Processing Power Research Articles

Data Centers Expansion in Italy: Energy Considerations and Big Data Analysis

The need for a Mediterranean hub that allows the spread of artificial intelligence has induced the wind of data center expansion to blow in Italy. Based on public and web data (therefore of only approximate and illustrative reliability), a possible proto-plan is outlined hereinafter that highlights - together with the need for time and cost refinements to reach a real concrete plan - how the construction of data centers should proceed on a broad front in order to optimize the plan objectives. These objectives include, in addition to operational reliability, also the times and costs associated with construction. Some elements of the plan are therefore discussed below from a technical-economic and organizational point of view, but also from a design point of view, useful for localization and for plant optimization, especially for the energy part (in its varioustechnological options) in a European context. After discussing the need for a data center to also operate off-grid, as well as the need for regulation and power factor correction for electrical supplies, the opportunity of an operational index to evaluate performance is highlighted, which should be part of the guaranteed parameters. For this purpose, an estimation index of Normalized Annual Processing Power (NAPP) is provided as a proposal-example to be used in the case of “turn-key” type construction contracts through a “main contractor” or through an “architect-engineer” with managerial functions. Finally, ethical and responsibility aspects are highlighted that concern the ownership of data and the purposes of their use, especially in general cases of experiments that involve a social body worried about generalized social control.

Structural basis of the allosteric regulation of cyanobacterial glucose-6-phosphate dehydrogenase by the redox sensor OpcA

The oxidative pentose phosphate (OPP) pathway is a fundamental carbon catabolic route for generating reducing power and metabolic intermediates for biosynthetic processes. In addition, its first two reactions form the OPP shunt, which replenishes the Calvin-Benson cycle under certain conditions. Glucose-6-phosphate dehydrogenase (G6PDH) catalyzes the first and rate-limiting reaction of this metabolic route. In photosynthetic organisms, G6PDH is redox-regulated to allow fine-tuning and to prevent futile cycles while carbon is being fixed. In cyanobacteria, regulation of G6PDH requires the redox protein OpcA, but the underlying molecular mechanisms behind this allosteric activation remain elusive. Here, we used enzymatic assays and in vivo interaction analyses to show that OpcA binds G6PDH under different environmental conditions. However, complex formation enhances G6PDH activity when OpcA is oxidized and inhibits it when OpcA is reduced. To understand the molecular basis of this regulation, we used cryogenic electron microscopy to determine the structure of Synechocystis G6PDH and the G6PDH-OpcA complex. OpcA binds the G6PDH tetramer and induces conformational changes in the active site of G6PDH. The redox sensitivity of OpcA is achieved by intramolecular disulfide bridge formation, which influences the allosteric regulation of G6PDH. In vitro assays reveal that the level of G6PDH activation depends on the number of bound OpcA molecules, which implies that this mechanism allows delicate fine-tuning. Our findings unveil a unique molecular mechanism governing the regulation of the OPP in Synechocystis.

A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.

The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells. However, it might not be able to identify infections with small amounts of samples. In the microscopic detection model, blood stains are placed under a microscope for diagnosing malaria. But accurate diagnosis is hard in this method, particularly in developing nations where the disease is most common. The microscopic detection processes are expensive and time-consuming due to the usage of microscopes. The quality of the blood smears and the availability of a qualified specialist, who is skilled in recognizing the disease, impact the accuracy of malaria detection results. The traditional deep learning-based malaria identification models need more processing power. Therefore, a deep learning-based adaptive method is designed to detect malaria cells through the medical image. Hence, the images are gathered from the standard sites and then fed to the segmentation process. Here, the abnormality segmentation is carried out with the help of a developed Trans-MobileUNet + + (T-MUnet + +) network. Trans-MobileUNet + + captures global context, so it is well-suited for segmentation tasks. The segmented image is applied to the adaptive detection phase where the Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model is designed for the effective recognition of malaria cells. The efficiency of the designed approach is elevated by optimizing the parameters from the AA-CRMV2 network with the help of the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm. Several experimental analyses are evaluated in the implemented model over classical techniques to display their effectualness rate.

