Information Table Research Articles

NEW FORM OF SOFT SETS AND ITS PROPERTIES

Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. Many researchers have studied this theory and developed several models to solve decision-making and medical diagnostic problems, but most of these models deal only one set of parameters. This causes problems for users, especially with those who use questionnaires in their work and studies. Also Alkhazaleh and Salleh, also introduced the concept of soft-expert sets. This structure can be considered as a generalization of soft-sets in which experts and their opinions have been added to make decision analysis easier to handle. In our model, is more generalization of soft- set and soft-expert set, the collection of more specific information about object sets using mappings. This concept is more powerful for information tables, since collection of the information is very particular to define by mapping and also this model is approaches to rough set theory and information system.

A distributed load balancing architecture based on in‐band network telemetry

AbstractA data center (DC) is supposed to efficiently distribute the bandwidth of the network to provide high‐quality traffic transmission. However, the load imbalance issue can easily occur due to the complex topology and traffic features. Equal‐Cost Multi‐Path(ECMP) distributes traffic on different paths but doesn't consider network congestion. Although HULA solved some of ECMP's problems, it can easily congest the best path. RPS randomly distributes packets across multiple paths, potentially causing packet reordering in certain scenarios. This paper presents DHLB, a distributed hop‐by‐hop load balancing architecture based on in‐band network telemetry. With active In‐band network telemetry, DHLB gathers essential load information and systematically records it in a load information table. DHLB distributes traffic proportionally on different paths based on their load degree. We build a fat tree topology on mininet to verify the performance of our design. Experimental results indicate that DHLB outperforms other schemes regarding average Flow Completion Time (FCT). It also performs better on additional overhead than another probe‐based scheme.

Look Beyond Additivity and Extensivity of Entropy for Black Hole and Cosmological Horizons.

We present a comparative analysis of the plethora of nonextensive and/or nonadditive entropies which go beyond the standard Boltzmann-Gibbs formulation. After defining the basic notions of additivity, extensivity, and composability, we discuss the properties of these entropies and their mutual relations, if they exist. The results are presented in two informative tables that are of strong interest to the gravity and cosmology community in the context of the recently intensively explored horizon entropies for black hole and cosmological models. Gravitational systems admit long-range interactions, which usually lead to a break of the standard additivity rule for thermodynamic systems composed of subsystems in Boltzmann-Gibbs thermodynamics. The features of additivity, extensivity, and composability are listed systematically. A brief discussion on the validity of the notion of equilibrium temperature for nonextensive systems is also presented.

Experiment and analysis on seismic performance of a self-centering Y-eccentrically braced frames structure

Conventional eccentrically braced frames (EBFs) exhibit significant residual deformations following major earthquakes, necessitating costly repairs for the damaged structures. This study proposed a novel self-centering Y-eccentrically braced frames (SC-YEBFs) system to enhance the seismic resilience. A theoretical mechanical model which takes into account its components is formulated to predict the lateral load behavior of SC-YEBFs. The pseudo-static experiments were conducted on four specimens, and the resulting observations and analyses demonstrate that the shear links effectively function as ductile fuses, dissipating a significant portion of the input energy to safeguard the main frame from damage. Furthermore, the self-centering joint exhibits a hinge-like behavior with remarkable self-centering characteristics. The structural reparability of all specimens was found to be exceptional during major seismic events. By increasing the initial prestress and sectional area of steel strands, the self-centering performance can be further enhanced. The damaged shear links could be easily detached by loosening bolts, and the consideration of prestress loss is nonnegligible to ensure an exceptional self-centering performance. The proposed theoretical model predicted the mechanical properties of SC-YEBFs, including lateral stiffness, energy dissipation, and self-centering mechanism, through a comparison with experimental results. Moreover, the theoretical model was utilized to propose an equivalent simulation method for simulating self-centering joint and shear link using Connector function in ABAQUS software, while establishing a simplified frame finite element model for further analysis.

