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This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as "simulation," "modeling," "Artificial Intelligence," and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields. [ABSTRACT FROM AUTHOR]

After the carbon peaking and carbon neutrality goals were proposed, different institutions and scholars carried out research on China's medium- and long-term mitigation pathways. Through the literature review of China's research on carbon neutrality, this paper finds that the zero-carbon energy transition is the key to achieving carbon neutrality. The main driving factors of reducing energy-related carbon emissions include the cleanliness of primary and secondary energy supply systems, the electrification of energy consumption and the development of hydrogen energy, energy efficiency improvement, carbon capture and storage (CCS), and negative emissions. Furthermore, based on the literature, this paper conducts a quantitative comparative analysis, and selects key indicators for comparative analysis and summary from seven dimensions, including economic and social development, carbon emission pathways, primary energy consumption, final energy consumption, final hydrogen energy consumption, electricity demand and supply, and installed capacity and structure of power generation. Main conclusions are as follows: (i) In terms of carbon emission pathways, the institutions generally believed that China will peak carbon emissions around 2028 and achieve carbon neutrality between 2050 and 2060. Achieving net-zero or near-zero emissions first in the power sector is the key to carbon neutrality across the society; (ii) In terms of energy supply, it is a consensus to increase the proportion of clean energy and reduce carbon emissions from the source. The proportion of clean energy in primary energy will increase to more than 85%, and the proportion of clean energy power generation and installed capacity will reach more than 90%; (iii) In terms of energy use, electricity will become the core of final energy consumption in the future. The predicted electricity consumption across the society will range 14.3–18.4 PWh, and the predicted electrification rate will exceed 65%. [ABSTRACT FROM AUTHOR]

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances. [ABSTRACT FROM AUTHOR]

Application of big data analytics (BDA) is seen in various disciplines within an organization to predict trends, explore opportunities and monitor performance. Among all the industries, BDA presents immense value in sustainable manufacturing (SM) given that it is an industry that consumes a high amount of energy, emits high amounts of waste and carbon emissions and requires a large amount of manpower. This paper aims at illustrating the effects of BDA in supporting SM by studying the Indian manufacturing firms which have unfavorable labor laws compared to other developing countries. With an extensive literature review, this paper discusses the relationship between BDA and sustainability, the capabilities of BDA, the concept of SM, the BDA framework for SM, the relationship between Industry 4.0 and SM and the challenges of implementing BDA. Using qualitative meta-analysis research methodology, the paper examines the nine common critical success factors that enable SM through BDA implementation by comparing 15 primary studies. Finally, the paper concludes the research findings and outlines future research directions. The study provides theoretical and practical contributions to BDA implementation in achieving effective SM practices in emerging economies. [ABSTRACT FROM AUTHOR]

Vietnam, a country in Southeast Asia, is anticipated to experience an increase in energy consumption due to the growth of its manufacturing and industrial sectors, which aims to establish Vietnam as the world's new production hub. Fossil fuels, which are ecologically unfriendly and quickly running out, are now Vietnam's primary energy source. Due to the detrimental effects of fossil fuels on the environment, we can apply strategic decision-making in the industrial sector to choose the best alternative for a renewable energy resource (RER). RERs such as solar energy, wind energy, solid waste energy, biomass energy, geothermal energy, biofuel energy, and hydropower energy are important for generating energy and will be crucial for the survival of humanity, the planet, and other living things in the future. The actions of RER-based industrial development must be encouraged since they will make a significant contribution to overcome environmental impact limitations. The 2-tuple linguistic q -rung orthopair fuzzy set (2TL q -ROFS) is a novel development in fuzzy set theory that presents a decision-making method for selecting the most appropriate alternative. In this paper, we develop a family of the 2TL q -ROF Maclaurin symmetric mean aggregation operators such as the 2TL q -ROF Maclaurin symmetric mean (2TL q -ROFMSM) operator, the 2TL q -ROF dual Maclaurin symmetric mean (2TL q -ROFDMSM) operator and its weighted forms. We also look into a few of the proposed operators' properties and special cases. Then based on 2TL q -ROFS, a new Decision Making Trial and Evaluation Laboratory (DEMATEL) model is constructed, which can use the decision makers' preferences to get the optimum objective weights for attributes. Next, we extend the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to 2TL q -ROF version which not only covers the uncertainty of human cognition but also gives decision makers a larger space to represent their decisions. Utilizing this model the best RER for Vietnam is selected. The outcomes of the hybrid 2TL q -ROF-DEMATEL-TOPSIS method reveal that among the energy resources, hydropower energy ranked highest, followed by biofuel energy. [ABSTRACT FROM AUTHOR]

