Modal Perturbation Method for the Dynamic Characteristics of Timoshenko Beams by Menglin Lou, Qiuhua Duan, Genda Chen

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Flexural Design and Analysis of Composite Beams with Inverted-T Steel Girder with Ultrahigh Performance Concrete Slab by Wonchang Choi, Youngcheol Choi, Sung-Won Yoo

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Secure IoT data dissemination with blockchain and transfer learning techniques by Pooja Anand, Yashwant Singh, Harvinder Singh

“…However, most existing systems for storing and disseminating IoT data streams lack reliability, security, and transparency, primarily due to centralized architectures that create single points of failure. To address these limitations, this research introduces TraVel, a blockchain and transfer learning-based framework for secure IoT data management. …”
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E Belém? A questão da apropriação do patrimônio numa freguesia de Lisboa. by Rafael H. Teixeira-da-Silva

“…Endowed with an important architectural ensemble entitled by UNESCO as World Cultural Heritage in 1983, the area comprises two significant monuments, the Monastery of Hieronymites and the Tower of Belém. …”
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Amplitude and phase errors calibration method in satellite-borne dynamic power allocation network by Qi TANG, Jun-min JIANG, Meng XIA

“…For the mobile communication satellite each beam communication traffic distribution was not balanced,leading to differences in transmission power of downlink beam.The implementation architectures of dynamic power allocation network was given,which could allocate the transmitting beam power automatically according to the traffic demand,at the same time ensured the high efficiency of power amplifier.Because the performance of dynamic power allocation network was severely impaired by the mutual coupling among signals when amplitude/phase mismatch was existed,which led to a sharp drop in beam performance.A calibration method for the amplitude and phase errors was proposed.The decoupling of calibration signal was realized by using the cross-correlation orthogonal characteristic of the orthogonal zero-correlation zone (ZCZ) sequences in the zero correlation interval,and communication was free from calibration by reducing the transmit power for the excellent self-correlation characteristic of the ZCZ sequences.Performance simulation and far field test show that under the condition that the calibration signal power is less than the normal signal 10 dB,the amplitude error is less than 0.1 dB,the phase error is less than 0.3°,and the gain loss within the main lobe is less than 0.2 dB after calibration.…”
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Causality-driven feature selection and domain adaptation for enhancing chemical foundation models in downstream tasks by Eduardo Soares, Victor Yukio Shirasuna, Emilio Vital Brazil, Karen Fiorella Aquino Gutierrez, Renato Cerqueira, Dmitry Zubarev, Kristin Schmidt, Daniel P Sanders

“…We evaluate our approach on various foundation model architectures and datasets, demonstrating performance improvements, which showcases the robustness and the agnostic nature of our approach.…”
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‘Finding the play’ – exploring with occupational therapists practice possibilities in the context of Irish schoolyards by Michelle Bergin, Bryan Boyle, Margareta Lilja, Maria Prellwitz

“…Critical contextualised research is advocated to inform practice possibilities.Aims/Objectives This inquiry aimed to explore with occupational therapists their existing practices in Irish schoolyards to generate practice possibilities concerned with play, as an issue of occupational justice.Materials and Methods Using the theory of practice architectures, six occupational therapists from diverse sites of practice participated in the first phase of a critical action research process using dialogical focus group and occupational mapping methods.Results Three themes were generated (1) Existing practices as situated (2) (Re)mattering play and practices as occupations and (3) Practice possibilities – ‘Finding the play’ between responsiveness and responsibilities. …”
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Supervised and Self-Supervised Learning for Assembly Line Action Recognition by Christopher Indris, Fady Ibrahim, Hatem Ibrahem, Götz Bramesfeld, Jie Huo, Hafiz Mughees Ahmad, Syed Khizer Hayat, Guanghui Wang