Front Matter (Title Page, Table of Content, General Information, and Editorial Team)

Front Matter (Title Page, Table of Content, General Information, and Editorial Team)

Mathematical modeling and experiments on the acoustic properties of a frame structure with a membrane

This study is a fundamental work conducted to theoretically estimate the acoustic properties of a simplified frame structure, that is, a simplified foam sound absorbing material, which is a porous material. The changes in the sound absorption characteristics caused by the presence of a membrane are also considered as part of the fundamental study of foam materials. In the mathematical model, the clearance composed by the frame structure with the membrane is divided into elements perpendicular to the direction of the soundwave incidence and approximated as a clearance bounded by two parallel planes. The characteristic impedance and the propagation constant are obtained for the approximated clearance and introduced into a one-dimensional transfer matrix. The transfer matrix method is used to calculate the sound absorption coefficient. The calculated theoretical values are compared with the experimental values. Sensitivity analysis is also performed for the changes in the parameters used in the theoretical analysis, such as the cylinder diameter and the pitch between the cylinders. The samples are fabricated using a three-dimensional printer. The sound absorption coefficient is measured using a two-microphone impedance measurement tube. The theoretical values based on the analysis tended to be close to the experimental ones. They showed a high prediction accuracy with respect to the peak frequency of the sound absorption curve. These values for the presence and increase of the membrane closely followed the experimental values when the percentage of the membrane present was varied.

Matrix-based approximation dynamic update approach to multi-granulation neighborhood rough sets for intuitionistic fuzzy ordered datasets

With the explosive growth and rapid changes in the use of data, information systems are constantly evolving. Timely dynamic updates have become imperative with real-time monitoring of data increasingly common. Effectively characterizing the approximation space in dynamic environments is of significant concern. This paper investigates a dynamic update mechanism for generalized multi-granulation neighborhood dominant rough sets, based on a matrix form, in an intuitionistic fuzzy ordered information table. We first define support and inclusion functions to construct the model of generalized multi-granulation neighborhood dominant rough sets. Additionally, we analyze the dynamic update process in which objects are added or removed in matrix form. Corresponding dynamic update algorithms are proposed based on generalized multi-granulation neighborhood dominant rough sets. Finally, to validate the effectiveness of the matrix-based dynamic approximation update algorithm, eight UCI datasets are used to perform experiments. The results verify that our matrix-based dynamic update algorithm is effective in approximating updates for dynamic intuitionistic fuzzy ordered information datasets.

PPB-MCTS: A novel distributed-memory parallel partial-backpropagation Monte Carlo tree search algorithm

Monte-Carlo Tree Search (MCTS) is an adaptive and heuristic tree-search algorithm designed to uncover sub-optimal actions at each decision-making point. This method progressively constructs a search tree by gathering samples throughout its execution. Predominantly applied within the realm of gaming, MCTS has exhibited exceptional achievements. Additionally, it has displayed promising outcomes when employed to solve NP-hard combinatorial optimization problems. MCTS has been adapted for distributed-memory parallel platforms. The primary challenges associated with distributed-memory parallel MCTS are the substantial communication overhead and the necessity to balance the computational load among various processes. In this work, we introduce a novel distributed-memory parallel MCTS algorithm with partial backpropagations, referred to as Parallel Partial-Backpropagation MCTS (PPB-MCTS). Our design approach aims to significantly reduce the communication overhead while maintaining, or even slightly improving, the performance in the context of combinatorial optimization problems. To address the communication overhead challenge, we propose a strategy involving transmitting an additional backpropagation message. This strategy avoids attaching an information table to the communication messages exchanged by the processes, thus reducing the communication overhead. Furthermore, this approach contributes to enhancing the decision-making accuracy during the selection phase. The load balancing issue is also effectively addressed by implementing a shared transposition table among the parallel processes. Furthermore, we introduce two primary methods for managing duplicate states within distributed-memory parallel MCTS, drawing upon techniques utilized in addressing duplicate states within sequential MCTS. Duplicate states can transform the conventional search tree into a Directed Acyclic Graph (DAG). To evaluate the performance of our proposed parallel algorithm, we conduct an extensive series of experiments on solving instances of the Job-Shop Scheduling Problem (JSSP) and the Weighted Set-Cover Problem (WSCP). These problems are recognized for their complexity and classified as NP-hard combinatorial optimization problems with considerable relevance within industrial applications. The experiments are performed on a cluster of computers with many cores. The empirical results highlight the enhanced scalability of our algorithm compared to that of the existing distributed-memory parallel MCTS algorithms. As the number of processes increases, our algorithm demonstrates increased rollout efficiency while maintaining an improved load balance across processes.