Addition and multiplication are some of the most broadly adopted arithmetic operations in a wide range of applications. This paper proposes new structures of approximate multipliers to optimize the area, delay, and power without affecting the accuracy metrics. Multipliers and adders play a significant role in the functioning of any digital circuit or system. The overall performance of a processor highly depends on the speed of adders and the energy consumption. In this paper, two types of compact error-tolerant approximate adders are designed and used along with approximate 4:2 compressors to improvise the efficiency of the approximate multipliers. The proposed approximate multipliers show good results when compared to the existing structures in terms of area, delay, power, and accuracy. The approximate multipliers are applied to image sharpening and image multiplication applications. The error-tolerant adder's performance is evaluated in the practical domain using the image blending application. Peak signal-to-noise ratio (PSNR) performance and the structural similarity index metric (SSIM) are used to assess the modeled designs. The proposed approximate multipliers and adders exhibit better performance in terms of PSNR and SSIM and are found to be an optimized design to apply effectively in various error-tolerant image processing applications. [ABSTRACT FROM AUTHOR]

Today's commonly used risk management procedures allow the planning team to focus on sensitive areas. It focuses the project team's attention on activities and resources when a great risk threat exists or when the most time- and effective-engineering solutions can achieve expense reductions. Industrial security issues have worsened dramatically over the last decade. Over time, the risks in the sector rose as limited, single-train or batch operations shifted to massive multi-train operations. Often an accident leads to detrimental effects: shutting down the operation, loss of life, environmental disruption, and loss of business. The reparation of losses from the budget is unwelcome since the government's reserve will not cope with these needs. This paper has a definitive comparative edge for company sponsors for project risk management (PRM) strategy. The sponsors who knowingly take chances, forecast unfavorable developments, defend themselves against unforeseen incidents and gain experience in price danger take the lead. However, this market benefit is largely dependent on the method to initially identify risks in the architecture of extensive multidisciplinary capital ventures. This paper explores how a sustainable market strategy works in reality. Using the market model, the business issues that need to be considered during the implementation or expansion of urban strategies are discussed. The PRM strategy shows a better-quality assurance ratio of 97.2%, probability ratio of 95.3%, safety rate of 97.5%, reduced risk management rate of 22.6%, an accident rate of 17.4%, cost management of 25.1% and fuel consumption ratio of 23.7% when compared to existing strategies. [ABSTRACT FROM AUTHOR]

This paper discusses the effects of green technology innovation, renewable energy consumption and renewable energy investment on environmental quality. Panel data covering 81 countries from 2001 to 2020 are used to estimate the effects. The results indicate the following: First, at the total sample level, the effect of green technology innovation, renewable energy consumption and renewable energy investment on Carbon dioxide (CO 2) emissions is significantly negative at the level of 10–1%; Second, at the subsample level, the green technology innovation and renewable energy consumption of non-Belt and Road (B&R) and high-income countries significantly affect CO2 emissions; while the green technology innovation and renewable energy consumption of middle- and low-income and B&R countries have no significant effect on CO2 emissions; Third, the endogeneity and robustness tests of the model verified that this empirical process is credible. Based on the above results, this paper proposes a series of policy implications needed to achieve carbon emission reduction and environmental quality improvement. [ABSTRACT FROM AUTHOR]

In the analogy where achieving the "carbon peaking and carbon neutrality" goals (the "dual carbon" goals) is compared to a battle, energy is the main battlefield, and electric power the main force. The low-carbon transformation path of the electric power industry exerts fundamental impact on progress towards carbon peaking and carbon neutrality of the whole society. Therefore, this paper first analyzes challenges and opportunities faced by the electric power industry to achieve the "dual carbon" goals; secondly, on the basis of deep low-carbon, zero-carbon and negative-carbon scenarios of the electric power industry, this paper quantifies and delves into the consumption structure of primary energy and final energy, as well as the future development positioning and trend of different categories of energy. Based on the research results, this paper proposes low-carbon transformation path of the electric power system at the stages of carbon peaking, deep low-carbon and carbon neutrality, and analyzes the trend of power supply cost changes in different scenarios. On this basis, a series of implementation key points and measures are proposed to adapt to green and low-carbon transformation of the electric power industry, covering areas such as coal-fired electric power development positioning, new energy development and utilization, diversified supply and demand, new energy industry chain, and power supply costs, and so forth. [ABSTRACT FROM AUTHOR]

In this research on range query technology for two-tiered wireless sensor networks, the existing researches still need improvement in privacy security and communication cost of the sensor node. In this paper, a security multi-dimensional range query protocol is proposed. The left 0-1 encoding mechanism (a new and optimized encoding mechanism), HMAC algorithm, and BASE64 encoding are employed to optimize the calculation of comparison factor. The encryption index chain is constructed by using AES algorithm to replace the long encryption constraint chain, so as to lower the energy consumption of computation and communication. The base station can verify the authenticity and integrity of query results on the basis of continuity of storage time and encryption index chain. The experiment has been conducted in the real systems. The protocol is compared and analyzed with the latest proposed Communication-Efficient Secure Range Queries (CSRQ). The results have shown that the proposed protocol can gain a better performance in terms of four aspects, i.e., the length of sensing data, the number of data collected in each cycle, the number of data dimensions and the number of sensor nodes in each unit. Meanwhile, compared with CSRQ, the communication cost of the proposed protocol is reduced by at least 10%. [ABSTRACT FROM AUTHOR]

