Overview Of Architecture Research Articles

Cryo-EMstructure of the Smc5/6 holo-complex.

The Smc5/6 complex plays an essential role in the resolution of recombination intermediates formed during mitosis or meiosis, or as a result of the cellular response to replication stress. It also functions as a restriction factor preventing viral replication. Here, we report the cryogenic EM (cryo-EM) structure of the six-subunit budding yeast Smc5/6 holo-complex, reconstituted from recombinant proteins expressed in insect cells – providing both an architectural overview of the entire complex and an understanding of how the Nse1/3/4 subcomplex binds to the hetero-dimeric SMC protein core. In addition, we demonstrate that a region within the head domain of Smc5, equivalent to the ‘W-loop’ of Smc4 or ‘F-loop’ of Smc1, mediates an important interaction with Nse1. Notably, mutations that alter the surface-charge profile of the region of Nse1 which accepts the Smc5-loop, lead to a slow-growth phenotype and a global reduction in the chromatin-associated fraction of the Smc5/6 complex, as judged by single molecule localisation microscopy experiments in live yeast. Moreover, when taken together, our data indicates functional equivalence between the structurally unrelated KITE and HAWK accessory subunits associated with SMC complexes.

Capsule networks for image classification: A review

Over the past few years, the computer vision domain has evolved and made a revolutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain with its roots deeply correlated with neuroscience; any new findings that trigger a more intuitive understanding and working of the human visual system generally impact the design strategy of computer vision algorithms. The convolutional neural network is one such algorithm that is currently the de facto standard for most computer vision tasks such as image classification, object detection, image segmentation, etc. As convolutional neural networks are associated with inherent constraints such as the requirement for an immense amount of labeled data and an inefficient data routing policy, capsule networks could be a viable alternative. Upheld by the backpropagation and the dynamic routing algorithm, the capsule network has set the new paradigm for developing reliable computer vision algorithms. Despite the phenomenal theoretical backing from neuroscience and the groundbreaking performance on benchmark datasets, the lack of information concerning the conception and working of capsule networks becomes the major impediment to adopting them for computer vision algorithms. This paper presents a concise overview of capsule network-based classification architectures, routing algorithms, performance analysis, limitations, and future scope, helping the research community to adopt capsule networks at the forefront of modern computer vision research.

When Cache Meets Vehicular Edge Computing: Architecture, Key Issues, and Challenges

Task-oriented caching in vehicular edge computing (VEC) has become a hot topic recently, which brings in many opportunities for capability limited vehicles and makes VEC task offloading and service provision more cost-effective. Most works on caching assisted task offloading in VEC focused mainly on the reuse of computation results and neglected the characteristics pertaining to VEC. In effect, task-oriented caching in VEC is time-sensitive, requiring multiple-choice and multi-granularity. We need to address the issues on different computing and storage capabilities between vehicles and edge server with their diverse applications. Therefore, caching enabled task offloading in VEC aims for exploitation and enhancement of the functions in edge computing. To investigate in depth the benefits of caching enabled task offloading in VEC, we first provide an architectural overview on caching enabled vehicular edge computing (CVEC). Then, we strive to address the most concerned issues, that is, where, what, how and when to cache the tasks in CVEC. Specifically, three caching strategies are introduced to address what to cache in CVEC. In addition, caching strategies are evaluated with respect to response latency and energy consumption at edge servers. Simulation results reveal that caching enabled task offloading in CVEC can remarkably improve the performance of VEC. We also take a look at the opportunities and discuss the challenges in CVEC to shed light on future research directions on CVEC.

Real-Time Big Data Analytics for Data Stream Challenges: An Overview

The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data from a variety of sources, such as sensor networks, web traffic, social media, video streams, and other sources, is a considerable difficulty. The main goal of this paper is to give an overview of the current architecture for real time big data analytics, real-time data stream processing methods available, including their system architectures Lambda, kappa, and delta large data stream processing.

