Power Usage Research Articles

Design analysis of a low-power, high-speed 8 T SRAM cell using dual-threshold CNTFETs

Recently, carbon nanotube field-effect transistors (CNTFETs) have garnered significant attention from VLSI engineers due to their exceptional electrical properties. This paper proposes a novel high-speed, low-power eight-transistor (8 T) static random-access memory (SRAM) cell based on 32-nm CNTFET technology. The SRAM cell was simulated using the HSPICE tool with a VDD of 0.9 V. The high-speed and low-power characteristics of the SRAM design are attributed to the high subthreshold slope and high carrier mobility of metal-oxide-semiconductor field-effect transistor (MOSFET)-like CNTFETs utilized in the simulations. The implementation of dual threshold transistors, coupled with a transmission gate for bitline access, contributes to the enhanced performance. Key performance metrics such as noise margins, power consumption, delay, and SRAM electrical quality metric (SEQM) of the proposed SRAM have been evaluated and compared with existing CNTFET-based SRAM designs. The proposed cell demonstrates reductions of 73.73%, 43.18%, and 58.70% in read power, write power, and hold power, respectively, compared to the lowest respective power values of other examined SRAM designs. The proposed SRAM ranks second, third, and second in write static noise margin (WSNM), hold static noise margin (HSNM), and read static noise margin (RSNM), respectively, among other designs. Additionally, the proposed SRAM exhibits the least sensitivity to parametric variations compared to other designs. The SEQM, which provides a comprehensive assessment of access times, noise margins, and power usage for the SRAM cell, has been calculated. The SEQM of the proposed SRAM is 10.6, 1.89, 13.15, and 1.82 times higher than that of C6T, BLP8T, Mani’s 10 T, and LP8T, respectively.

Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.

Turning Data Center Waste Heat into Energy: A Guide to Organic Rankine Cycle System Design and Performance Evaluation

This study delves into the adoption of the organic Rankine cycle (ORC) for recovering waste heat from data centers (DCs). Through a literature review, it examines energy reuse with a focus on electric power generation, the selection of working fluids, and system design principles. The objective is to develop a thorough framework for system design and analysis, beginning with a quantity and quality investigation of waste heat available. Air cooling systems, chosen often for their simplicity, account for about 70% of used cooling methods. Water cooling demonstrates greater effectiveness, albeit less commonly adopted. This study pays close attention to the selection of potential working fluids, meticulously considering the limitations presented by the available sources of heat and cold for vaporization and condensation, respectively. It reviews an ORC-based system setup, incorporating fluid streams for internal processes. The research includes a conceptual case study where the system is designed and simulations are conducted in the DWSIM environment. The simulation model considers hot air or hot liquid water returning from the data center cooling system for ORC working fluid evaporation. Ambient water serves for condensing, with pentane and isopentane identified as suitable organic fluids. Pentane assures ORC net electric efficiencies ranging between 3.1 and 7.1% when operating pressure ratios increase from 2.8 to 6.4. Isopentane systems, meanwhile, achieve efficiencies of 3.6–7.0% across pressure ratios of 2.7–6.0. Furthermore, the investigation provides key performance indicators for a reference data center in terms of power usage effectiveness (PUE), energy reuse factor (ERF), energy reuse effectiveness (ERE), and greenhouse gas (GHG) savings. This study concludes with guidelines for system analysis, including exergy considerations, and details the sizing process for evaporators and condensers.

Optimizing Power Consumption in Machining Nickel-Based Superalloys: Strategies for Energy Efficiency

<div>In the face of the world’s population growth and ensuing demands, the industrial sector assumes a crucial role in the management of limited energy supplies. Superalloys based on nickel, which are well-known for their remarkable mechanical qualities and resilience to corrosion, are now essential in vital applications like rocket engines, gas turbines, and aviation. However, these metals’ toughness presents a number of difficulties during machining operations, especially with regard to power consumption. This abstract explores the variables that affect power consumption during the machining of superalloys based on nickel in great detail and suggests ways to improve energy efficiency in this area. The effects of important variables on power consumption are extensively investigated, including cutting speed, feed rate, depth of cut, tool geometry, and cooling/lubrication techniques. A careful balance between these factors is necessary to maximize machining efficiency and reduce power usage. Furthermore, this study reviews the effect of different heat source applications on power consumption and the resultant quality of machined nickel-based superalloys. Additionally, the critical role of cooling and lubrication in mitigating the adverse effects of high temperatures generated during machining is thoroughly examined. Innovative cooling strategies, including cryogenic or high-pressure coolant systems, are explored as potential avenues to enhance heat dissipation and minimize power requirements. In essence, this abstract not only sheds light on the challenges inherent in machining nickel-based superalloys but also offers actionable insights into how energy efficiency can be maximized through strategic parameter optimization and the adoption of innovative cooling techniques. By addressing these aspects, manufacturers can effectively navigate the complexities of machining superalloys while minimizing their environmental footprint and operational costs.</div>

Research on the Coordinated Trading Mechanism of Demand-Side Resources and Shared Energy Storage Based on a System Optimization Model

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism.