Multigranulation variable-scale fuzzy neighborhood measures and corresponding Choquet-like integrals for feature selection

Choquet-like integrals, a nonlinear fuzzy aggregation function, are diffusely applied in several real problems. However, the corresponding fuzzy measures in them are provided by human intervention and not by data-driven methods. As an effective knowledge representation tool, rough set models in different approximation spaces are established for data processing. Particularly, the multigranulation fuzzy β-covering approximation space has been concerned with only a single scale β∈(0,1]. However, different granulation structures should have different scales. Therefore, establishing a multigranulation approximation space with a variable scale by inducing fuzzy measures through data-driven methods and using corresponding Choquet-like integrals in practical problems is a significant challenge. To address this challenge, the notion of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented here, as well as multigranulation variable-scale fuzzy neighborhood measures in it. Furthermore, Choquet-like integrals with the presented measures are constructed to solve the issue of feature selection (i.e., attribute reduction) under the new approximation spaces. Firstly, the concept of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented, where different fuzzy β-coverings have different scales β∈Θ. Moreover, multigranulation variable-scale fuzzy neighborhood measures in it, as fuzzy measures, are presented. Subsequently, Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures are constructed. A novel attribute reduction method under Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures is proposed for any decision information table. Finally, the presented method is selected for solving problems of classification and diagnosis of rolling bearing faults. Several public data sets are used to demonstrate the effectiveness and feasibility of the presented methods mentioned above.

Comparison of Seismic and Wind Actions on Medium to High-Rise Buildings in Muscat, Oman

This study is a comparison of wind and seismic loads on medium and high-rise buildings in Muscat, Oman. It uses the proposed Omani Seismic Code and Eurocode EN1991 for seismic and wind calculations, respectively. Muscat falls under Zone-1 in the Omani seismic code and experience basic wind speed of 30 m/sec. The research investigates buildings with varying aspect ratios (1:1 and 1:2), heights (11, 15, and 19 stories), and structural layouts (frame only, core shear wall, and corner shear wall), using ETABS for structural analysis. The findings reveal that seismic actions are generally more significant than wind actions for buildings in Muscat. In frame-only structures, wind-induced base shear ranges from 16%-33% for 1:1 aspect ratio and 21%-43% in the x-direction and 10%-20% in the y-direction for 1:2 aspect ratio, when compared to seismic actions. This difference decreases with increasing building height. Incorporating shear walls notably reduces the maximum lateral displacement across all scenarios, with core-located walls being most effective, leading to a 49% reduction in lateral displacement. Shear walls also substantially mitigate first-story column shear forces and bending moments. The study concludes that seismic actions are more critical than wind actions in Muscat for simple moment-resisting frame systems. Additionally, using shear walls in these buildings is highly beneficial for controlling lateral displacements and reducing member forces.

Strength analysis of linear-elastic frame structures under pulse-periodic loads

Pulse-periodic loads on structures are one of the most dangerous for the their stress-strain state under dynamic load.Purpose: The aim of the work is to analyze the dynamic deformation of linear-elastic frame structures under pulse-periodic loads.Approach: It is found that a simple frame structure with additional concentrated masses affects the loading parameters (length, shape and pulse frequency). The analysis is given to physical characteristics of the deformed structure and its dynamic response under the pulse-periodic load.Research findings: Prerequisites are formulated for an unfavorable (from the position of VAT parameters) combination of the load and structural parameters.

Systematic Mapping of Circular Economy in Structural Engineering

Facing increasing sustainability demands, the construction industry is at a turning point where the implementation of circular economy (CE) strategies plays an essential role in driving the necessary transformation aimed at reducing the environmental impact. To facilitate this shift, structural engineering must effectively integrate circular principles into building design. With the exponential growth of research articles within this field, it is crucial to map the evolution of the research area. The objective of this study is to detail the trends with, challenges to, and research contributions, integration, and material applications of CE principles within structural engineering. Consequently, a systematic mapping of the CE within the field of structural engineering has been conducted in this study. Initially, the mapping process began with the identification of relevant keywords, followed by searches across four databases. Each resulting article was carefully screened against content criteria, culminating in 91 publications that were thoroughly evaluated. The publications were then categorized and analyzed based on attributes such as research type, circular design, materials, and applications. The results are presented through informative figures and tables. The analysis of the research indicates a predominant focus on technical solutions for structural systems, with demountable connections designed to facilitate the future reuse of materials representing more than half of the literature reviewed. A significant portion of the literature also addresses designing from reclaimed elements; these articles reflect a transformation in engineering approaches, incorporating computational design and innovative methodologies. The focus on steel as a structural material is prominent in the reviewed literature. However, there is an increasing focus on timber, which signals a definitive shift toward sustainable structural systems. Recurring challenges identified in the literature regarding the transition to a circular economy (CE) in the construction industry include the need for industry-wide adoption, precise standardization, the integration of digital tools, and the overcoming of related obstacles in policy and market acceptances. Furthermore, the literature demonstrates a significant research gap: the absence of a comprehensive digital framework enabling an effective digital circular structural design workflow.