Crustal structures and salt tectonics on the margins of the western Algerian Basin (Mediterranean Region)

We present an overview of the crustal architecture of the continental margins of the oceanic Algerian Basin in the westernmost Mediterranean Sea. During the Cenozoic, and with a variable oblique convergence between the African and Eurasian plates, the Western Mediterranean Sea has experienced thinning and extension behind a tight orogenic arc formed by the Betics, Rif, and Tell Cordilleras. This study is focused on the structural style affecting the Messinian salt layer, which is mostly restricted to the deep domains of the Algerian Basin, where it is floored by a thin oceanic crust of probable Miocene age.Using deep-penetrating seismic profiles and wells from offshore western Algeria to southeastern Spain, we have analyzed the crustal structures affecting the domains close to the oceanic-continent transition on the three margins of the western Algerian Basin. Since the Early Miocene, active shortening in the Tell-Atlas domain has accommodated most of the plate convergence in the basin, whereas the Alboran margin in the west and the Iberian margin in the north experienced eastward and southward crustal extension and thinning, respectively, accompanied by volcanism. The Algerian margin in the south shows incipient thrusting of African continental crust over oceanic crust. This shortening occurred since at least the Late Miocene, also promoting decoupling and contraction of the deep, sub-horizontal Messinian salt layer. The salt exhibits diapir squeezing and suprasalt folding, whereas the presalt sequence preserves partially-inverted half-grabens. Salt tectonic processes along the northern and western margins of the Western Mediterranean Basin show contrasting structural styles formed by narrow extensional and transtensional domains with gentle salt anticlines.This region shows therefore a somewhat unusual salt-tectonic style, departing from the gravity-driven model typical of continental margins that contain an initial continuous, gently-dipping salt layer. In the Algerian Basin, salt is mostly restricted to deep water domain floored by oceanic crust, so it does not participate in significant gravity-driven deformation. Instead, Messinian salt and the suprasalt sequences underwent significant shortening along the southern margin, simultaneous with thick-skinned extension involving the Messinian evaporites in the northern and eastern margins.

Aerial Platform Design Options for a Life-Finding Mission at Venus

Mounting evidence of chemical disequilibria in the Venusian atmosphere has heightened interest in the search for life within the planet’s cloud decks. Balloon systems are currently considered to be the superior class of aerial platform for extended atmospheric sampling within the clouds, providing the highest ratio of science return to risk. Balloon-based aerial platform designs depend heavily on payload mass and target altitudes. We present options for constant- and variable-altitude balloon systems designed to carry out science operations inside the Venusian cloud decks. The Venus Life Finder (VLF) mission study proposes a series of missions that require extended in situ analysis of Venus cloud material. We provide an overview of a representative mission architecture, as well as gondola designs to accommodate a VLF instrument suite. Current architecture asserts a launch date of 30 July 2026, which would place an orbiter and entry vehicle at Venus as early as November 29 of that same year.

Guest Editorial Introduction to the Special Issue on Context Prediction of Autonomous Vehicles

The integration of advanced sensing, signal processing, deep learning, and edge computing into vehicles is enabling intelligent automated vehicles that can navigate autonomously in various environments. There are several exciting developments in new technologies that may contribute to the improvement of the robustness of autonomous vehicles and thus making them safer on the road. However, the development of suitable context prediction methodologies in order to provide proactive behavior for intelligent transportations remains a challenge. The reason is that future context information, hidden in the raw context traces left by users in the real world, is not immediately accessible to applications. Therefore, sophisticated context prediction approaches are required that could discover and mine patterns (e.g., of a driver’s behavior) from observed context history. The major challenge of a context prediction approach is in the prediction accuracy and prediction expressiveness. Neural networks along with deep-learning methods have shown noticeably better performance in comparison with previous methods regarding the accuracy of the outcomes. However, deep learning also issues more complexity and interpretability problems and, hence, arises serious challenges regarding the verifiability of these approaches. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for context prediction of autonomous vehicles.

Guest Editorial Intelligent Autonomous Transportation System With 6G—II

In the near future, the 6G-supported Intelligent Transportation System (ITS) is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport System. Twenty articles were selected for publication in the second part of the issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered factors pertaining to originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data.

