinerja Maskapai di Suatu Bandara

The aviation industry is growing very significantly due to increased mobility, economic expansion and current technological advances. However, as mobility increases and the number of airline passengers increases, airlines experience problems in overcoming flight schedule delays or in other words delays. Data analysis plays an important role in processing large amounts of data to find hidden insights in data. The aim of writing this service journal is to identify days with the highest average minutes of delay, months that experience the highest average delays, and at what times flight delays often occur. . This service object uses secondary data obtained from the Kaggle site. It is hoped that this journal can be used as material for consideration in decision making and improving the operational performance of each airline involved.

Method of allocation of information streams in the digital communication system with usage the generalized index

In conditions of constantly increasing intensity of input traffic and increasing requirements for the quality of service of heterogeneous information in digital communication systems (DCS), significant overloads often arise in individual switching nodes (SM) and in entire segments of the system, which lead to an unacceptable decrease in the efficiency of information exchange ( AND ABOUT). In this case, a contradiction arises between the available channel-network resource of the DSS and the possibility of its effective use to provide information output with the required quality under conditions of high input traffic. Resolution of this contradiction is possible based on the implementation of a set of measures that ensure optimal distribution of information flows (routing) taking into account the capabilities of the DCS for transmitting and storing information. However, existing approaches, algorithms and implemented routing protocols are based on the use of local metric parameters, which do not allow to fully resolve the noted contradiction. Thus, it is urgent to search for new ways to effectively distribute information flows, allowing to improve a number of important characteristics of the information source (average time delay of a packet, number of losses, performance of the digital network). It should be noted that to implement these methods, it is necessary to use a generalized indicator of information efficiency, which at the network level would simultaneously characterize the capabilities of the central network for transmitting and storing information. Ultimately, solving this problem is of great scientific and practical importance for ensuring the timeliness and reliability of information transfer to the DCS.

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

Reliability mapping of 5G low orbit constellation network slice is an important means to ensure link network communication. The problem of state space explosion is a typical problem. The deep reinforcement learning method is introduced. Under the 5G low orbit constellation integrated network architecture based on software definition network (SDN) and network function virtualization (NFV), the resource requirements and resource constraints of the virtual network function (VNF) are comprehensively considered to build the 5G low orbit constellation network slice reliability mapping model, and the reliability mapping model parameters are trained and learned by using deep reinforcement learning, solve the problem of state space explosion in the reliability mapping process of 5G low orbit constellation network slices. In addition, node backup and link backup strategies based on importance are adopted to solve the problem that VNF/link reliability is difficult to meet in the reliability mapping process of 5G low orbit constellation network slice. The experimental results show that this method improves the network throughput, packet loss rate and intra slice traffic of 5G low orbit constellation, and can completely repair network faults within 0.3 s; For different number of 5G low orbit constellation network slicing requests, the reliability of this method remains above 98%; For SFC with different lengths, the average network delay of this method is less than 0.15 s.

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

Specialist referrals and diagnostic delays in motor neurone disease: Mapping patients’ journey through hoops and hurdles in healthcare

Motor neuron disease (MND) is an uncommon but invariably fatal condition, with a median survival of 24–48 months from symptom onset. Although there is no cure at the moment, early diagnosis is crucial to enable timely access to multidisciplinary care, and enrolment in clinical trials utilising investigational therapies. Unfortunately, diagnostic delays remain common, and the average delay between symptom onset and diagnosis is 12 months. Large numbers of specialist referrals have been suggested as a key contributor to diagnostic delays.We conducted a retrospective review of the medical records of patients diagnosed with MND in Lancashire and South Cumbria, to investigate whether large numbers of specialty referrals are a common occurrence in MND.Our review identified that 35% of patients with MND were seen by two or more specialties before being referred to neurology. This rose to 49% when patients with bulbar onset disease were considered. 9% of cases saw three or more specialists. There was a statistically significant correlation between the number of specialist referrals and delays in neurology referral. We hope our findings will increase awareness of the importance of early neurology referral in the diagnosis of MND and promote the use of the MND Red Flag tool as a means of identifying patients in need of prompt neurological evaluation.

Alpine songbirds at higher elevations are only raised with a slight delay and therefore under harsher environmental conditions.

The breeding phenology of birds is often timed to coincide with a peak in food availability. However, the shortening of the vegetation period with increasing elevation may force bird species at high elevations to breed earlier in relation to optimal environmental conditions due to time constraints. We investigated differences in fledging dates in five Alpine woodland songbird species along an elevational gradient from 1500 to 2200 m in Switzerland. We estimated fledging dates from a nationwide citizen science bird monitoring dataset and used the date when the proportion of observations of 'fledged young' reached 50% among all observations indicating breeding behaviour. This measure had the advantage that we could estimate average timing of the broods across a wide geographic range and over many years without the need to search for individual nests. We then compared differences in timing of the broods with climatic conditions and larch budburst across different elevational bands. The daily mean air temperature of 10-15°C was reached 34-38 days later at 2200 m compared to 1500 m, which is a similar delay as found in previous reports on snow melt-out date. The average delay in larch budburst was 19.2 days at 2200 m compared to 1500 m. In comparison, the average timing of the birds' broods was only 5.4 days later in coal tits and 0.5 days later in Alpine tits at 2200 compared to 1500 m (the two species for which we had the narrowest interval estimates). Also, the estimated delay at higher elevations in the broods of song thrushes, mistle thrushes and Eurasian chaffinches was relatively small. Rather than postponing breeding dates to better environmental conditions later in the season that would match the earlier conditions at low elevation, songbirds breeding at higher elevations may thus have evolved adaptations to cope with the harsher conditions.

N-DIBL optimization of NC-GAAFET NW for low power fast switching applications

Gate-all-around field effect transistors (GAAFETs), exhibit improved SCEs, are proposed to replace conventional FinFET in scaled nanodevices owing to excellent gate control. The voltage scaling concept is embodied in negative capacitance (NC) which provides same on current at reduced voltage. The proposed NC-GAAFET yields 5.31 times larger ION and IOFF is significantly reduced by ⁓105 orders, which is due NC effect, compared to baseline NW. SSavg for the NC-GAAFET is 33mV/dec which surpasses Boltzmann tyranny and manifests steep subthreshold behavior. Effect of FE thickness (tfe) variations on DIBL has been explored and found to be negative, which improves SCEs. The negative-DIBL for device has been found to be −20 mV/V at tfe = 6 nm. Furthermore, a CMOS inverter circuit employing the NC-GAAFET has been presented that provides an average propagation delay of 174 fS which is 47 % lesser as compared to that of baseline device. The NC-GAAFET NW findings fulfill the quest of low power fast switching device for digital applications.