Enhancing Traffic Routing Inside a Network through IoT Technology & Network Clustering by Selecting Smart Leader Nodes

. IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

BVT-IMA: Binary Vision Transformer with Information-Modified Attention

As a compression method that can significantly reduce the cost of calculations and memories, model binarization has been extensively studied in convolutional neural networks. However, the recently popular vision transformer models pose new challenges to such a technique, in which the binarized models suffer from serious performance drops. In this paper, an attention shifting is observed in the binary multi-head self-attention module, which can influence the information fusion between tokens and thus hurts the model performance. From the perspective of information theory, we find a correlation between attention scores and the information quantity, further indicating that a reason for such a phenomenon may be the loss of the information quantity induced by constant moduli of binarized tokens. Finally, we reveal the information quantity hidden in the attention maps of binary vision transformers and propose a simple approach to modify the attention values with look-up information tables so that improve the model performance. Extensive experiments on CIFAR-100/TinyImageNet/ImageNet-1k demonstrate the effectiveness of the proposed information-modified attention on binary vision transformers.

An Approach for Exoplanet Detection using Machine Learning By IJISRT

Traditionally, astronomer and astrophysicist teams were the only ones capable of identifying planets. They used techniques and equipment that are only available to persons with years of formal education and training. NASA's program for exploring exoplanets has provided cutting-edge satellites that can gather a wide range of information on celestial objects to help with studies on these items. The capacity to write and understand machine-learning models has made planet detection more accessible to those using satellite data. This study used a number of classification methods and datasets to determine the likelihood that an observation is an exoplanet. The best machine learning model for classifying items of interest in the Cumulative Kepler Object of Information table was determined to be a Random Forest Classifier. The cross-validated accuracy of the Random Forest classifier yielded a score of 98%. The likelihood of 968 candidate observations being exoplanets is greater than 95%. Ultimately, an Azure Container Instance web service and an application programming interface (API) on the Microsoft Azure cloud enabled the Random Forest classifier to be publicly accessible.

A trustworthy data collection scheme based on active spot-checking in UAV-Assisted WSNs

The UAV-WSN systems integrates the flexibility of UAVs with the communication and sensing capabilities of WSNs, making them important means of information monitoring, collection, and analysis in various fields that has received widespread attention. However, various malicious attacks pose threats to the security of UAV-WSN systems, resulting in serious vulnerabilities in trustworthy data collection. Therefore, in this article, we propose an Active Spot-Checking-based Trustworthy Data Collection (ASC-DC) scheme, which makes the following main contributions: (1) A novel UAV-assisted active and verifiable node trust evaluation framework is proposed. It includes an active trust acquisition mechanism based on spot-checked factors of nodes and a node trustworthy interaction mechanism based on Authoritative Information Tables (AIT). Through actively spot-check nodes using UAV, this framework can rapidly and accurately evaluate node trustworthiness while assisting sensor nodes in establishing trust relationships quickly. (2) A layered trust computation model is proposed, where the primary trust computation and node security supervision tasks are offloaded to more powerful UAV, thereby reducing information congestion and node energy consumption caused by trust evaluation in the WSN. (3) An improved Ant Colony Optimization (ACO) algorithm is proposed to efficiently acquire UAV’s spot-checking flight trajectories. It reduces the cost of spot-checking nodes using UAV and improves the efficiency of trust verification. Experimental results demonstrate that the proposed ASC-DC scheme can accurately evaluate node trustworthiness in an efficient way, ensuring reliable data collection in UAV-WSN systems.

Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks

The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective.

Mapping awareness-raising and capacity-building materials on developmental disabilities for non-specialists: a review of the academic and grey literature.