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Https://habibiaislamicus.com/index.php/hirj/article/view/222

Proverbs are Words of Wisdom, uttered traditionally, expressing some perceived truth based on common sense or Human Experiences. Proverbs bears Ironic and Metaphorical Nature and are used in Formulaic Structure of Languge. This unique Genre of Folklore enhances the Wisdom Literature. As Proverbs are the Lamps of Speech and are Strategies to deal with the Situations, that is why, for them, it is necessary to have a memorizable Sentence Structures. Studies have proven that almost all Languages of the world possess a large variety of Proverbs and all these Languages have tried to adopte catchy patterns applied to a stuff of Proverbs. These Identified Patterns give a very attractive status to proverbs which make them memorizable and then Retrievable on the occasions when needed. For example (No Guts no Glory) and (No Pain no Gain) etc fits to the formulaic Form of "No X no Y". Such like (Better be Safe than Sorry) and (Better be late than never) etc adopted the Pattern of "Better be X than Y" Another advantage of Structural Patterns adopted by numerous Proverbs is that there is a Possibility of new born Proverbs following the structure of the bona fide ones. For Instance: The Proverb (Where there is a well there is a way) was easily followed by (Where there is a Star, there is a Scandle) The aim of this Article is to provide a general overview of the unique Architecture of English Poroverbs across of some Catchy Patterns which, deals with the Communicable quality indebted to the production of new born Proverbs.

A Summary on the Genetics of Systemic Lupus Erythematosus, Rheumatoid Arthritis, Systemic Sclerosis, and Sjögren's Syndrome.

Systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, and Sjögren's syndrome are four major autoimmune rheumatic diseases characterized by the presence of autoantibodies, caused by a dysregulation of the immune system that leads to a wide variety of clinical manifestations. These conditions present complex etiologies strongly influenced by multiple environmental and genetic factors. The human leukocyte antigen (HLA) region was the first locus identified to be associated and still represents the strongest susceptibility factor for each of these conditions, particularly the HLA class II genes, including DQA1, DQB1, and DRB1, but class I genes have also been associated. Over the last two decades, the genetic component of these disorders has been extensively investigated and hundreds of non-HLA risk genetic variants have been uncovered. Furthermore, it is widely accepted that autoimmune rheumatic diseases share molecular disease pathways, such as the interferon (IFN) type I pathways, which are reflected in a common genetic background. Some examples of well-known pleiotropic loci for autoimmune rheumatic diseases are the HLA region, DNASEL13, TNIP1, and IRF5, among others. The identification of the causal molecular mechanisms behind the genetic associations is still a challenge. However, recent advances have been achieved through mouse models and functional studies of the loci. Here, we provide an updated overview of the genetic architecture underlying these four autoimmune rheumatic diseases, with a special focus on the HLA region.

Disrupted Lessons in Engineering Robotics: Pivoting Knowledge Transfer From Physical to Virtual Learning Environments.

This study examined the effects of an Arduino microrobot activity on college students’ interest in robotics through three specific objectives: (1) determining how students’ conceptual understanding regarding the basics of microcomputing and computer programming changes after engaging in an engineering robotics learning module, (2) assessing the impact of these changes on students’ sense of competence in engineering robotics, and (3) explaining the role of students’ perceived knowledge transferability in the relationship between their sense of competence and changes in their interest for pursuing engineering robotics. Participants (n = 58) were recruited from two Engineering Physics courses and surveyed before (Time 1) and after (Time 2) an Arduino microcomputing learning activity. First, significant increases were reported post-activity for interest in robotics, as well as conceptual understanding of microelectronics and computer programming. Second, changes in the understanding of computer programming significantly predicted students’ sense of competence at Time 2. Finally, high and low levels of competence and perceived knowledge transferability were related to changes in students’ interest in robotics. Moreover, high levels of perceived knowledge transferability alone played an important role in students’ interest in robotics. Transferring complex engineering ideas to novel situations was beneficial regarding students’ learning gains associated with computer programming and with the Arduino microcontroller platform. An overview of the virtual lab architecture used is provided with suggested novel directions for teaching college-level courses about engineering robotics.