High speed and low power hybrid adder using 22 nm SG-TG FinFET

Abstract This paper demonstrates a new 22-T (transistor) hybrid adder for low-power applications using 22 nm shorted gate (SG) tri-gate (TG) FinFET technology. Large conducting channels and increased gate control make FinFET suitable for high-speed low-power computing circuits. The proposed hybrid adder is designed using pass transistor and CMOS logic. Also, it adopts the state-of-the-art techniques of dynamic leakage balancing on the ground path and stacking ground transistors that reduce parasitic effects and increase the speed of the logic transitions compared to traditional designs. Extensive simulations were executed to characterize for PDP (Power Delay Product), power dissipation, and delay of the hybrid adder circuit. From the Monte Carlo analysis, the robustness and efficiency of the circuit are also observable for minimum variation in power and delay due to process voltage and temperature (PVT) variations. Besides, concerning the supply variability and temperature, the circuit shows excellent stability by keeping the power consumption and delay performance consistent. The proposed 22-T hybrid adder using 22 nm SG-TG FinFET are very well-suited in low power and high-speed processors showing highly stable behaviour with reduced process variability.

Investigating the Effects of Remapping Method in Explosion

Using real explosives to detect explosion effects is a difficult and impractical method due to its danger and cost. Performing numerical analyses based on previously conducted experimental studies allows for the diversification of studies on this subject. Since the elaboration of numerical analyses causes an increase in the amount of processing, it requires the devices that need to perform these operations to be very advanced. It is an important optimization that the solutions are shorter and closer to reality, and this can be done with the remapping method. With this method, the explosion loads obtained from 1D analysis can be integrated into 2D and 3D analyses with certain methods, and the duration of these analyses is reduced and their accuracy is increased. Within the scope of this study, the methods and approaches for the application of this method are explained. As a result of this study, it will help those who want to perform explosion simulations to obtain more accurate results in a shorter time with less processing power, and it will pave the way for many scientific studies to be carried out.

Design and implementation of deep neural network hardware chip and its performance analysis

The artificial neural network (ANN) with a single layer has a limited capacity to process data. Multiple neurons are connected in the human brain, and the actual capacity of the brain lies in the interconnectedness of multiple neurons. As a specified generalization of ANN deep learning makes use of two or more hidden layers, which implies that a greater number of neurons are required to construct the model. A network that has more than one hidden layer, also known as two or more hidden layers, is referred to as a deep neural network, and the process of training such networks is referred to as deep learning. The research article focuses on the design of a multilayer or deep neural network presented for the target field programmable gate array (FPGA) device spartan-6 (xc6stx4-2t9g144) FPGA. The simulation is carried out using Xilinx ISE and ModelSim software. There are two hidden layers in which (2×1) multiplexer blocks are utilized for processing twenty neurons into the output of ten neurons in the first hidden layer and demultiplexers (1×2) and vice versa. The hardware utilization is estimated on FPGA to compute the performance of the deep neural hardware chip based on memory, flip flops, delay, and frequency parameters. The design is scalable and applicable to various FPGA devices, which makes the work novel. FPGA-based neuromorphic hardware acceleration platform with high speed and low power for discrete spike processing on hardware with great real-time performance.

Effects of microwave pretreatment on the physicochemical properties of enzyme-infused carrots

We investigated the possibility of textural modifications in older individuals with dysphagia by administering microwave treatment to enzyme-infused carrots. Microwave pretreatment conditions, including processing time and power levels (120, 400, 640, and 800 W), were examined for their impact on texture softening efficiency, microstructure, β-carotene content, and color. Regression analysis confirmed that processing time substantially influenced carrot hardness, with higher the softening rate increasing as microwave power levels rose from 120 to 800 W. The log-linear model demonstrated a strong correlation coefficients (R2 = 0.9732–0.9407) across different power levels, confirming its reliability in achieving the target texture. The derived times, based on a log-linear model, successfully reached a target hardness of 5.0 × 104 N/m2 (KS level 2), with times of 371 s at 120 W, 198 s at 400 W, 72 s at 640 W, and 61 s at 800 W. The samples pretreated for the derived times corresponding to 120, 400, 640, and 800 W exhibited varying porosity levels, influencing the quality of enzyme-infused carrots. With increasing microwave power, the microwave-pretreated carrots showed improved color (more yellow and red) and preservation of β-carotene, potentially linked to shorter processing times. These results suggest that microwave pretreatment followed by enzyme infusion is an efficient approach to softening carrot texture without compromising its quality, and the log-linear model offers a predictive framework for modifying food textures in dysphagia management.

