Results 41 to 50 of about 86,374 (257)

Profound Leadership Strategies: Transcending the Leadership Crisis in the Age of Artificial Intelligence

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT This article examines the evolving role of organizational leadership amidst the rapid advancements in artificial intelligence (AI). It explores a broadly experienced and documented crisis in leadership, due in part to the disruptive nature of AI and emerging technology.
Rachel Wlodarsky, Davin Carr Chellman
wiley   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Graph reinforcement learning for power grids: A comprehensive survey

Energy and AI
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev, Josephine Thomas, Bernhard Sick, Christoph Scholz   +5 more
doaj   +1 more source

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

Sensors, 2020
With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are ...
Zheqi Yu, Adnan Zahid, Shuja Ansari, Hasan Abbas, Amir M. Abdulghani, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi   +7 more
doaj   +1 more source

Plasma EV Proteomics Identifies ECM Remodeling and Inflammatory Proteins LUM and C7 as Candidate Biomarkers in FSHD

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Facioscapulohumeral muscular dystrophy (FSHD) is one of the most debilitating and common muscular dystrophies. Despite its severity, no approved therapy exists for FSHD patients. However, several therapeutic candidates are currently under development, and some have recently entered clinical trials, marking the need for reliable ...
Mustafa Bilal Bayazit, Chiranth K. Nagaraj, Jackson S. Newell, Kim Truc Nguyen, Xilal Y. Rima, Jacob Doon‐Ralls, Eduardo Reátegui, Jeffrey M. Statland, Rabi Tawil, Kevin M. Flanigan, Scott Q. Harper, Nizar Y. Saad   +11 more
wiley   +1 more source

Reaction rebalancing: a novel approach to curating reaction databases

Journal of Cheminformatics
Purpose Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks.
Tieu-Long Phan, Klaus Weinbauer, Thomas Gärtner, Daniel Merkle, Jakob L. Andersen, Rolf Fagerberg, Peter F. Stadler   +6 more
doaj   +1 more source

Engagement Patterns with an AI Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, Accepted Article.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed‐methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation

Sensors, 2022
A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation
Avirup Roy, Hrishikesh Dutta, Henry Griffith, Subir Biswas   +3 more
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark

Journal of Big Data
Systolic blood pressure (SBP) and heart rate (HR) are critical for proactive clinical decision-making, particularly in intensive care environments. Early identification of abnormalities in these vital signs helps patients receive appropriate treatment ...
Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali   +5 more
doaj   +1 more source