Antagonism in Orthotospoviruses Is Reflected in Plant Small RNA Profile by Md Tariqul Islam, Kaixi Zhao, Nathan Johnson, Michael Axtell, Cristina Rosa

“…Using small RNA sequencing, we identified that the host plant contributes to this antagonism through differential small RNA processing, which appears to regulate viral replication and the success of infection. …”
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TempliPhi, ⌽29 DNA Polymerase Based Rolling Circle Amplification of Templates for DNA Sequencing by John R. Nelson, Yuyang Christine Cai, Theresa L. Giesler, Joseph W. Farchaus, Shanmuuga T. Sundaram, Maria Ortiz-Rivera, Lou P. Hosta, Peter L. Hewitt, J. Anthony Mamone, C. Palaniappan, Carl W. Fuller

“…This single subunit, proofreading DNA polymerase has excellent processivity and strand displacement properties for generation of multiple, tandem double-stranded copies of the circular DNA, generating as much as 107-fold amplification. …”
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A method for pre-determining edges in 3D point clouds using contour detectors in images by D. G. Filipov, S. L. Katsarska-Filipova, P. L. Raeva

“…For this purpose, based on the photogrammetrically captured images and predefined and automated processing, masks are created that isolate only the contours and edges. …”
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FPGA Programming Challenges When Estimating Power Spectral Density and Autocorrelation in Coherent Doppler Lidar Systems for Wind Sensing by Sameh Abdelazim, David Santoro, Fred Moshary

“…The second algorithm computes the autocorrelation of the received signals and accumulates the results over the same number of pulses. Both signal pre-processing algorithms are initially developed as logic designs and compiled using the Xilinx System Generator toolset to produce a hardware VLSI image. …”
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Advanced control scheme for harmonic mitigation and performance improvement in DC-AC microgrid with parallel voltage source inverter by Buddhadeva Sahoo, Subhransu Ranjan Samantaray, Mohammed M. Alhaider

“…To reduce computational complexity, the FS-MPC selectively employs 30 out of 64 switching vectors, ensuring faster processing without sacrificing performance. …”
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SUPERMAGOv2: Protein Function Prediction via Transformer Embeddings and Bitscore-Weighted Features by Gabriel Bianchin de Oliveira, Helio Pedrini, Zanoni Dias

“…Our machine learning-based model (SUPERMAGOv2) utilizes transformer-based backbones to extract features from multiple layers, which are then processed by six multilayer perceptrons that incorporate a novel bitscore-weighted input derived from DIAMOND alignments, and by an image classification model that converts the extracted feature vectors into images. …”
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Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms by S. I. Rudikov, V. Yu. Tsviatkou, A. P. Shkadarevich

“…The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.Methods. Image processing methods are used.Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. …”
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Open Radio Access Networks “O-RAN” SystemsThroughput improvement) by Russul Qasim, Ismail S. Baqer

“…In this work, we balance the spectral efficiency and the required power, which will reduce the signalling overheads and processing in high-density radio access networks. More specifically, a linear channel estimation (based on the MMSE technique) is employed to design the Conjugate Beamforming vectors. …”
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A Combined Approach to Safety and Security of IoT by Applying Fault Tree Analysis and Attack Trees with Minimal Cut Sets by Mohammad Rezaul Karim, Sohag Kabir, Ci Lei, Raluca Lefticaru, Mohammad Abdul Baset

“…Quantitative analysis (mean safety: 0.005, variance: 0.000006) confirmed the methodology’s effectiveness in capturing interdependencies across IoT layers (Perception, Network, Data Processing, Application). Results emphasize that combined safety-security analysis prevents isolated risk assessments, offering actionable insights for resilient IoT design. …”
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DeepContainer: A Deep Learning-based Framework for Real-time Anomaly Detection in Cloud-Native Container Environments by Ke Xiong, Zhonghao Wu,  Xuzhong Jia

“…The system architecture incorporates specialized components for real-time data collection, processing, and analysis, achieving a detection accuracy of 96.8% with an average response latency of 7.3ms. …”
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Short Text Semantic Similarity Measurement Approach Based on Semantic Network by Naamah Hussien Hameed, Adel M. Alimi, Ahmed T. Sadiq

“… Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. …”
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Enhancing Cyber Security: Comparing the Accuracy of the Bert Model with Other Common Deep Learning Models in Identifying Email Spam by Tao Xu

“…In this study, deep learning (DL) and natural language processing (NLP) methods were employed to develop a spam detection model. …”
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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. …”
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Rheological properties of superparamagnetic iron oxide nanoparticles by Javanbakht T., Laurent S., Stanicki D., Salzmann I.

“…These results suggest that these nanoparticles are promising candidates with appropriate properties for fluid processing applications and drug vectors in biomedical applications.…”
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EmoNet: Deep Attentional Recurrent CNN for X (Formerly Twitter) Emotion Classification by Md. Shakil Hossain, Md. Mithun Hossain, Md. Shakhawat Hossain, M. F. Mridha, Mejdl Safran, Sultan Alfarhood

“…EmoNet utilizes an AttentionEmbedder module to generate query, key, and value vectors, which are then processed through an attention layer to compute weighted representations. …”
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Research on the quantification and automatic classification method of Chinese cabbage plant type based on point cloud data and PointNet++ by Chongchong Yang, Chongchong Yang, Lei Sun, Lei Sun, Jun Zhang, Jun Zhang, Xiaofei Fan, Xiaofei Fan, Dongfang Zhang, Dongfang Zhang, Tianyi Ren, Tianyi Ren, Minggeng Liu, Minggeng Liu, Zhiming Zhang, Zhiming Zhang, Wei Ma, Wei Ma

“…This study has proposed a method for the rapid and accurate quantification and classification of Chinese cabbage plant types based on point-cloud data processing and the deep learning algorithm PointNet++. …”
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INNSE: Invertible neural network-based DNA image storage with self-correction encoding by Yanfen Zheng, Xiaokang Zhang, Lijun Sun, Ben Cao, Bin Wang, Qiang Zhang, Pan Zheng

“…A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. …”
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Alignment-free viral sequence classification at scale by Daniel J. van Zyl, Marcel Dunaiski, Houriiyah Tegally, Cheryl Baxter, Tulio de Oliveira, Joicymara S. Xavier, The INFORM Africa research study group

“…Our study highlights the practical advantages of AF techniques, including significantly faster processing compared to alignment-based methods and the ability to classify sequences using modest computational resources.…”
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Automated Tunnel Point Cloud Segmentation and Extraction Method by Zhe Wang, Zhenyi Zhu, Yong Wu, Qihao Hong, Donglai Jiang, Jinbo Fu, Sifa Xu

“…By using a sliding window, it effectively distinguishes between curved and straight sections of long tunnels while removing non-tunnel structural point clouds with normal vectors, thereby enhancing the lining boundary features and significantly improving the automation level of tunnel processing. …”
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M2Caps: learning multi-modal capsules of optical and SAR images for land cover classification by Haodi Zhang, Anzhu Yu, Kuiliang Gao, Xuanbei Lu, Xuefeng Cao, Wenyue Guo, Weiqi Lian

“…Subsequently, the cascaded features fusion module integrates various feature scales, concurrently processing deep multi modal features, shallow OPT features, and shallow SAR features at each layer. …”
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