A reinforcement learning-based online learning strategy for real-time short-term load forecasting

Real-time Short-Term Load Forecasting (STLF) is crucial for energy management and power system operations. Conventional Machine Learning (ML) methodologies for STLF are often challenged by the inherent variability in energy demand. To tackle the challenge associated with inherent variability, this paper presents a novel Reinforcement Learning (RL)-enhanced STLF method. Different from conventional methods, our method dynamically improves the STLF model by selecting optimal training data to capture recent power usage trends and possible variations in demand patterns. By doing so, our method can significantly reduce the impact of unforeseen fluctuations in real-time forecasting. In addition to the novel RL-enhanced STLF method, we propose a comprehensive evaluation framework, encompassing three key dimensions: accuracy, runtime efficiency, and robustness. Tested on three distinct real-world energy datasets, our RL-enhanced method demonstrates superior forecasting performance across three evaluation metrics by achieving accurate and robust predictions in real-time under varying scenarios. Furthermore, our approach provides uncertainty bounds for practical predictions applications. These results underscore the significant advancements made by our RL-based method in forecasting precision, efficiency, and robustness. We have made our algorithm openly accessible online to promote continued development and advancement of STLF methods.

Retrofitting HVAC systems for enhanced energy performance and resilience in university data center room

Data centers have been extensively implemented globally to satisfy the increasing need for IT services. Small data centers with insufficient cooling require much power to keep IT equipment running reliably. As a result, retrofitting has become an efficient method to increase energy efficiency and save operating costs. This study examines the impact of retrofitting on energy efficiency in a data center room at a university. The data center under consideration underwent a comprehensive retrofitting process. Implementing a closed in-row type data center increases cooling capacity, resulting in more efficient cooling and improved temperature control. Measurement data were collected before and after retrofitting, including power usage effectiveness (PUE), energy consumption, temperature, and velocity distribution. Computational fluid dynamics (CFD) simulations were also conducted to determine the characteristics of the data center's airflow distribution and temperature profile. Numerical simulations carried out for two operational situations have indicated that a slight modification has the potential to greatly enhance the efficiency of the cooling procedure. Numerical simulations have revealed that developing hotspots in the racks has resulted in air temperatures exceeding the allowable maximum temperature, which can be improved after retrofitting. The PUE of the data center before/after retrofitting was 2.17 and 1.52, respectively. Free cooling technology is also implemented, making the data center more efficient and reducing PUE to 1.32. The results showed a significant improvement in energy efficiency, with a PUE reduction of 0.85, which reveals that energy efficiency has been improved by 48.71 %. The retrofitting results in significant cost savings and environmental benefits.

An Experimental Investigation Of µUAV Rotor Wingtip Vortices Through High Spatial Resolution PIV

The interest for µ UAVs platforms with VTOL capabilities dramatically rose over the last decade due to the fast-paced advancements in electronics and control. However, the vortical structures shedded from the rotary wings -and their interaction-, which are responsible for high power usage and high noise level emissions, are yet to be fully understood. In this work, an experimental PIV campaign was conducted on a commercially available propeller with a 230mm diameter. The operating angular velocity was chosen to be 5750RPM, producing a chord Reynolds number of about Re_{c,75%} ≃ 11000, generating 3.42N of thrust while requiring 55W of power. The resolution of the PIV setup was able to reliably fit 8 measurement points in the vortex core when using narrowest field-of-view (FOV), while the widest FOV comprises a measurement area of about 0.5D×0.57D. Time averaged as well as phase averaged measurements were performed. The evolution and interaction of the wingtip vortices and shear layers shedded in the flow field is captured with an azimuthal angle step increment of ∆Ψ = 5º. The thrust produced by the multirotor configuration was indeed found to be approximately 5% lower than the one produced by the isolated propeller in hovering conditions. Additionally, the PIV setup successfully met the target spatial accuracy requirements: induced velocities are accurately captured in every stage of the wingtip vortex evolution.

