Allprimescanbeindexedbyk,asprimesmustbeintheform of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k.

This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.

Next word prediction is an input technology that simplifies the process of typing by suggesting the next word to a user to select, as typing in a conversation consumes time. A few previous studies have focused on the Kurdish language, including the use of next word prediction. However, the lack of a Kurdish text corpus presents a challenge. Moreover, the lack of a sufficient number of N-grams for the Kurdish language, for instance, five grams, is the reason for the rare use of next Kurdish word prediction. Furthermore, the improper display of several Kurdish letters in the Rstudio software is another problem. This paper provides a Kurdish corpus, creates five, and presents a unique research work on next word prediction for Kurdish Sorani and Kurmanji. The N-gram model has been used for next word prediction to reduce the amount of time while typing in the Kurdish language. In addition, little work has been conducted on next Kurdish word prediction; thus, the N-gram model is utilized to suggest text accurately. To do so, R programming and RStudio are used to build the application. The model is 96.3% accurate.

Building the innovative blockchain-based architecture across the Internet of Things (IoT) platform for the education system could be an enticing mechanism to boost communication efficiency within the 5 G network. Wireless networking would have been the main research area allowing people to communicate without using the wires. It was established at the start of the Internet by retrieving the web pages to connect from one computer to another computer Moreover, high-speed, intelligent, powerful networks with numerous contemporary technologies, such as low power consumption, and so on, appear to be available in today’s world to connect among each other. The extension of fog features on physical things under IoT is allowed in this situation. One of the complex tasks throughout the area of mobile communications would be to design a new virtualization framework based on blockchain across the Internet of Things architecture. The goal of this research is to connect a new study for an educational system that contains Blockchain to the internet of things or keeping things cryptographically secure on the internet. This research combines with its improved blockchain and IoT to create an efficient interaction system between students, teachers, employers, developers, facilitators and accreditors on the Internet. This specified framework is detailed research’s great estimation.

Connecting a large number of intermittent distributed generators (DGs) and electric vehicles (EVs) to a distribution network increases uncertainty and seriously threatens the safety and economic operation of that distribution network. Robust optimization is an important method of hedging against the adverse effects of uncertainty. However, the existing literature on robust optimization of distribution networks has not taken into account constraints on the travel distances of regulating equipment. In response to this deficiency, this paper considers constraints on the cycle period of energy storage systems (ESSs), travel distances of switchable capacitors and reactors, and load tap changers (OLTCs) and step voltage regulators (SVRs). A two-stage multi-period mixed-integer second-order cone robust model for coordinated active and reactive power optimization of a distribution network is formulated based on branch flow equations. There are not any dummy variables in the second stage model. In contrast to the deficiency of the low computational rate of the column-and-constraint generation (CCG) method, an iterative solution to the first and second stage models is proposed based on the cutting plane. Increases to the variables and constraints are not needed to solve the first stage model. For the second stage multi-period model, only the model of each single period needs to be solved first. Then the results of the two models are accumulated. Overall, the computational speed is significantly enhanced. The capabilities of the proposed method are validated by three simulation cases.

We experimentally demonstrate enhanced self-phase modulation in silicon nanowire waveguides integrated with layered graphene oxide films. We achieve spectral broadening of optical pulses in the GO-silicon waveguide with a broadening factor up to 2.96.

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for hydroelectric power. This paper proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine’s rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, followed by the application of a K-Nearest Neighbor (KNN) machine learning algorithm for fault detection and severity classification. The framework’s effectiveness is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that pitch imbalance faults can be accurately detected 100% of the time and classified based upon severity more than 97% of the time.