“…The comparative study was extended to self-supervised learning via a modified SPOT model, which achieved a mAP@IoU=0.1:0.7 of 65% with just 10% of the data labeled using extractor architectures from the fully-supervised portion. Milestones include high scores for both fully and semi-supervised learning on this dataset and improved SPOT performance on ANet1.3. …”
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Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes by Ehsan Homayouny, Rasoul Mahdavifar Khayati, Seyed Massood Nabavi, Vania Karami

“…The overall accuracy of the classification for acute, chronic, and T2 lesion classifications was 96.1%, 90.5%, and 92.9%, respectively. The proposed architectures reached high sensitivity in discrimination between MS lesion subtypes when 3T perfusion MRIs were used.…”
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Pole-to-Pole Short-Circuit Categorization for Protection Strategies in Primary Shipboard DC Systems by Alejandro Latorre, Thiago Batista Soeiro, Xinqian Fan, Rinze Geertsma, Marjan Popov, Henk Polinder

“…This article focuses on dc systems onboard ships, for which diverse applications require different power levels, architectures, and protection strategies. Existing protection frameworks and regulations are often inadequate or outdated for the field, leading to certification issues and insufficient fault analysis. …”
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Active Hard Sample Learning for Violation Action Recognition in Power Grid Operation by Lingwen Meng, Di He, Guobang Ban, Guanghui Xi, Anjun Li, Xinshan Zhu

“…We utilize ResNet50 and ViT architectures with Faster-RCNN for detection and recognition, developed using PyTorch 2.0. …”
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Capter les richesses du patrimoine mondial dans une ville sainte de l’islam : la problématique de la mise en tourisme à Djenné au Mali by Diama Cissouma Togola, Soufian Al Karjousli

“…Depuis, les programmes de développement proposés ont essentiellement visé à préserver le patrimoine architectural de la mosquée et des maisons traditionnelles. …”
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Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection by William Villegas-Ch, Jaime Govea, Rommel Gutierrez, Aracely Mera-Navarrete

“…Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. …”
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Ultraviolet Laser Activation of Phosphorus‐Doped Polysilicon Layers for Crystalline Silicon Solar Cells by Yijun Wang, Di Yan, Jesus Ibarra Michel, Sanje Mahasivam, Vipul Bansal, Robert Delaney, Jiali Wang, Thien Truong, Peiting Zheng, Jie Yang, Xinyu Zhang, James Bullock

“…In contrast to furnace‐based activation, lasers can be used to achieve highly localized doping with controlled dopant concentrations, useful in advanced architectures such as the interdigitated back contact (IBC) solar cell. …”
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Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review by S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

“…It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. …”
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A Review on Software-Defined Networking for Internet of Things Inclusive of Distributed Computing, Blockchain, and Mobile Network Technology: Basics, Trends, Challenges, and Future... by Shakila Shafiq, Md. Sazzadur Rahman, Shamim Ahmed Shaon, Imtiaz Mahmud, A. S. M. Sanwar Hosen

“…Internet of things (IoT) and software-defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. …”
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Solar photovoltaic panel cells defects classification using deep learning ensemble methods by H. Tella, A. Hussein, S. Rehman, B. Liu, A. Balghonaim, M. Mohandes

“…This study emphasizes the efficacy of integrating various deep learning architectures to augment defect detection precision in photovoltaic systems, enhancing operational reliability and enabling prompt maintenance under challenging environmental conditions.…”
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AI-Assisted Forecasting of a Mitigated Multiple Steam Generator Tube Rupture Scenario in a Typical Nuclear Power Plant by Sonia Spisak, Aya Diab

“…These simulations generated a comprehensive dataset, which served as the foundation for training three ML architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional LSTM (CNN+LSTM). …”
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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…Employed techniques encompass Logistic Regression (LR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), in conjunction with deep learning architectures such as Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN). …”
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Real-time event detection using recurrent neural network in social sensors by Van Quan Nguyen, Tien Nguyen Anh, Hyung-Jeong Yang

“…First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. …”
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