Available Online: 26 Aug 2026
AI-Guided Self-Driving Laboratories for Advanced Materials Discovery
Volume 3
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Available Online: 26 Aug 2026
Volume 3
Bernard Mallia
bernard.mallia@equinoxadvisory.com
Rossana Caputo
president@miict.org
Available Online: 17 Aug 2026
Volume 3
Idowu Olugbenga Adewumi
adexio2010@gmail.com
Wumi Ajayi
ajayiw@babcock.edu.ng
Oluwafisayo Babatope Ayoade
22053430@student.westernsydney.edu.au
Victoria Bolanle Oyekunle
bola.oyekunle@lcu.edu.ng
Akintayo Ayoade
akintayo.a@lcu.edu.ng
Azeez Ajani Waheed
waheed.azeez@lcu.edu.ng
Available Online: 10 Aug 2026
Volume 3
Manimegalai Ramalingam
mmegalai217@gmail.com
Vijayalakshmi P. Soundararajan
vijips2605@gmail.com
Priyadharshini Aruchamy
phdpriyadharshini@gmail.com
High speed wireless communication systems support the data-intensive requirements of contemporary 5G networks and emerging 6G technologies. However, maintaining high throughput, low latency, and reliable connectivity in rapidly varying wireless channels remains a significant engineering challenge. This study presents a machine-learning-driven framework that integrates a deep reinforcement learning (DRL) scheduler with a hybrid CNN-LSTM channel predictor to jointly optimize radio resource allocation, modulation order, transmit power, and bandwidth assignment in..
Available Online: 04 Aug 2026
Volume 3
Miguel Angel Vargas Cruz
miguelangel@grupoalianzaempresarial.com
Published: 27 Jul 2026
Volume 3
Gabriel Silva-Atencio
gsilvaa468@ulacit.ed.cr
Published: 24 Jul 2026
Volume 3
Gabriel Silva-Atencio
gsilvaa468@ulacit.ed.cr
Published: 15 Jul 2026
Volume 3
Gabriel Silva-Atencio
gsilvaa468@ulacit.ed.cr
Published: 11 May 2026
Volume 3
Linus Tabari
ltabari1@st.knust.edu.gh
Kate Takyi
takyikate@knust.edu.gh
Rose-Mary Owusuaa Mensah Gyening
rmo.mensah@knust.edu.gh
Published: 12 May 2026
Volume 3
Rahibu Abdalla Abassi
r.abassi@suza.ac.tz
Rocky Rajabu Akarro
akarror@udsm.ac.tz
Missing data are a common occurrence in research and, if not appropriately addressed prior to analysis, may compromise the validity of study findings. This article evaluates the effectiveness of various imputation techniques as formal approaches for handling missing covariate data. Root Mean Squared Error (RMSE) was computed for each imputation method under Missing Completely at Random (MCAR) and Missing at Random (MAR) mechanisms to identify the technique that yielded the..
Published: 02 Apr 2026
Volume 3
Mircea Ţălu
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