Multifocal Near-Eye Display: Timing of Optical Diffuser Elements and Synchronization to DLP-Based Projection Microunit

Abstract The paper present the key technical details of a multifocal near-eye display concept. Along with an overview of the basic architecture, a particular implementation that utilises a digital light processing (DLP®) based spatial light modulator as the image source is provided in the study. The investigated approach involves the utilisation of a small-scale volumetric screen formed by a stack of fast-switching optical diffuser elements based on liquid crystal technology. The volumetric screen is illuminated by a rear image projector. To make the whole system functional and small, the challenge lies within the development of integrated control board for the projection modules as well as the synchronization of the DLP® projector image output to the optical diffuser element switching-cycle. The main difficulty of the development process is accounting for the peculiarities of in-house developed diffuser elements and the off-the-shelf DLP®, which is the main focus of this paper. There is no direct control over the full set of DLP® operational parameters, an indirect method for adjusting frame dead time is proposed, showing that an increase in dead time close to 0.3 ms (from 0.3 ms to 0.6 ms in the particular setup) can be achieved without significantly sacrificing image colour depth or quality. Tuneable dead time mitigates the limitations set by the non-instantaneous switching of liquid crystal diffuser elements as longer dead times allow for the removal of image bleeding between frames.

Deep learning techniques for liver and liver tumor segmentation: A review

Liver and liver tumor segmentation from 3D volumetric images has been an active research area in the medical image processing domain for the last few decades. The existence of other organs such as the heart, spleen, stomach, and kidneys complicate liver segmentation and tumor identification task since these organs share identical properties in terms of shape, texture, and intensity values. Many automatic and semi-automatic techniques have been presented in recent years, in an attempt to establish a system for the reliable diagnosis and detection of liver illnesses, specifically liver tumors. With the evolution of deep learning techniques and their exceptional performance in the field of medical image processing, medical image segmentation in volumetric images using deep learning techniques has received a great deal of emphasis. The goal of this study is to provide an overview of the available deep learning approaches for segmenting liver and detecting liver tumors, as well as their evaluation metrics including accuracy, volume overlap error, dice coefficient, and mean square distance. This research also includes a detailed overview of the various 3D volumetric imaging architectures, designed specifically for the task of semantic segmentation. The comparison of approaches offered in earlier challenges for liver and tumor segmentation, as well as their dice scores derived from respective site sources, is also provided.

A Survey on Memory Subsystems for Deep Neural Network Accelerators

From self-driving cars to detecting cancer, the applications of modern artificial intelligence (AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able to extract high-level features after the network has been trained using statistical learning. However, due to the massive amounts of parallel processing in computations, the memory wall largely affects the performance. Thus, a review of the different memory architectures applied in DNN accelerators would prove beneficial. While the existing surveys only address DNN accelerators in general, this paper investigates novel advancements in efficient memory organizations and design methodologies in the DNN accelerator. First, an overview of the various memory architectures used in DNN accelerators will be provided, followed by a discussion of memory organizations on non-ASIC DNN accelerators. Furthermore, flexible memory systems incorporating an adaptable DNN computation will be explored. Lastly, an analysis of emerging memory technologies will be conducted. The reader, through this article, will: 1—gain the ability to analyze various proposed memory architectures; 2—discern various DNN accelerators with different memory designs; 3—become familiar with the trade-offs associated with memory organizations; and 4—become familiar with proposed new memory systems for modern DNN accelerators to solve the memory wall and other mentioned current issues.

Health Informatics: Engaging Modern Healthcare Units: A Brief Overview.

In the current scenario, with a large amount of unstructured data, Health Informatics is gaining traction, allowing Healthcare Units to leverage and make meaningful insights for doctors and decision-makers with relevant information to scale operations and predict the future view of treatments via Information Systems Communication. Now, around the world, massive amounts of data are being collected and analyzed for better patient diagnosis and treatment, improving public health systems and assisting government agencies in designing and implementing public health policies, instilling confidence in future generations who want to use better public health systems. This article provides an overview of the HL7 FHIR Architecture, including the workflow state, linkages, and various informatics approaches used in healthcare units. The article discusses future trends and directions in Health Informatics for successful application to provide public health safety. With the advancement of technology, healthcare units face new issues that must be addressed with appropriate adoption policies and standards.