Synthesis of tantalum oxide thin films using RF magnetron sputtering for RRAM application

Resistive Random Access Memory (RRAM) is a prominent contender in the realm of emerging non-volatile memory technologies owing to its numerous advantages like simple structure, low power usage, high speed, and exceptional scalability. In the current study, RRAM device was fabricated using tantalum oxide (TaOx) as a resistive switching insulating oxide layer, which was sandwiched between two conducting electrodes, namely Ag and ITO. RF magnetron sputtering was used to deposit the insulating layer of TaOx at room temperature on the ITO substrate. Further, X-ray reflectivity and hard X-ray photoelectron spectroscopy analyses were conducted to find the thickness, roughness, and elemental composition of the deposited TaOx films. Using a semiconductor analyzer, the RRAM cell's first DC switching characteristics were determined. The data acquired exhibited a resistance ratio (R HRS/R LRS) of 120 showing a good distinction between the two-resistance states. Additionally, a thorough investigation was conducted to examine the electrical conduction mechanism occurring inside the fabricated RRAM device.

Research on key technologies of digital sensing and fault warning of isolation switch in GIS

Abstract GIS device is a small electrical device. It is widely used in power systems due to its small size, easy installation, reliable operation, and anti-noise. In recent years, GIS isolation switches have frequently caused power grid accidents due to their own reasons, causing great economic losses to the power grid and users. Therefore, in GIS, in order to improve the stability of the power system, the isolation switch in GIS must be monitored and fault diagnosed. This article analyzes the primary and secondary working mechanisms of GIS. Starting from the basic principles of primary and secondary, this paper conducts a theoretical analysis of various parameters in GIS, and conducts a simple analysis of its sensing performance and monitoring system. It also briefly introduces common faults in GIS, providing a theoretical basis for research on key technologies of digital sensing and fault warning of isolation switch in GIS.

Towards Carbon Footprint Management in Hybrid Multicloud

Enterprises today aspire to optimize the operating costs and carbon footprint (CFP) of their IT operations jointly without compromising their business imperatives. This has given rise to a hybrid approach in which enterprises retain the dynamic choice to leverage private data centers and one or more public clouds in conjunction. While cloud service providers (CSPs) have long provided APIs for estimating, reconciling, and optimizing operating costs, they have only recently started exposing APIs related to CFP. Indeed, this is a step in the right direction. Nevertheless, our analyses of these APIs reveals many gaps that need to be addressed to facilitate sizing and placement decisions that can factor in carbon. First, there is a lack of standardized, transparent methodology for CFP quantification across different CSPs. Second, the coarse granularity of the CFP data provided today can help with post-facto reporting but is not suitable for proactive fine-grained optimization. Last, enterprises themselves are unable to independently compute the current CFP or estimate potential CFP savings since CSPs do not share the required power usage data. To address these gaps, enterprises have started developing their own carbon assessment methodologies and tools to estimate the CFP of workloads running on public clouds using the available user-facing APIs. These systems hold the promise for an independent and unbiased evaluation and estimation of relative savings between different deployment options by cloud users. We describe and analyze the details of CSP-native carbon-reporting tools and their quantification methodology, and the "outside-of-the-cloud" estimation approaches. Finally, we present opportunities for future research in the direction of trustworthy, fine-grained, public cloud workload CFP estimation, which is a prerequisite for meaningful realization of carbon optimization.

Quantum‐safe Lattice‐based mutual authentication and key‐exchange scheme for the smart grid

AbstractThe smart grid network (SGN) is expected to leverage advances in the Internet of Things (IoT) to enable effective delivery and monitoring of energy. By integrating communication, computing, and information tools like smart sensors and meters to facilitate the process of monitoring, predictions, and management of power usage, the SGN can improve competence of power‐grid architecture. However, the effective deployment of IoT‐powered SGNs hinges on the deployment of strong security protocols. With the advent of quantum computers, classic cryptographic algorithms based on integer factorization and the Diffie‐Hellman assumptions may not be suitable to secure the sensitive data of SGNs. Therefore, in this paper, a secure quantum‐safe mutual authentication and key‐exchange (MAKe) mechanism is proposed for SGNs, that make use of the hard assumptions of small integer solution and inhomogeneous small integer solution problems of lattice. The proposed protocol is intended to offer confidentiality, anonymity, and hashed‐based mutual authentication with a key‐exchange agreement. Similarly, this scheme allows creation and validation of the mutual trust among the smart‐meters (SMs) and neighbourhood‐area network gateway over an insecure wireless channel. A random oracle model is then used to perform the formal security analysis of the proposed approach. A thorough formal analysis demonstrates proposed algorithm's ability to withstand various known attacks. The performance analysis shows that the proposed approach outperforms other comparative schemes with respect to at least 22.07% of minimal energy utilization, 51.48% effective storage and communications costs, as well as 76.28% computational costs, and thus suitable for resource‐constrained SGNs.