Active suspension technique normally provides the best vibration mitigation performance at the cost of high-energy consumption. In contrast, the energy consumption of a semi-active suspension is normally much smaller than that of an active suspension, whereas the control performance is compromised as well. Observing the core issue that caused such a phenomenon is that the existing semi-active control force, unlike the active control force, shall always oppose to the relative motion of the actuator (i.e. clipping phenomenon). This paper subsequently proposes a novel semi-active vehicle suspension system that incorporates a passive negative stiffness (NS) spring and a semi-active damper (SD) to realize uncompromised active control force while consuming energy of a typical semi-active suspension system. In specific, the proposed system allows for decomposition of the target active control force and tracked via the collaboration of the NS and SD components. Herein, the NS element is capable of releasing the store potential energy that subsequently eases the aforementioned clipping phenomenon of a traditional semi-active suspension. In this paper, besides the relevant clarifications on the system topology and working mechanism, its feasibility and performance enhancement are also validated via systematic numerical simulations of a vehicle suspension; and the results indicate, for the first time, that the proposed semi-active suspension can fully track the active control force and subsequently achieve unprecedented control performance comparable to an active controller. [ABSTRACT FROM AUTHOR]

In recent times, the virtual power plant (VPP) is gaining more attention in power system engineering due to its tremendous potential in enhancing sustainable urbanism, in which, it supplies clean energy from distributed generators. Electricity is deemed a basic requirement for future automotive and ultra-modern technologies. The deficiency of traditional energy resources and their complex generation process make the production cost of electricity increase dramatically. Moreover, traditional power distribution systems are encountering issues in distributing electrical energy to fulfill customer demands. Therefore, this paper proposes a novel power management system named 'the hybrid genetic local binary knowledge (HGLBK) algorithm' to manage power distribution in the transmission lines and to optimize the total operation cost of the network. The hybrid optimization algorithm effectively controls the load by supplying the surplus power load to the adjacent feeders thereby optimally selecting and placing the distributed energy resource (DER). The proposed concept is implemented at Kayathar, Tamil Nadu in India, and their real-time data are utilized for modeling the VPP. The proposed VPP concept is implemented in the IEEE-9 bus system and the performance of VPP is simulated using the MATLAB software. The performance of the proposed HGLBK algorithm is assessed by comparing its effectiveness with the existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, it has been demonstrated that the tri-stable energy harvesters are more efficient than the bi-stable and mono-stable harvesters in energy harvesting. The oscillators of energy harvesters can be designed as a vibration absorber to attenuate the vibration of the primary system. This paper presents a combined vibration absorber and energy harvester (VAEH) system for simultaneous vibration control and energy harvesting, and the dynamic performances of the VAEH with two magnet arrays of the same geometric parameters, but opposite polarity external magnets, are studied. The mathematical model of the tri-stable VAEH is established, by which the dynamic and energetic characteristics of the VAEH system are obtained. It is found that the potential barrier and potential well depth of the repulsive magnet array are greater than the attractive one, reducing the voltage harvesting efficiency under harmonic and impulsive excitation. Both the amplitude and frequency of excitation are crucial to the dynamic response of the VAEH system. The attractive VAEH performs better than the repulsive VAEH in vibration suppression, especially for high impulse input. From the frequency response of the system, the vibration of the primary system is shown to be magnified at certain frequencies. The magnified primary system vibration and the harvested voltage can be attenuated by increasing the VAEH damping, which is a critical parameter to balance the weight of vibration suppression and energy harvesting. The attractive VAEH designed in this paper performs better than the repulsive one in vibration suppression and energy harvesting. [ABSTRACT FROM AUTHOR]

With the rapid development of digital technologies based on information and communication technology (ICT), some studies suggest that the development of the digital economy is bound to have a significant impact on energy consumption. There are also studies demonstrating from different perspectives that ICT plays an important role in improving energy efficiency. Then, will the rapid development of the digital economy lead to a sharp increase in energy demand (electricity demand), thus affecting China's socio-economic transition to green and low-carbon development? To this end, it is of great practical significance to explore the relationship between the digital economy and energy consumption from theoretical and practical perspectives. Focusing on the issue of ICT and its impacts on energy consumption, this paper clarifies the internal influencing mechanism between ICT and energy consumption. On this basis, this paper compares and analyzes the models, methods, and data in existing studies from the aspects of measurement of ICT's direct energy consumption, empirical research on ICT and energy consumption, and discussions on rebound effects. After a brief summary, this paper identifies their shortcomings and explores future research directions. [ABSTRACT FROM AUTHOR]