Most children with developmental disabilities (DD), such as intellectual disabilities and autism, live in low- and middle-income countries (LMICs), where services are usually limited. Various governmental, non-governmental and research organisations in LMICs have developed awareness-raising campaigns and training and education resources on DD in childhood relevant to LMICs. This study aimed to comprehensively search and review freely available materials in the academic and grey literature, aimed at awareness raising, training and education on DD among non-specialist professionals and community members in LMICs. We consulted 183 experts, conducted key-word searches in five academic databases, four grey-literature databases and seventeen customised Google search engines. Following initial screening, we manually searched relevant systematic reviewsand lists of resources and conducted forwards and backwards citation checks of included articles. We identified 7327 articles and resources after deduplication. We then used a rigorous multi-step screening process to select 78 training resources on DD relevant to LMICs, of which 43 aimed at informing and/or raising awareness DD, 16 highlighted specific strategies for staff in health settings and 19 in education settings. Our mapping analysis revealed that a wealth of materials is available for both global and local use, including comics, children's books, flyers, posters, fact sheets, blogs, videos, websites pages, social media channels, handbooks and self-education guides, and training programmes or sessions. Twelve resources were developed for cross-continental or global use in LMICs, 19 were developed for and/or used in Africa, 23 in Asia, 24 in Latin America. Most resources were developed within the context where they were intended to be used. Identified gaps included a limited range of resources on intellectual disabilities, manuals for actively delivering training to staff in education settings and resources targeted at eastern European LMICs: future intervention development and adaptation efforts should address such gaps, to ensure capacity building materials exist for a sufficient variety of DD, settings and geographical areas. Beyond identifying these gaps, the value of the review lies in the compilation of summary tables of information on all freely available resources found, to support their selection and use in wider contexts. Information on the resource content, country of original development and copyright is provided to facilitate resource sharing and uptake.

ShaFA, a New Microsoft Windows-Based Software for Food Intake Analysis

In food intake studies, converting eaten foods into calories and nutrients and other food components using books and tables of food ingredients is a very time-consuming and error-prone task. ShaFA is new Microsoft Windows-based software for food component derivation of individual and group food intake data. This software is developed using C sharp programming language. Microsoft Access has been used to put the information of 8790 food types and their 85 food components based on the USDA-SR28 in the database of software. The user is capable to search in the database for desired food by the name or special code of the food and select them for each person. Each person’s food intake data can be stored via allocating an exclusive ID. Finally, the user can get a report of the imported data in a new window, and also can get a Microsoft Excel export which can be imported into statistical software such as SPSS and STATA. Each research project data can be stored in a file with the unique extension (ShaFA) which can be opened and edited in any system that its operating system is Microsoft Windows, and has ShaFA software installed on it. This software can provide researchers with valuable information in a short time, especially nutritional epidemiology studies. It can also be used in food industry to extract the information needed to label and complete food information table for a variety of food industry products.

An Effective Video Transformer With Synchronized Spatiotemporal and Spatial Self-Attention for Action Recognition.

Convolutional neural networks (CNNs) have come to dominate vision-based deep neural network structures in both image and video models over the past decade. However, convolution-free vision Transformers (ViTs) have recently outperformed CNN-based models in image recognition. Despite this progress, building and designing video Transformers have not yet obtained the same attention in research as image-based Transformers. While there have been attempts to build video Transformers by adapting image-based Transformers for video understanding, these Transformers still lack efficiency due to the large gap between CNN-based models and Transformers regarding the number of parameters and the training settings. In this work, we propose three techniques to improve video understanding with video Transformers. First, to derive better spatiotemporal feature representation, we propose a new spatiotemporal attention scheme, termed synchronized spatiotemporal and spatial attention (SSTSA), which derives the spatiotemporal features with temporal and spatial multiheaded self-attention (MSA) modules. It also preserves the best spatial attention by another spatial self-attention module in parallel, thereby resulting in an effective Transformer encoder. Second, a motion spotlighting module is proposed to embed the short-term motion of the consecutive input frames to the regular RGB input, which is then processed with a single-stream video Transformer. Third, a simple intraclass frame interlacing method of the input clips is proposed that serves as an effective video augmentation method. Finally, our proposed techniques have been evaluated and validated with a set of extensive experiments in this study. Our video Transformer outperforms its previous counterparts on two well-known datasets, Kinetics400 and Something-Something-v2.