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In this article, we present dielectric modulation-based label free detection of biomolecules at a cavity under gate metal located near the tunneling junction of charge plasma based dual metal-double gate tunnel FET (CP-DM DGTFET). The biomolecules that settle down in the cavity act as dielectric above the tunneling junction and cause drain current to increase/ decrease. To eliminate the short channel impact and to enhance ION/IOFF ratio without compromising any other device features, dual metal gate architecture with laterally split dielectric is used. The biosensor is capable of recognizing neutral, positive, and negative charges with the highest drain current sensitivity of $2.9\times 10 ^{9}$ Ccm-2 and highest ION/IOFF ratio of $9.02\times 10 ^{11}$ for biomolecule gelatin with dielectric constant k =12. This work explores the effectiveness of charge-plasma based DM DGTFET for detection of biomolecules such as gelatin (k =12), keratin (k =8), bacteriophage T7 (k =6.3), and APTES (k =3.57) with varied charge distribution at various biasing. The drain current sensitivity, subthreshold slope, drain current, ON/OFF current ratio are computed for various biomolecules and their variations with respect to cavity length, biomolecule charge, etc. are used to detect unknown biomolecules. Finally, RF analysis such as gain bandwidth product, cut-off frequency, transit time, and linearity/selectivity of biosensors are incorporated. The device shows better linearity and less distortion for high –k biomolecules. The results of our studies are reproducible for repeated computed analysis.

Air pollution is a critical global concern, demanding precise air quality forecasting to mitigate its severe consequences. Our study introduces Future Air Quality Prediction using Long Short-Term Memory based on Hyper Heuristic Multi-Chain Model (H2MCM) to project future air quality, considering various meteorological factors (MFs) and pollution-related variables like atmospheric pressure, temperature, humidity, and wind patterns. Leveraging 12 units of Long Short-Term Memory neural networks (LSTMs), H2MCM accurately predicts forthcoming air pollutants (APs) concentrations such as particulate matter with diameter $2.5 \; \mu $ m (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2). Additionally, it accounts for spatiotemporal correlations between these APs and MFs, which significantly influence the air quality prediction for the next immediate time interval. H2MCM utilizes a multi-chain mechanism, employing 1-hour prediction models to forecast air quality hourly, enabling approximations for the next 12 hours. Also, for an efficient model selection, Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), Hannan-Quinn Information Criterion (HQIC), and corrected AIC (AICc) tools are used based on their ability to balance model fit and complexity. Furthermore, it demonstrates the ability to enhance the performance of any predictor. Experimental results substantiate H2MCM’s superiority over various models, including the Support Vector Regressor (SVR), Multi-Layer Perceptron (MLP), Recurrent Air Quality Predictor (RAQP), and Valchogianni models. H2MCM achieves impressive up to 75% better accuracy and consistency compared to SVR, 60% better than MLP, 38% better than RAQP, and 70% better than Valchogianni models.

In the paradigm of Device-to-Device (D2D) and System-on-Chip (SoC) communications, the optimization of high-speed data transfer while minimizing hardware resource utilization imposing paramount importance. Traditional methodologies have often resulted in undesirable outcomes, such as inflated area, latency, and power consumption. Consequently, this study adopts a focused approach by introducing an innovative Route-Controlling Network-on-Chip (RC-NoC) architecture, integrating critical components like FIFO-Buffer, crossbar switching, route control, and arbiter modules. The operational sequence begins with the FIFO-Buffer logic, which strategically manages data storage from diverse devices, allocating and organizing data flow based on distinct IP addresses. Subsequently, the route controller module orchestrates the operation of heterogeneous routers within the crossbar switching fabric, implementing a prioritized scheduling scheme. The arbiter plays a crucial role in overseeing and directing data flow from source to destination, leveraging request-level prioritization for efficient data transmission. Empirical evidence from simulations unequivocally demonstrates the superiority of the proposed RC-NoC architecture. Comparative analysis against essential state-of-the-art NoC architectures reveals significant enhancements in key metrics, notably area efficiency, latency reduction, and power optimization, with over 45% improvement observed across these metrics compared to GALS-NoC, F-NoC, and CE-NoC in the Orion framework. This robust validation substantiates the efficacy and technical prowess of the RC-NoC design paradigm in addressing the challenges of modern D2D and SoC communication paradigms. Furthermore, the optimization process notably decreased routing costs for different packets, with Router2 exhibiting a 40% reduction in Packet1’s routing cost and Router3 displaying a 44% decrease in Packet2’s cost. Moreover, the attained router capacities demonstrated an average increase of 17%, significantly enhancing network efficiency.