Strategies to combat climate change may be based on mitigation of the phenomenon or on adaptation to its consequences. This paper aims to identify the driving factors of the choice between these two categories of strategies in the context of urban climate plans. The fight against climate change being characterized by a logic of free riding, the situation tilts the balance towards adaptation strategies in an urban context, to the detriment of mitigation. This hypothesis is tested here through a review of the existing literature on urban climate strategies. This study shows that, counterintuitively, mitigation prevails over adaptation in urban climate strategies up to now. This paper explores the explanations for this seemingly paradoxical situation. We argue that a big part of the explanation has to do with the institutional context of urban climate strategies, specifically the decision-making capacities of municipalities, or the fact that they take part in international networks promoting mitigation over adaptation. Other explanations rely on the cost/benefit impact of adopting mitigation or adaptation, like the collateral local/private benefits of urban climate strategies that are often bigger with mitigation than adaptation. Another finding is that there is no systematic planning making it compulsory to choose between mitigation and adaptation strategies, as they are in some instances complementary, providing co-benefits. [ABSTRACT FROM AUTHOR]

China's automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles' whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China's automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China's automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations' research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important element of Europe's Climate and Energy scheme to meet the target of a 20% reduction in energy demand by 2020. To achieve this goal, it is essential to transform to low-carbon economy and much of required transformations take place at the local and regional level, while regional authorities have the tools to influence and encourage this process. The paper presents the results of a survey relating to the energy efficiency projects conducted among councilors at the regional level. The paper investigates local councilors' attitudes about the impact of energy efficiency projects, prerequisites for successful implementation of energy efficiency projects and preconditions for efficient public procurement in the field of energy efficiency. Survey results could provide some useful insights for policy recommendations in the field of energy efficiency. [ABSTRACT FROM AUTHOR]

Capacitor mismatch plays an important part in the spurious-free dynamic range (SFDR) performance of high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs). This paper presents a symmetric group control dynamic element matching (SGCDEM) method aiming at enhancing SFDR of SAR ADCs applied in biomedical applications. The proposed scheme symmetrically divides the capacitor array into four sub-DACs, using quantization results corresponding to each sub-DAC as rotary control codes to symmetrically control the rotation of the capacitors. In order to improve the energy efficiency and save chip area, the proposed method replaces the pseudo-random number generator (PRNG) with quantization results of ADC as the rotary control code. Moreover, split rotator technology is applied in the proposed scheme to reduce the number of multiplexers (MUX) used in the rotator, which introduces a low logic depth. The proposed architecture has been implemented on a 12-bit SAR ADC model in MATLAB in order to verify its performances. After taking a standard deviation of 1% for the unit capacitance into consideration, this method achieves above 77.3 dB SFDR, enabling to correct the SFDR by more than 23.6 dB compared with the structure without DEM. [ABSTRACT FROM AUTHOR]

The Underwater Wireless Sensor Network (UWSN) is capable of supporting a wide range of low-data-rate acoustic sensor networks, as well as scalability and energy-efficient routing algorithms. Furthermore, because of the lower bandwidth and longer propagation delays, energy consumption is a major concern with underwater networks. Because radio transmissions are unstable in deep water, a UWSN often communicates via acoustic channels. UWSN features create constraints on data packet transmission and energy-efficient routing. The hardest challenge is creating an effective routing protocol for UWSNs that uses sensor node localization. There have been several routing algorithms reported for identifying nodes via a localization process. This paper proposes a novel vector-based forwarding (NVBF) and efficient depth-based routing (DBR) protocol to provide robust, scalable, and energy-efficient routing. Efficient DBR is a stochastic model that can survive even in acoustic channels with substantial transmission loss. An adjacent node table is created to decrease the energy consumption and end-to-end delay by limiting the adjacent node request generated every single time. The redundant packet transmission is reduced by creating a packet queue which also minimizes the energy. The NVBF algorithm uses a random waiting time to overcome the collision that occurs while sending the acknowledgment packets. Based on the previous sender's depth and the depth of the own node, the DBR makes packet transmission decisions. The experiments are conducted using the NS3 simulator and the efficiency of the proposed approach is evaluated in terms of propagation delay, network lifetime, energy consumption, and delivery ratio. When compared to the existing techniques such as DBR, VBF, and energy-efficient DBR (EDBR), the proposed methodology offers improvement up to 40% and 30% in terms of propagation delay and delivery ratio. [ABSTRACT FROM AUTHOR]

Reliability and energy efficiency are two hostile objectives considered in designing task scheduling in most real-time multiprocessor systems on chip (MPSoC). Addressing and improving one of them may affect and degrade the efficiency of the other one and vice versa. In this paper, we intend to examine these challenges and ultimately achieve an optimal energy consumption and reliability state. This paper presents a novel scheduling technique that can adapt to the limitations of real-time systems and have optimal energy consumption and reliability. This is done by minimizing the overlap of tasks, adjusting the speed of processors and the number of backups of each task. The proposed scheme reduces energy consumption on average by 11% to 42% compared to the previous state-of-the-art techniques and keeps the reliability at a high level. [ABSTRACT FROM AUTHOR]

Wireless sensor networks' energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node's time of death has been delayed by 3% in total. [ABSTRACT FROM AUTHOR]