Computational intelligence in edge and cloud computing

An amalgamated computing prototype that assembles assets midway and gives users these funds via cyberspace are broadly termed as Cloud Computing (CC). The CC is far away from users geographically but is accessible from anywhere. Apart from this, EC (Edge Computing), usually dispenses funds to the web edge and enjoying increase of popularity in vast applications besides less suspension and more committed requirements. EC is providing assets in not a centralized manner, which reply to user’s requirement faster and better than the Cloud, but with grounded computing abilities. Together Cloud and Edge are tactful in funds, many large problems occur, for instance performing job scheduling, allocation of resources, and offloading of tasks, which notably is influencing performances in the entire system. CI(Computational intelligence), basically consists of sets of nature- inspired algorithmic approaches, currently exhibiting more capabilities while labeling such optimizing issues inside Cloud and Edge. This article is providing an overview of architecture of application software service system besides architecture in Cloud and Edge and current advances to address those aiding through Computational techniques.

Big Data Streaming Platforms: A Review

Yesterday’s “Big Data” is today’s “data.” As technology advances, new difficulties and new solutions emerge. In recent years, as a result of the development of Internet of Things (IoT) applications, the area of Data Mining has been confronted with the difficulty of analyzing and interpreting data streams in real time and at a high data throughput. This situation is known as the velocity element of big data. The rapid advancement of technology has come with an increased use of social media, computer networks, cloud computing, and the IoT. Experiments in the laboratory also generate a large quantity of data, which must be gathered, handled, and evaluated. This massive amount of data is referred to as “Big Data.” Analysts have seen an upsurge in data including valuable and worthless elements. In extracting usable information, data warehouses struggle to keep up with the rising volume of data collected. This article provides an overview of big data architecture and platforms, tools for data stream processing, and examples of implementations. Streaming computing is the focus of our project, which is building a data stream management system to deliver large-scale, cost-effective big data services. Owing to this study, the feasibility of large-scale data processing for distributed, real-time computing is improved even when the systems are overwhelmed.

Characteristics of Modern Landscape Architecture and Its Planning Methods

Landscape architecture is the planning, design, and construction of Landscape environments. Includes management and breeding. Landscape architects are unique With skills, they work to improve human and environmental health in all communities. They will strengthen parks, campuses, street views, alleys, plazas, apartments and communities Plan and design other projects. Landscape Architecture, Gardens, Yards, Campgrounds, Landscaping Development and ornamental planting of parks and other planned green outdoor areas. Nature Improve landscape gardening to create a Landscape setting for buildings, cities and towns Used. It is one of the decorative arts and is also used in architecture, town planning and gardening associated with. The following is a brief overview of Landscape architecture. For full treatment, gardening And see landscape design. Landscape architecture is the specific type of space Is the task of creating the outside. It can be a city square or even a whole city as a playground Let's design. It can design a pond to be ideal for frogs, turtles, fish and birds. Some types of landscape architecture are easy to see as a park. Other types are lawn or It can seem completely wild, like the mountains. Landscape Architecture Healthy Environments and For the design of communities and the health of the people, Dedicated to protecting security and well-being

Fabrication of Superamphiphobic Surfaces via Spray Coating; a Review

AbstractSuperamphiphobic coatings that simultaneously repel both water and oil and are applicable to a wide range of surfaces are needed for use in self‐cleaning, anti‐icing, and antimicrobial coatings. Spray coating is a method that can be used to apply such coatings to a wide range of surfaces in a scalable and high throughput manner. This review presents a comprehensive overview of the materials architecture, synthesis, applications, and figures‐of‐merit of superamphiphobic surfaces that are deposited using spray coating. The design requirements of superamphiphobic surfaces—surface roughness and wettability, re‐entrant topographic features, and chemical composition—are initially introduced. Based on the material, different synthesis techniques are then discussed with a focus on metal oxides and metal oxide composites, polymers, emerging, and green materials. The areas of application of superamphiphobic coatings are also presented. Finally, the main hurdles in using such coatings in real‐life applications are discussed in depth, and emerging technologies for overcoming these challenges are presented.