Artificial Intelligence Integration in Biochips: Enhancing Diagnostics and Precision Medicine

Abstract: The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi- functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

DEEP LEARNING METHODS COMPARISON ON IMAGES OF TOMATO AND CUCUMBER LEAF IDENTIFICATION

Deep learning and image processing applications have become widespread, thanks to hardware developments and increased processing power. The use of technology in agriculture is increasing rapidly with the development of technology. One of the recent applications of technology in agriculture is image processing applications using deep learning. Image processing is aimed at sustainable agriculture. Deep learning is used in applications such as disease detection, agricultural spraying, maturity granding, irrigation, fertilization. In this study, deep learning models AlexNet and SqueezeNet are used to classify tomato and cucumber leaf images. 30 tomato leaves and 30 cucumber leaves are photographed to create the dataset used in the study. Afterwards, the images obtained are increased with data augmentation methods and a data set is created. The dataset consists of 2 classes and a total of 300 images. The data set is used 70% for training and 30% for validation. The results obtained from AlexNet and SqueezeNet deep learning models are given comparatively.

Reconfigurable logic-in-memory circuits with ferroelectric nanosheet field-effect transistors

Abstract To accommodate the requirements of small device dimensions and the application of ferroelectric field-effect transistors (FeFETs) in logic-in-memory circuits, we realize the reconfigurable logic-in-memory circuits with ferroelectric nanosheet field-effect transistors (FeNSFETs) through simulation. By evaluating the memory window and logic performances of the devices, it is demonstrated that the key to constructing logical functions are the magnitudes of the drain current at program (PGM) and erase (ERS) state when V G = 0 V. We find that appropriately increasing the number of nanosheets can enhance the current at PGM state, and appropriately increasing the write voltage can decrease the current at ERS state, which are both beneficial for the achievement of reconfigurable circuits. Thus, it is necessary to take into consideration the trade-offs between the process complexity, power consumption, and logical function realization. This work contributes to the prospective design for ferroelectric reconfigurable logic-in-memory circuits.

Young white children's (not so) innocent wonderings about skin color

Issues of race and racism in early childhood education remain under-researched in Denmark—a country where the ideology of equity is particularly prevalent. However, international research on race and racism has long shown that children as young as three to four years of age can express stereotypes and develop prejudicial attitudes related to race, leading to exclusionary social behaviors. This article presents a microsocial analysis of how skin color becomes a socially constructed site of power and access in children's social processes of differentiation. The article is written from the perspective of childhood sociology and is based on participant observations from critical ethnographic-inspired fieldwork conducted in a Danish kindergarten. The analysis shows how normative whiteness orientates two white five-year-old boys’ interpretations of a four-year-old Black girl's dark-complexioned skin. By extension, the article discusses why young white children's meaning-making wonderings about skin color should not be considered an innocent everyday part of kindergarten life. Finally, the article considers the role of early childhood educators when emphasizing the importance of recognizing early childhood education settings as critical sites in the (re)production and disruption of contemporary issues of race and racism.

Processing characteristics of C/C composites with nanosecond pulse laser

This study investigates the effects of the nanosecond pulse laser parameters on the processing characteristics of carbon fiber-reinforced carbon matrix (C/C) composites through a series of laser cutting experiments. It compares the cutting width, depth, heat-affected zone (HAZ) width, processed surface micro-morphology, as well as the oxidation characteristics of the material under different processing parameters. The HAZ width is analyzed in relation to the variations in the oxygen content. The results indicate that both the cutting width and depth initially increase and then decrease with increasing repetition frequency. Moreover, increasing the laser power and pulse width gradually increases the cutting width, depth, and HAZ width, whereas a higher scanning speed tends to reduce the cutting width and depth. Since changes in the processing power, scanning speed, and pulse width affect the HAZ, the oxygen content exhibits a proportional relationship with the HAZ width. In addition, an increase in the repetition frequency can expand the HAZ while reducing the oxygen content. A careful increase in the repetition frequency aids in lowering the ablation threshold of the material, leading to more material removal. Nevertheless, a continuous increase in energy density can trigger a fiber pull-out phenomenon and cause degradation of the matrix.

Blockchain-based energy consumption approaches in IoT

Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT.