This paper proposed a single-degree-of-freedom self-sustained nonlinear oscillator capable of precisely predicting the bouncing force induced by a person during bouncing activity on a flat and rigid surface. A bouncing person produces essential internal energy required to maintain its motion, so it can be modeled as a self-sustained oscillator that can generate (i) the stable limit cycle, (ii) the periodic bouncing force signal, and (iii) the self-sustained motion. A hybrid Van der Pol–Rayleigh oscillator added with two quadratic and one cubic nonlinear terms has been derived to yield desired softening and hardening effects as well as even and odd harmonics, as observed from the analysis of experimental bouncing force data. The force applied on the surface corresponds to its restoring force. The stability analysis of the oscillator has been performed using the energy balance and perturbation methods. The model parameters are extracted from the experimental bouncing force data resulting from a bouncing test on a group of seven subjects with shoe insoles at six different frequencies guided by a metronome. The bootstrapping method has been performed to examine the convergence of mean values of each model parameter by increasing the cardinality of the experimental set. The bouncing force signals produced by the proposed model and experimental results demonstrate an excellent agreement. [ABSTRACT FROM AUTHOR]

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption. [ABSTRACT FROM AUTHOR]

A thermoelectric generator (TEG) is a semiconductor-based device that can exhibit a flow of electric current through the external circuit if the sides of the TEG are subjected to a temperature difference caused by a heat source. The terminal voltage and the TEG’s internal resistance are altered depending on the temperature difference. Therefore, a suitable power electronic converter with a maximum power point tracking (MPPT) feature is required to harvest maximum power from the TEG and store the harvested energy in a battery-based energy storage system. This paper investigates a novel energy harvesting methodology from TEG using a series compensated buck-boost converter (SCBBC). MPPT has also been implemented using a sliding mode controller (SMC). A mathematical model of a TEG source has been developed in MATLAB-Simulink and tested with generic boost converter (GBC) and SCBBC for their dynamic performances during battery charging. The efficiency of conversion is found to be higher for SCBBC, at 97.87%. An experimental prototype has been developed with SCBBC and tested for two configurations, viz high voltage and low current configuration. The former configuration exhibits an efficiency of 86.3%, while the latter has an efficiency of 83%. The results indicate that SCBBC, along with SMC, opens up new efficient prospects for harvesting waste heat energy using TEGs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach solving the holes problem in Wireless Sensor Networks (WSNs) based on Khalimsky k-Clustering and data routing protocol (MDKC). The aim of this solution is to establish optimized data routing paths between isolated nodes/clusters and the Sink in noisy environment with the presence of obstacles. This approach is an improvement of a previous work deploying stationary WSN not dealing with the problem of "holes". At first, the MDKC algorithm divides the WSN into k-hop (k ≥ 2) compact dynamic clusters. For each cluster, a node is elected cluster-head in its k-neighborhood according to some criteria such as the remaining energy, the k-degree and the communication probability average. Then, some nodes are selected as Khalimsky anchors to optimize the intra-cluster data routing process. The Khalimsky anchors at the border layers ensure the inter-cluster data routing between adjacent clusters. In the next phase of MDKC, Mobile Collectors (MCs) are used for data collecting and relaying from isolated nodes/clusters to the connected Khalimsky's nodes. The simulation results prove that MDKC minimizes the energy consumption and improves the connectivity rate between sensors and the delivery rate compared to some existing approaches. [ABSTRACT FROM AUTHOR]

Knee-stretched walking is desirable for biped robot because it is a characteristic of human walking as well as lower energy consumption. In this paper, we proposed a new knee-stretched bipedal walking pattern generation approach named Cosine-law-based Spatially Quantized Gait (C-SQG). C-SQG includes two parts, the first one is the Cosine law-based kinematic joint angles generation for knee-stretched biped walking with the hip horizontal displacement; the second one is combining with the concept of Spatially Quantized Dynamics (SQD) and the optimization-based methods. Based on these two approaches, the balanced human-like walking patterns with fully extended knee joints for humanoid robots can be generated. The proposed approach was tested on the CoppeliaSim simulation platform, and the experimental results indicate that the knee-stretched walking patterns for different walking speeds and foot locations can be generated. Compared to the existing knee-stretched gait generation approach, the C-SQG is more robust and adaptive to different desired foot locations appropriately. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low-jitter reference-less clock and data recovery (CDR) circuit with a speed range of 9.8–12.5 Gb/s. The proposed CDR uses a multi-stage half-rate bang-bang phase detector (ML-HR-BBPD) to maximize the quantization of the phase difference. In addition, a unit interval adjuster (UIA) is added to the CDR circuit. So that the circuit can minimize the phase detector's phase error before the output clock frequency is locked. Finally, the loop filter (LF) is improved to realize the coarse and fine adjustment of the phase error over a wide range of phase differences. The CDR circuit's total power consumption is reduced by using a half-rate phase detector. The CDR circuit was fabricated in TSMC 40 nm CMOS process. The measured results are obtained in the proposed CDR circuit at a data rate of 12.5 Gb/s. With a pseudo-random bit sequence (PRBS) of 2 3 1 − 1 , the measured result shows that the bit error rate (BER) is < 1 0 − 1 2 , and the root mean square jitter recovered in the output is 0.302 p s rms . The circuit's jitter tolerance (JTOL) is 0.46 UIpp, and its total power consumption is 74.8 mW with a 5.98 pJ/bit energy efficiency. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including photon-based decision making; supply chain management; and energy consumption.

