Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment by Priyankan Datta, Sreejesh Moolayadukkam, Dhrubajyoti Chowdhury, Adnan Rayes, Nan Sook Lee, Rakesh P. Sahu, Qifa Zhou, Ishwar K. Puri

“…Different sensitizers used in clinical and preclinical trials of various cancer treatments are listed, and the critical ultrasound parameters for SDT are reviewed. We also discuss approaches to improve the efficacies of these sonosensitizers, the role of the 3-dimensional spheroid in vitro investigations, ultrasound-controlled CAR-T cell and SDT-based multimodal therapy, and machine learning for sonosensitizer optimization, which could facilitate clinical translation of SDT.…”
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The separatrix operational space of next-step fusion experiments: From ASDEX Upgrade data to SPARC scenarios by T. Eich, T. Body, M. Faitsch, O. Grover, M.A. Miller, P. Manz, T. Looby, A.Q. Kuang, A. Redl, M. Reinke, A.J. Creely, D. Battaglia, J. Hillesheim, M. Wigram, J.W. Hughes

“…We then introduce a normalized SepOS framework and LH minimum scaling and show that normalized H-Mode boundaries across multiple machines are nearly identical, suggesting that the normalized SepOS can be used to translate results between different machines. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
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Estimation of soil organic carbon content and dynamics in Mediterranean climate regions considering long-term monthly climatic conditions by Lei Shen, Wei Zhang, Duanqiang Zhai, Shuo Han, Shuang Tian

“…Therefore, this study used random forest (RF) and light gradient boosting machine (LightGBM) algorithms to construct models. …”
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Big data analytics for strategic decision-making across the battery value chain: a review of status, trends, and future directions by André Hemmelder, Anne Sehnal, Simon Lux, Jens Leker

“…Research trends were examined using bibliometric data alongside framework parameters.63 relevant publications were identified and analysed. …”
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An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization by Shehu Lukman Ayinla, Azrina Abd Aziz, Micheal Drieberg, Misfa Susanto, Afidalina Tumian, Mazlaini Yahya

“…To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. …”
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Optimizing Federated Learning With Aggregation Strategies: A Comprehensive Survey by Naeem Khan, Shibli Nisar, Muhammad Asghar Khan, Yasar Abbas Ur Rehman, Fazal Noor, Gordana Barb

“…This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to collaboratively train models without sharing their local datasets. …”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The model integrates key mix parameters such as material proportions, curing conditions, and the chemical composition of FA/GGBS binders, making it chemistry-informed. …”
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On the black-box explainability of object detection models for safe and trustworthy industrial applications by Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser

“…In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. …”
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Designing and characterizing first Iranian study evaluating serum levels of lithium in patients for population pharmacokinetics (FIRELOLIPOP): baseline and first report by Mohammad Mahdi Khakshoor, Naser Pariz, Vahid Reza Askari, Mohammad Reza Fayyazi Bordbar, Sara Honari, Matin Shirazinia, Vafa Baradaran Rahimi, Mohammad Bagher Naghibi Sistani

“…Future research should incorporate pharmacokinetic modeling and machine learning to refine lithium therapy.…”
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A Two-Tiered Bidirectional Atrous Spatial Pyramid Pooling-Based Semantic Segmentation Model for Landslide Classification Using Remote Sensing Images by G. NaliniPriya, E. Laxmi Lydia, Reem Alshenaifi, Radhika Kavuri, Mohamad Khairi Ishak

“…Precisely identifying landslides over a vast region with intricate background entities is difficult. Machine Learning (ML) and Deep Learning (DL) have attained extraordinary performance in classifying images utilizing remotely sensed images from numerous platforms. …”
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Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

“…These arrays can precisely monitor physical parameters like pressure and strain in complex environments, making them highly beneficial for sectors such as smart wearables, robotic tactile sensing, health monitoring, and flexible electronics. …”
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Effect of residual stress on the mechanical strength in Al Beryl composites subjected to extrusion process by K.G. Sagar, P. SampthKumaran

“…The metallurgical analysis has been carried out to obtain the microstructure features as well as the grain size distribution and relating them to other parameters.The Aluminum Beryl composites in the extruded condition (Al-B-0 EX) have yielded very good results in respect of tensile strength and hardness, finer microstructure and smaller grain size over annealed composites. …”
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The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks by Nataliia Sheliemina

“…Rapid advancements in AI (artificial intelligence) technologies, including machine learning, natural language processing, and computer vision, have developed sophisticated tools capable of performing complex medical tasks. …”
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Deep learning for property prediction of natural fiber polymer composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…Aligning with this approach, Xue et al. (2023) compared DNN performance with genetic programming and minimax probability machine regression in predicting the lateral confinement coefficient for CFRP-wrapped RC columns, showing competitive predictive capability. …”
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WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks by Jose Perdomo, M. A. Gutierrez-Estevez, Chan Zhou, Jose F. Monserrat

“…Heterogeneous graphs are fed as samples into the HMPGNN model to simulate the wireless phenomena within WirelessNet’s model architecture. Model parameters associated to the same underlying wireless phenomena are shared across network nodes. …”
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XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles by Yakub Kayode Saheed, Joshua Ebere Chukwuere

“…It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. …”
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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Thus, this work developed an inference engine for robust water quality assessment and prediction using some statistical and intelligent discriminant functions. Results show that machine learning algorithms including the Logistic Regression, Decision Trees, Random Forest, XGBoost, and Neural Networks schemes reliably predicted water potability in the absence of two missing instrumentation parameters namely: pH and DO. …”
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Meta-Ensemble Learning for Heart Disease Prediction: A Stacking-Based Approach With Explainable AI by Mehwish Naz, Aqsa Khalid, Abdul Hameed, Rabia Taj, Waleed Mumtaz, Faiz Abdullah Alotaibi, Mrim M. Alnfiai

“…The work demonstrates a robust machine learning framework for classifying heart disease on three widely used benchmark datasets for heart disease such as Heart_2020_Cleaned, Heart Statlog Cleveland Hungary, and Cardio Train. …”
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Metric Scaling and Extrinsic Calibration of Monocular Neural Network-Derived 3D Point Clouds in Railway Applications by Daniel Thomanek, Clemens Gühmann

“…While multi-image approaches like Structure from Motion produce sparse point clouds, single-image depth estimation via machine learning promises denser results. However, many models estimate relative depth, and even those providing metric depth often struggle with unseen data due to unfamiliar camera parameters or domain-specific challenges. …”
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