The paper investigates the terahertz performance of a mutually injection-locked multi-element high electron mobility avalanche transit time (HEM-ATT) source based on AlGaN/GaN two-dimensional electron gas (2-DEG). Utilizing a nanostrip patch type planar coupling circuit, mutual injection locking between adjacent elements is achieved. The paper provides a comprehensive analysis of the integrated power combining technique in the mutually injection-locked multi-element HEM-ATT oscillator. A ten-element mutually injection-locked integrated power combined source is designed for operation at 1.0 THz, and simulation studies are conducted to examine its DC, large-signal, and avalanche noise characteristics. The capability of generating a narrow-band terahertz wave is verified by introducing various levels of structural mismatches between the elements. Results indicate that the ten-element HEM-ATT oscillator can deliver 2.27 W peak power with a 17% DC to THz conversion efficiency at 1.0 THz. The average noise measure of the oscillator is found to be 12.54 dB. Additionally, the terahertz performance of the mutually injection-locked ten-element HEM-ATT oscillator is compared with other state-of-the-art THz sources to evaluate its potentiality as an excellent integrated THz radiator.

The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents’ environment and well-being. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM model enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

The device dimension down scaling beyond 14 nm technology node utilization of device architecture and new materials is a need of the semiconductors industry. In this paper, three materials, In $_{\mathrm {1-x}}$ GaxAs/ In $_{\mathrm {1-x}}$ GaxP, In $_{1-}$ xGax GAA JL FET developed and analyzed for their analog performance at high temperature. In $_{\mathrm {1-x}}$ GaxAs has a composition of 53% InAs and 47% GaAs with a band gap of 0.75 eV, whereas In $_{\mathrm {1-x}}$ GaxP and In $_{\mathrm {1-x}}$ GaxN exhibit a wider band gap of 1.90 eV and 3.2 eV, respectively, and exhibit different analog performance. An analytical model that is temperature-dependent for the drain current of In $_{\mathrm {1-x}}$ GaxAs, In $_{\mathrm {1-x}}$ GaxP, In $_{\mathrm {1-x}}$ GaxN GAA JL FET with mobility variations has also been developed. The ratio of transconductance-to-normalized drain current (gm/I $_{\mathrm {DS}}$ ) is utilized to examine analog properties. This ratio is a crucial measure of analog performance since it reveals the sensitivity and linearity of the device. All device parameters such as transconductance (g $_{\mathrm {m}}$ ), gm/IDS, cut-off frequency, and intrinsic gain were investigated for higher temperatures ranging from T =300 K to T =500 K. The newly developed device parameters have very low-temperature sensitivity, making these three material devices potential candidates for high temperature applications.

Tunicate Swarm Algorithm (TSA) is a novel swarm intelligence algorithm developed in 2020. Though it has shown superior performance in numerical benchmark function optimization and six engineering design problems over its competitive algorithms, it still needs further improvements. This article proposes two improved TSA algorithms using chaos theory, opposition-based learning (OBL) and Cauchy mutation. The proposed algorithms are termed OCSTA and COCSTA. The static and dynamic OBL are used respectively in the initialization and generation jumping phase of OCTSA, whereas centroid opposition-based computing is used, in the same phases, in COCTSA. The proposed algorithms are tested on 30 IEEE CEC2017 benchmark optimization problems consists of unimodal, multimodal, hybrid, and composite functions with 30, 50, and 100 dimensions. The experimental results are compared with the classical TSA, TSA with the local escaping operator (TSA-LEO), Sine Cosine Algorithm (SCA), Giza-Pyramid Construction Algorithm (GPC), Covariance Matrix Adaptation Evolution Strategy (CMAES), Archimedes Optimization Algorithm (AOA), Opposition-Based Arithmetic Optimization Algorithm (OBLAOA), and Opposition-Based Chimp Optimization Algorithm (ChOAOBL). The statistical analysis of experimental results using the Wilcoxon Signed Rank Test establishes that the proposed algorithms outperform TSA and other algorithms for most of the problems. Moreover, high dimensions are used to validate the scalability of OCTSA and COCTSA, and the results show that the modified TSA algorithms are least impacted by larger dimensions. The experimental results with statistical analysis demonstrate the effectiveness of the proposed algorithms in solving global optimization problems.

For an extended period, a technological architecture known as cloud IoT links IoT devices to servers located in cloud data centers. Real-time data analytic are made possible by this, enabling better, data-driven decision making, optimization, and risk reduction. Since cloud systems are often located at a considerable distance from IoT devices, the rise of time-sensitive IoT applications has driven the requirement to extend cloud architecture for timely delivery of critical services. Balancing the allocation of IoT services to appropriate edge nodes while guaranteeing low latency and efficient resource utilization remains a challenging task. Since edge nodes have lower resource capabilities than the cloud. The primary drawback of current methods in this situation is that they only tackle the scheduling issue from one side. Task scheduling plays a pivotal role in various domains, including cloud computing, operating systems, and parallel processing, enabling effective management of computational resources. In this research, we provide a multiple-factor autonomous IoT-Edge scheduling method based on game theory to solve this issue. Our strategy involves two distinct scenarios. In the first scenario, we introduced an algorithm containing choices for the IoT and edge nodes, allowing them to evaluate each other using factors such as delay and resource usage. The second scenario involves both a centralized and a distributed scheduling approach, leveraging the matching concept and considering each other. In addition, we also introduced a preference-based stable mechanism (PBSM) algorithm for resource allocation. In terms of the execution time for IoT services and the effectiveness of resource consolidation for edge nodes, the technique we use achieves better results compared with the two commonly used Min-Min and Max-Min scheduling algorithms.