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment. [ABSTRACT FROM AUTHOR]

This paper presents a modified energy-efficient Dual Mode Transmission Gate Diffusion Input (DMTGDI) design and is termed as M-DMTGDI. A contention issue in dynamic mode operation of existing DMTGDI design and DMPL design is identified and illustrated through mathematical formulation and simulations. To resolve this concern, the pre-charge/pre-discharge transistor in existing DMTGDI design is replaced by dual mode inverter in the proposal. The functional verification and performance comparison of NAND, NOR, XOR gates and 1-bit full adder based on proposed M-DMTGDI is carried out using 90 nm BSIM4 model card for bulk CMOS using Symica DE tool. The performance of the circuits is evaluated in terms of power, delay and Power Delay Product (PDP) in both static and dynamic modes. The variation of PDP with the ratio of the time the circuit is designed to run in dynamic mode against static mode is also investigated to analyze the energy efficiency of the M-DMTGDI design. The proposed approach offers maximum PDP reduction of 33.52%, 99.39% and 96.61% for 2-input gates as compared to their footed DML, DMPL and DMTGDI counterparts, respectively. The reduction in PDP is quite significant in 1-bit full adder circuit where the corresponding values are 94.18%, 99.41% and 99.79%, respectively. [ABSTRACT FROM AUTHOR]

This paper uses the chaotic fuzzy encryption (CFE) technique and a greedy chemical reaction optimization algorithm based on mobile cloud computing to protect data from any assault. Mobile users who use protected mobile cloud computing (Mobi-Cloud) must trust the cloud service provider to keep the data sent from their mobile devices safe when mobile users express strong reservations about storing personal information in the public cloud. The Greedy Chemical Reaction Optimization (G-CRO) algorithm reduces critical limitations by determining the offloading end with motion at run time, i.e. the task's operating time, CPU usage, memory utilization, and energy usage. The experimental results show that the proposed method performs better in terms of file uploading time, file downloading time, memory usage in file uploading, memory usage in file downloading, encryption time, decryption time, memory usage on file encryption, memory usage on file decryption, security levels, and key generation time. Compared to the Proxy Re-Encryption (PRE) algorithm, the suggested method consumes less time for key creation (26.04). [ABSTRACT FROM AUTHOR]

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations. [ABSTRACT FROM AUTHOR]

Companies now face two challenges in order to remain innovative and competitive. The first is to digitally transform their production, products and services. Second is to address sustainability topics to reduce their global footprint. However, introducing new digital technologies may contradict company's sustainability goals, for example, introducing a digital technology improves assembly lines, but increases energy consumption. This paper introduces a sustainability-enabled assessment framework for digital technologies (SAFT) supporting companies to choose the best digital technology for them. The SAFT framework assesses digital technologies in terms of sustainability factors as well as crucial aspects such as technology potential, and the fit to the company. The paper concludes with a case study in which the SAFT framework is applied to a company in the construction industry. [ABSTRACT FROM AUTHOR]

The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine. [ABSTRACT FROM AUTHOR]

Memristors can be used to build nonvolatile memory systems with in-memory computing (IMC) capabilities. A number of prior works demonstrate the design of an IMC-capable memory macro using a memristor crossbar. However, read disturbance limits the use of such memory systems built using a 0-transistor, 1-RRAM (0T1R) structure that suffers from the sneak path problem. In this paper, we introduce a scheme for both memory and logic operations using the 1-transistor, 1-RRAM (1T1R) memristor crossbar, which effectively mitigates the read disturbance problem. The memory array is designed using nMOS transistors and the VTEAM memristor model. The peripheral circuitry like decoders, voltage multiplexers, and sense amplifiers is designed using a 45 nm CMOS technology node. We introduce a mapping technique to realize arbitrary logic functions using Majority (MAJ) gate operations in the 1T1R crossbar. Through extensive experimentation on benchmark functions, it has been found that the proposed mapping method gives an improvement of 65% or more in terms of the number of time steps required, and 59% or more in terms of energy consumption as compared to some of the recent methods. [ABSTRACT FROM AUTHOR]