There are many task in surveillance monitoring such as object detection, person identification, activity and action recognition etc. Integrating variety of surveillance task through a multimodal interactive system will benefit real-life deployment, and will also support human operators. We first introduce a dataset which is first of its kind and named as Surveillance Video Question Answering (SVideoQA) dataset. The multi-camera surveillance monitoring aspect is considered through the multimodal context of Video Question Answering (VideoQA) in the SVideoQA dataset. This paper proposes a deep learning model where VideoQA task on the SVideoQA dataset is attempted to solved in a manner where memory-driven relationship among appearance and motion aspect of the video features are captured. At each level of the relational reasoning respective attentive parts of the context of the motion and appearance features are identified forwarded through frame level and clip level relational reasoning module. Also, respective memories are updated which are again forwarded to the memory-relation module to finally predict the answer word. The proposed memory-driven multilevel relational reasoning is made compatible with the surveillance monitoring task through the incorporation of multi-camera relation module, which is able to capture and reason over the relationships among the video feeds across multiple cameras. Experimental outcome exhibits that the proposed memory-driven multilevel relational reasoning perform significantly better on the open-ended VideoQA task compared to other state-of-the art systems. The proposed method achieves an accuracy of 57\% and 57.6\% respectively for the single-camera and multi-camera task of the SVideoQA dataset.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient’s condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation

We experimentally demonstrate enhanced four-wave mixing in micro-ring resonators (MRRs) integrated with graphene oxide films. We achieve up to ~7.6-dB enhancement in conversion efficiency for a uniformly coated MRR and ~10.3-dB for a patterned device.

We developed an MPC motion controller for a mobile robot to follow a dynamic object. The main contribution is to find an adjustable transient response characteristic in a special formulation of MPC and apply to a real robot platform.

The home automation system is indeed a system that offers a mobile application to monitor it through the smartphone or tablet. It can control home appliances like light, fans, air conditions, and smart security locks, etc. Bluetooth or Wi-Fi is used for remote control of things. Smart home is essentially how it looks: automating with such an easy touch of a button or a voice command the ability to manage things across the home. Several operations were both simple and extremely low-cost, such as setting up a light to turn on and off at the time of your moment. Others need much more money and resources, including advanced spy cameras. Some people thought that technology is taking a really huge part of our lives. It does! we’re living in a modern generation where smart and intelligent systems are necessary to be there wherever we are to make our lives easier and much better, for example, we can do many things faster, better, and more accurate.

This paper contains field research on problems in mortgage and real estate transactions and a technological solution proposal for these issues. Interviews were conducted on players in the mortgage and real estate industries in Saint Louis, Missouri. These interviews led to conversations revealing systematic issues and trends in certain processes of mortgages and real estate transactions. Furthermore, a survey of 40+ year old homeowners and mortgage customers was conducted using Survey Monkey, which asked questions relating to customer dissatisfaction, bottlenecks and customer opinions of the mortgage process. These responses gave insight on what issues mattered to mortgage customers the most. Some descriptive statistics were derived from the survey results to help interpret them. A proposal for a blockchain based mortgage assistant platform is made at the end of the paper, which explains how blockchain, OCR and chatbots can potentially solve problems found in the survey.

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

The neutral grounding in power distribution system is an important aspect for earth fault protection, power supply reliability and safety. The performance varies greatly with different grounding methods by which the protective effect presents various results with identical impedance of single phase earth fault. Arguments for better neutral protection has been continued in the distribution field for decades, unfortunately, there is still not a conclusion due to the discussions lacking of a unified modelling or theory of neutral groundings. Thus, the understanding of neutral grounding in most countries differs considerably. Surprisingly solid/isolated grounding in some countries is still considered as a mainstream grounding method in today’s distribution grids, likewise, some utilities are still persisting on adopting resistance grounding to pursue to improve detection sensitivity and reliability, and so on. In this paper, a unified theory is proposed to shed light on the neutral groundings within one unprecedented modelling by which neutral groundings can be compared and evaluated quantitatively for the first time in the history of power distribution field perhaps.

Solving the Visual Question Answering (VQA) task is a step towards achieving human-like reasoning capability of the machines. This paper proposes an approach to learn multimodal feature representation with adversarial training. The purpose of the adversarial training allows the model to learn from standard fusion methods in an unsupervised manner. The discriminator model is equipped with a siamese combinatin of two standard fusion method namely multimodal compact bilinear pooling and multimodal tucker fusion. Output multimodal feature representation from generator is a resultant of graph convolutional operation. The resultant multimodal representation of the adversarial training allows the proposed model to infer the correct answers from open-ended natural language questions from the VQA 2.0 dataset. An overall accuracy of 69.86\% demonstrates the accuracy of the proposed model.