The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

Nowadays, data is ubiquitous and has a significant influence on our day-to-day activities due to the emergence of high speed internet and widespread use of sensor-enabled Internet of Things (IoT) devices. Owing to major improvement in sampling rate of sensors in recent years, a low variation of the sensed physical parameters is witnessed during a small time interval of observation, which in turn shows high correlation in time domain. Data-critical applications like personal healthcare monitoring, video surveillance, and other applications where data dropping creates significant barriers are attracted by high correlation data. However, due to their power-constrained nature, such applications do not benefit much from the transmission of redundant data. The ideal solution to this problem could possibly be achieved by adopting typical lossless source coding strategy with low-complex design. This paper presents a novel encoding scheme termed as Dual Run Distribution based Encoding (DRDE) scheme by exploiting high correlation of sensor data to suitably encode them using symbol run statistics, leading to a reduced length of data with a very large percentage of 0s. Employing silent symbol based communication, the transmitter can be kept in silent state during periods of the most dominant symbol ’0’ in the encoded messages and using a hybrid $FSK-ASK$ modulation/demodulation technique for communication with a non-coherent receiver results in a significant reduction in transmitter energy. We simulate the proposed sensor data encoding technique on real-life data with low-cost, low data-rate transceivers like CC2420. Simulation results show about 88% (theoretical) and 79-82% (practical) savings in transmitter and 12% (theoretical) 23.5% (practical) savings in receiver energy over conventional BFSK with real-life sensor dataset. Furthermore, our proposed method outperforms in terms of overall energy savings and reduction of $CO_{2}$ footprint, generating 1.48 – 0.041 mg/day, which is 78% lesser than conventional BFSK modulation scheme, making our proposed scheme suitable for sustainable IoT applications in WSN compared to existing schemes. Furthermore, we investigate the influence of various data compression algorithms on computation time, CPU power consumption, and transmission cost on an LPC2148 microcontroller built upon a 16-bit/32-bit ARM7TDMI chipset.

Access to reliable electricity is crucial for rural development and improving the quality of life in remote areas. Standalone photovoltaic PV systems and hybrid power systems HPS are promising solutions for rural electrification. However, frequency stabilization is a critical challenge in such systems, and conventional control techniques are inadequate. This work proposes a novel approach to optimize the PID controller gains for the standalone PV-based isolated HPS (IHPS). The student psychology-based optimization algorithm (SPBOA) and quasi-oppositional-based whale optimization algorithm (QOWOA) are employed to enhance the performance of the IHPS models separately. The objective function considered is the integral of time absolute error (ITAE), and the frequency response profile is studied in the presence of the proposed SPBOA and QOWOA-based PID controllers. The results show that the proposed SPBOA outperforms the QOWOA in terms of reducing the ITAE by nearly 5% and minimizing the peak and settling time of the frequency response by 5%-7% in different scenarios. The transient responses of the systems to different input conditions are also analyzed, indicating that both the IHPS-I and IHPS-II models are feasible for remote rural electrification. Overall, the proposed approach offers an efficient solution to optimize the PID controller gains for frequency stabilization in standalone PV and HPS. The results demonstrate the benefits of using the SPBOA and QOWOA algorithms to enhance the performance of the systems, making them suitable for remote rural electrification.

In recent years, blockchain technology has gained significant attention for its potential in various domains. However, the lack of interoperability between different blockchain platforms poses a significant challenge in meeting the demands of the modern world. To address this issue, our research focuses on unlocking blockchain interconnectivity through smart contract-driven cross-chain communication. We aim to contribute to the development of a model that enhances the functionality and usability of blockchain technology. To achieve interoperability, we explore various options and leverage the power of smart contracts. These contracts enable seamless communication and exchange of services between different blockchain platforms, as well as with legacy systems. By implementing our proposed model, we intend to bridge the gap between isolated blockchain systems, enabling them to work together efficiently and effectively. To validate the effectiveness of our model, we have conducted a comparative analysis with existing parent blockchain models for cross-chain communication. We have measured and compared their mean and standard deviation, which indicate the performance improvements achieved by our approach. The results demonstrate that our model outperforms the existing parent blockchain model, offering better cross-chain communication capabilities and showing a 42.27% improvement in efficiency as well. Through our research, we aim to contribute to the advancement of blockchain technology by addressing the critical issue of interoperability. By enabling seamless communication and collaboration between different blockchain platforms, our model has the potential to revolutionize various industries and unlock new opportunities for innovation and growth.

Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.