In the design of high performance complex arithmetic logic circuits, ground bounce noise, leakage current and leakage power are important and challenging issues in nanometer down scaling. In this paper, the low power and reduced ground bounce noise using 10 transistor full adder has been proposed. Full adder is the most important basic building of digital circuits employing arithmetic operation. Adder circuit is widely used in many digital circuits not only for arithmetic operation but also for address generation in processors and microcontrollers. It is therefore necessary to make these systems more efficient so that they consume less power. Here, we use stacking power gating technique to evaluate leakage current, power and ground bounce noise. This paper describes reduction of leakage power and ground bounce noise from the 10 T full adder circuits to make it more reliable to be used to have low power and stable and errorless output. All the simulation in this paper has been carried out using cadence virtuoso at 45 nm technology at various voltages and various temperatures. By using this technique the leakage current reduction can be improved by 80% and leakage power to 70% as compared to conventional 10 T full adder. Ground bounce noise cam be reduced to 60% as compared to the base full adder. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the state-of-art high-speed dynamic comparators. The comparator is a critical block of high-speed, low-power analog-to-digital converters, determining the speed and overall power consumption. Therefore, the design of a high-speed comparator with tolerable offset, noise and power consumption is of utmost importance. Recent work reported on high-speed comparator topologies is investigated in detail with the help of simulations in 65 nm CMOS technology. Various parameters, such as delay, energy consumption, speed, offset, kickback noise, power delay product, etc., are compared. A detailed comparative study is also presented on several design methodologies. [ABSTRACT FROM AUTHOR]

This paper presents the adiabatic logic called 2 N – N –2 P , which can operate with less number of transistors and high energy efficiency than the existing circuit styles. It is a dual rail logic operated by four-phase power clock (PC). The 2 N – N –2 P adiabatic logic is capable of operating through a wide range of frequency from 100 MHz to 1 GHz. Relentless scaling of MOSFETs towards lower technology nodes results in short channel effects in addition to increasing higher leakage current issues. In this scenario, FinFET advantageously replaces MOSFET with its unique features of the elimination of the short channel effects encountered by the MOSFETs with its gate structure that wraps around the channel completely. It incurs that the lower energy consumption and the feasibility of designing energy recovery circuits using FinFETs are analyzed in this paper. Comparatively, the energy efficiency of FinFET-based 2 N – N –2 P against the 2 N 2 N 2 P and Positive Feedback Adiabatic Logic (PFAL) are analyzed. Simulation results also validate the robustness and efficiency of 2 N – N –2 P adiabatic logic circuit under process parameter variations of FinFET technology. Complex adiabatic adders and multipliers taken as bench mark circuits have been designed using 32-nm FinFET technology node and the results validate the enhanced energy efficiency characteristics of 2 N – N 2 P over 2 N 2 N 2 P and PFAL designs. [ABSTRACT FROM AUTHOR]

A mobile community can be composed of multiple mobile devices through D2D (Device-to-Device) network. In many cases, these mobile devices cannot conveniently connect to the Internet, for various reasons. To overcome this obstacle, one solution is to let the mobile devices cooperate with each other through a D2D-enabled network, forming a mobile community that, as a whole, may be able to autonomously execute the tasks requested by its members. To maximize the overall benefits of mobile communities, this paper proposes a novel task allocation approach, EDTG (Energy-aware and Deadline-constrained Task allocation using Game theory). In mobile communities, energy consumption is responsible for the largest part of the cost. Energy management can lead to performance degradation and even be perceived as a bottleneck, while load balancing between devices can improve service performance and resource utilization to the largest extent. EDTG has considered both the inevitable performance constraints at each device and a method based on the connectivity of graph theory, in order to narrow down the search scope of optimal target mobile devices where requested tasks can be executed. The "Bargaining Game" method is designed and exploited to obtain the final task allocation solution. Final experimental results demonstrate that compared to existing approaches, EDTG ensures high-performance task execution and reaches the goal of maximizing the overall benefits to some extent, by achieving better energy savings and exploiting load balancing between devices. [ABSTRACT FROM AUTHOR]

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased. [ABSTRACT FROM AUTHOR]

There are cases in energy supply applications with low-power batteries, where the power required by the load is larger than the supply maximum deliverable power. Using a capacitor to create a high instantaneous power for the load was investigated as a useful solution. To increase the charging efficiency, a stepwise method for charging the capacitor was employed. This paper presents a new pulsed power supply approach in which the charging process is resumed after high instantaneous power delivery. Through this, the loss of residual energy in the capacitor was decreased and load turn-on speed and energy efficiency were significantly improved. The proposed structure consists of a buck converter that charges the capacitor using inductor current. A pulse frequency modulation (PFM) technique was used to generate the necessary signals for the buck converter switches, which resulted in controlling the inductor current. The proposed circuit was designed and simulated in a 0.18 μ m CMOS technology. It was shown that the designed CMOS circuit could reach a peak energy efficiency of 93.2% with a sleep period of 1.05 ms when the load consumes 7.06 μ J of energy. [ABSTRACT FROM AUTHOR]