IMPACT OF LOADS ON LOCOMOTIVE MOTOR AXLE BEARINGS ON THE CONDITIONS OF FORMATION OF OIL FILM ON FRICTION SURFACES

During the active operation of locomotives, a significant portion of failures (up to 20 %) is attributed to the malfunction of motor-axial bearings. This is due to the imperfection of the lubrication system of the motor-axial bearings, the proper functioning of which largely depends on various factors such as climatic conditions, the level of wear of lubricating oil, and lubrication devices, among others. There is a need to improve the existing lubrication systems for motor-axial sliding bearings. One of the methods to address this issue involves examining the factors influencing the lubrication process of the friction surfaces of motor-axial bearings and the conditions of lubricant material supply. The article provides calculations of the loading forces on the motor-axial bearings, their action vectors, and analyzes the impact of these parameters on the ability to form an oil film on the friction surfaces of motor-axial bearings. To determine the magnitude of the load, technical specifications of the locomotive were used, and the relationship between power usage and locomotive speed was analyzed. The study involved an analysis of recent publications regarding the generation of contact pressure force between the contact surfaces of friction pairs, the magnitude of which exceeds permissible limits, considered one of the primary factors leading to bearing failure. Additionally, the existing bearing lubrication system used on the locomotive was analyzed, its shortcomings were identified, and methods for addressing these issues were discussed. The calculations were performed for the 2TE116 locomotive with traction electric motors ED-118B, based on the operational characteristics of the locomotive in the speed range of 0-80 km/h.

Mastery of assistive technology: What is it? How do we measure it?

ABSTRACT This paper presents the results of a one-year study on mastery of assistive technology (AT). This study sought to develop a conceptual framework for talking about mastery of AT and to create an instrument for measuring individual mastery. A Delphi Study was conducted with individuals with disabilities considered to be “power users” of AT, practitioners, and researchers. Participants were asked to: identify factors that are predictors and indicators of AT mastery, determine how to measure these factors and determine criteria for each factor for the stages of AT mastery (e.g. novice, context-dependent, transitional, and power user). The resulting measure is called the Continuum of AT Mastery (CATM).

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

Against the backdrop of China's carbon peak and carbon neutrality goals, the use of virtual power plants for electricity economic dispatch has gradually become a research hotspot. The virtual power plant includes both supply and demand sides. If the inherent conflicts of interest between both parties cannot be effectively addressed, there are significant hidden dangers in the sustainable operation of the virtual power plant. On the basis of existing research, this article constructs a regional virtual power plant. With the goal of maximizing supply and demand utility, a two-stage Stackelberg dynamic game model was established between virtual power plant operators and users in the electricity market and carbon trading market mechanisms, and an incentive based hierarchical carbon price constraint was designed in the optimization model. In the model constructed in this article, the operator of a virtual power plant will consider environmental benefits based on expected returns, adjust the output of controllable units to change the electricity supply strategy, and formulate a pricing strategy. At the same time, users will adjust their electricity consumption strategy based on their own electricity consumption characteristics, and both will achieve a game equilibrium. Based on the optimization results, this article conducted a sensitivity analysis on the incentive coefficient of graded carbon prices and obtained the optimal incentive coefficient under the case conditions. The research results indicate that the model can coordinate and meet the interests and needs of both the power generation and consumption sides, and the implementation of incentive based hierarchical carbon pricing strategies has certain advantages compared to traditional carbon pricing strategies in different user types scenarios. The research conclusion of this article has good reference value for the sustainable green operation of virtual power plants.