An innovative framework to securely transfer data through the Internet of Things using advanced generative adversarial networks

The Internet of Things (IoT) is developing so quickly that cloud-centric computing finds it challenging to keep up with the demands of usability and low latency. Edge computing unifies computing, networking, storage and applications into a distributed open system. At the edge of the IoT, it provides intelligent services. The edge network is made up of several wired and wireless networks, and edge nodes have constrained amounts of memory and processing power. The edge network is vulnerable to several types of cyberattacks because of these factors. Large-scale network data gathering and detection for IoT security is also challenging for an IoT edge node to provide. Data analytics for intrusion detection guarantees high accuracy of intrusion detection systems (IDSs), but the implementation of such algorithms on IoT might be a challenge due to the limited resources on edge nodes. Inspired in part by these challenges, we suggest a sophisticated IDS based on a generative adversarial network (GAN). This article suggests a novel method for detecting intrusions in the IoTs networks that make use of Colony Predator Algorithm (CPA) for optimization of the detection process. Through the use of GANs to create real-looking data samples, the suggested technique helps to have reliable anomaly and intrusion detection. The integration improves the training process, making it more efficient and enabling the CPA system to better differentiate between benign and malicious activities, which exponentially raises the system's efficiency.

Reducing Makespan and Enhancing Resource Usage in Cloud Computing With ESJFP Method: A New Dynamic Approach

ABSTRACTA well‐designed web‐based tool or application allows consumers to access on‐demand services on a pay‐per‐use basis via the internet in the cloud computing service paradigm. Cloud computing continues to be a leading technology trend, with a primary focus on optimizing and enhancing end‐user applications. The increasing challenge of meeting the diverse needs of clints for service providers is propelling the development of load scheduling algorithms. Some of the current scheduling algorithms used in cloud computing include First Come First Serve (FCFS), Shortest Job First (SJF) and Round Robin (RR). The response time and makespan—the interval between the start and end times of consecutive tasks on the same machine—are the two most crucial load balancing elements. Response time refers to the duration a server takes to respond to a client's request. Measured in milliseconds, this timer starts when a client sends a request and stops when the server sends its initial response. This study examines many load balancing methods and suggests improvements for cloud computing's Shortest Job First (SJF) methodology. To minimize makespan and maximize resource usage, we provide a solution that is Enhanced Shortest Job First with Priority (ESJFP) load scheduling algorithms. Under the ESJFP method, computers with more processing power are assigned the longest tasks with higher MIPS (million instructions per second) needs, while machines with lesser processing capacity are assigned the shortest jobs with lower MIPS requirements. By giving equal priority to all activities, this technique makes sure that neither high‐MIPS nor low‐MIPS jobs have to wait an extended period of time for resource allocation.

A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices

AbstractIntra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.

Formal Verification with ABV : A Superior Alternative to UVM for Complex Computing Chips

This article explores the evolution and effectiveness of formal verification enhanced with Assertion-Based Verification (ABV) as a superior alternative to traditional Universal Verification Methodology (UVM) in complex computing chip design. Through analysis of implementation data from major semiconductor companies, including Intel's Core i7 and IBM's POWER processors, the article demonstrates how formal methods achieve up to 100% coverage of critical modules compared to UVM's typical 80-85% coverage. The research presents quantitative evidence of formal verification's advantages, including a 25-30% reduction in verification cycles, a 40% increase in security vulnerability detection, and a 55% reduction in overall verification time through advanced techniques such as compositional verification and proof decomposition. The article also addresses scalability challenges and resource optimization strategies, providing a comprehensive framework for implementing formal verification in modern chip design workflows.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning

This paper introduces JEMADAR-AI, an approach to energy management within smart grids, leveraging an edge-cloud continuum architecture coupled with Deep Q-Learning to optimize the operation of smart home devices. The main hypothesis of this work is that combining advanced machine learning models with edge-cloud computing can significantly improve energy efficiency and cost savings in smart grids. The proposed system utilizes SARIMA for seasonal trends and LSTM for long-term dependency models to forecast energy consumption and production, enabling proactive decision-making to balance supply and demand in real-time. JEMADAR-AI employs a deep reinforcement learning algorithm (Deep Q-Learning) to optimize appliance operations, dynamically adjusting energy usage based on predicted demand and supply fluctuations. This ensures that household energy consumption aligns with production capabilities, particularly during periods of renewable energy generation. The architecture combines the high processing power of cloud computing for long-term forecasting with the low-latency responsiveness of edge computing for real-time appliance control. This Edge-Cloud Continuum approach provides an efficient solution for managing energy in distributed smart grids. The experimental results, obtained using Gridlab-D and Omnet++ simulations, demonstrate that JEMADAR-AI improves decision-making speed by 32.25% and reduces household energy bills by 22.11% compared to traditional cloud-based systems.