With the virtual synchronous generator (VSG) technology gradually becoming an emerging technology for new energy consumption, its introduction has solved the problems of weak support and weak anti-interference of traditional new energy stations. However, the practice shows that the grid-connected characteristics of VSG limit its large-scale utilization, and there is little research on the interaction between VSG and a multi-machine system. In this paper, small signal models and time domain simulation models of each link of a photovoltaic(PV) power station with the PV virtual synchronous generator (PV-VSG) are first conducted, and then the influence of the grid-connected PV power station with the PV-VSG on the low-frequency oscillation of power system and its interaction mechanism with the multi-machine system are qualitatively obtained based on the small signal analysis method and prony analysis method from two dimensions of frequency domain and time domain, respectively. The analysis and simulation show that the PV power station based on VSG technology affects the dynamic characteristics of the system by changing the electromagnetic torque of each synchronous generator. Unreasonable operating conditions and parameter settings will aggravate the phenomenon of low-frequency oscillation of the system. Before the grid connection of PV power station, the operating parameters shall be reasonably set to ensure their safe and stable grid connection. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs) can be treated as an emerging technology that has the ability to handle the physical process and computational view of interlinked systems. At the same time, the high-performing processing capability provides assurance of CPS applications in real time. Besides, task scheduling is considered as the Nondeterministic Polynomial (NP)-hard problem and optimal allocation of tasks is important for the CPS environment. The primary concept of the optimum energy-based scheduling approach searches for the physical host allocation vector to the allotted virtual machine with an aim of reducing energy utilization. The multiple processor packet scheduling technique defined that every task in the system is already divided into processors by the task allocating scheme and every process can execute on the distinct or identical single processor scheduling technique. With this motivation, this paper presents a new quantum invasive weed optimization-based energy-aware scheduling (QIWO-EATS) technique for the CPS environment. The goal of the QIWO-EATS technique is to assign n autonomous tasks to m dissimilar resources, and thereby the whole task completion duration gets reduced and resources are completely used. The proposed model has been simulated using the MATLAB tool. The experimental results highlighted the better outcomes of the QIWO-EATS technique over the recent approaches in terms of several evaluation metrics. [ABSTRACT FROM AUTHOR]

An efficient method primarily depending on nonisolated quasi Z-source (QZS) novel multilevel inverter (NIQZS-NMLI) topology to interconnect photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is the combination of improved chameleon swarm algorithm (ICSA) and recurrent perceptron neural network (RPNN), hence it is named as ICSA–RPNN. Usually, the interface among the PV DC supply, the load is achieved through an NIQZS-NMLI topology. Here, the NIQZS-NMLI topology modeling design is increased with the new storage device to supply the largest amount of power from the PV power production system. The number of switches and full harmonic distortion of the machine are reduced through the NMLI topology, and it is used to achieve better upgrading capability, and reduce the voltage pressure on the entire active switching device has a better modulation index. At first, the objective function is described primarily depending on its controller parameters and control voltages, current, power, and modulation code. These parameters are useful to the inputs of the proposed ICSA-RPNN approach. The ICSA-RPNN methodology enhanced the voltage profile, reducing power distribution, power oscillations simultaneously distributing power to the load. The modulation load is controlled by proposed artificial intelligence (AI) mainly depends on the NIQZS-NMLI topography. In addition, the ICSA-RPNN methodology reduces the injected power, controls the voltage, current and frequency conditions of the DC hyperlink. The ICSA-RPNN approach is performed at MATLAB/Simulink site and the general performance of the output is compared to the existing systems for different load conditions. [ABSTRACT FROM AUTHOR]

In this paper, a low-cost rectenna based on an antipodal Vivaldi antenna (AVA) with elliptical slots (AVA-ES) is developed and analyzed. The design of AVA-ES, when compared to a reference AVA, presents significant improvements in directivity and gain (58% at 2.45 GHz for both), evidencing its advantages for using in rectennas. The proposed rectenna prototype presents satisfactory results at 2.45 GHz, such as conversion efficiency for low-power input levels (27% at − 10 dBm) and maximum output voltage (550 mV), that supports its use for radio-frequency energy harvesting (RFEH). [ABSTRACT FROM AUTHOR]

In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been investigated using appropriate probabilistic distributions. Incent-driven load-responsiveness management programs have been used as to encourage subscribers to reduce their power consumption during high-load hours. Whale optimization algorithm has been used for scheduling of network. [ABSTRACT FROM AUTHOR]

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants' location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants' positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control. [ABSTRACT FROM AUTHOR]

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures. [ABSTRACT FROM AUTHOR]

This paper presents two novel current-mode Full Adders based on Carbon Nanotube FET (CNFET). They comprise of three main parts: current-to-voltage converter, threshold detector and voltage-to-current converter. While other designs in the literature use either inverter or current source for detecting threshold, the new designs are based on a heuristic usage of an Ultra Low-Power Diode (ULPD) as voltage regulator. The use of diode does not lengthen the critical path and thereby ensuring high-speed operation. It does not increase power consumption significantly either. Simulation results by HSPICE and 32nm CNFET technology file demonstrate at least 19% higher performance in terms of energy consumption for the second proposed Full Adder compared to other traditional and state-of-the-art current-mode and mixed-mode designs. [ABSTRACT FROM AUTHOR]