Effective incentive based demand response with voltage support capability via reinforcement learning based multi-agent framework

With the ongoing advancements in power grids, it is crucial to maintain a balance between energy generation and usage to ensure the stability of power systems. Any disparity between the supply and demand of energy can lead to increased expenses for utility companies and consumers, which might potentially result in a widespread failure of the grid. To tackle this problem, this study offers the implementation of the Voltage Capability Incentive-Based Demand Response (VC-IBDR) system, which utilizes a multi-agent framework. The main goal is to reduce power usage by providing financial incentives, while also ensuring that voltage standards are satisfied at each node. In this method, the aggregator agent coordinates demand response operations by interacting with household agents that are equipped with home energy management systems. Every household utilizes a disjunctively constrained knapsack problem optimization method to effectively organize the consumption of home appliances, considering customer preferences and dissatisfaction. During demand response events, the aggregator agent works together with household agents to optimize appliance schedules, ensuring that voltage levels at each node remain within acceptable levels. The simulation is performed to assess the efficacy of VC-IBDR on a feeder that provides service to 25 residential households. The findings indicate substantial decreases in power usage, with an average decline in demand of 4.20% at the feeder level and a 10.41% decrease in peak power consumption throughout the day. Furthermore, the voltage values at each node stay consistently steady within the range of 0.96 to 1.0 per unit throughout the day.

Distribution System Survey and Analysis: A Case Study of Kathmandu University

Load flow analysis helps to understand a power system's voltage profile and power flow distribution, enabling the identification of potential issues such as overloading and under-voltage conditions. Fault analysis ensures reliability and safety by understanding and mitigating abnormal conditions in electrical distribution systems. Together, they contribute to efficient operation, equipment protection, and overall system reliability. This paper presents the Kathmandu University distribution system's load flow and fault analysis. Starting from the careful study of the power usage of the Kathmandu University distribution system then followed by the load flow analysis employed in the Dig Silent Power factory software, critical insights into the system’s performance were unveiled. Additionally, fault analysis within the simulated distribution system provides valuable data to enhance the understanding of the different patterns of currents for the various types of faults that occur at different locations of the Kathmandu University distribution system. This study provides a comprehensive understanding of the Kathmandu University distribution system. It identifies potential issues that can help to make the Kathmandu University power system more reliable and efficient in the future.

An IoT-assisted Intelligent Monitoring System

Objective: The usage of power and automated control systems can save energy, but creating an intelligent control system via the Internet of Things (IoT) is an advanced technique because it provides remote access and control through the Internet, especially for those who are facing issues with monitoring their home’s smart gears remotely. In this sense, NodeMcu is an internet-enabled device that operates under different conditions to monitor smart home gear securely. Methods: Therefore, this research has been conducted to provide a solid platform for remote home control and monitoring, together with a complete solution. In this research, we have built a small model by integrating hardware components and connecting online to Google Firebase, allowing the end-user to monitor and control their online home environment efficiently. Results: The online interface helps end-users care for their home equipment, lock doors, and install all intelligent gear inside smart homes. This interface ensures efficient and seamless interaction with all integrated smart home devices, enhancing the user experience and providing a higher level of convenience and security. Through this platform, end-users can perform a wide range of tasks such as adjusting lighting, regulating temperature, monitoring security cameras, and managing energy consumption. The system’s user-friendly design and reliable connectivity ensure that users can maintain control over their home environment from any location with internet access. Conclusion: The proposed model is Cost effective, making it accessible to a boarder demographic. This advancement in home automation enhances security and improves the overall management of smart home system.

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

Design of low delay low power hybrid logic based flip-flop using FinFET

The need for a low-power and high-speed technology for computation of digital signals is rising due to the fast growth of technological innovations. Flip-flops serve as fundamental elements in signal processing technologies. Hybrid logic has great potential for constructing flip-flop circuits because of its minimal transistor count as well as low-power properties.The present investigation presents a design for a tiny and low-power hybrid flip-flop that operates fully statically. The design has been developed by utilizing different circuitry-reduction techniques. It incorporates a hybrid logic approach combining pass transistor logic to decrease circuit complexity. The ongoing work utilizes a hybrid logic with low delay and low power to generate flip-flops. The proposed flip-flop design significantly decreases latency compared to the existing master-slave flip-flops. This flip-flop remains immune to clock overloading, resulting in reduced power consumption. Reducing the quantity of PFET (P-channel Field Effect Transistor) transistors enhances the circuit's delay, area, and power consumption. Implementing these circuit optimization techniques yields several benefits, such as reduced CLOCK-to-OUT and DIN-to-OUT node delays, decreased average power consumption, lower leakage power, and a transistor count of only 18. The results were achieved using Cadence Virtuoso at 18 nm finFET technology, with varied process corners, supply voltages ranging from 0.7 V to 1 V, and temperatures ranging from -50 °C to 75 °C. This study also presents a Monte Carlo simulation of power consumption, leakage, and delay for the suggested flip-flop. According to the simulations, the recommended flip-flop exhibits exceptional stability. The recommended flip-flop has lower power usage by at least 17.95 % compared to standard architectures due to the lower number of PFET transistors.