Biksegn Yirdaw | Cross-disciplinary Synthesis | Best Researcher Award

Biksegn Yirdaw | Cross-disciplinary Synthesis | Best Researcher Award

Assist Prof Dr Biksegn Yirdaw, London School of Hygiene & Tropical Medicine, United Kingdom

Dr. Biksegn Yirdaw (BSc, MSc, PhD) is an Assistant Professor in Public Mental Health at the London School of Hygiene and Tropical Medicine (LSHTM) 🌍. Since 2023, he has been with the UK Public Health Rapid Support Team (UK-PHRST) πŸ₯, contributing as a deployable staff member to address mental health needs during infectious disease outbreaks 🦠🧠. His research focuses on the intersection of disease outbreaks and mental health, specifically evaluating the integration of mental health and psychosocial support (MHPSS) into outbreak preparedness and response plans πŸ“Š.

Publication profile

orcid

Research focus

Biksegn Asrat Yirdaw’s research focus encompasses mental health issues, particularly anxiety, depression, and PTSD, within vulnerable populations. His work addresses these disorders in general health settings and among adolescents, internally displaced women, and individuals living with HIV/AIDS in sub-Saharan Africa. Yirdaw also explores psychological interventions, their effectiveness, and feasibility in low- and middle-income countries, highlighting the interconnectedness of mental health with factors like undernourishment and adherence to medical treatments. His studies emphasize the need for accessible mental health care and tailored therapeutic approaches in resource-limited settings. πŸŒπŸ§ πŸ’Š

Publication top notes

Management of generalized anxiety disorder and panic disorder in general health care settings: new WHO recommendations

Effectiveness of school-based psychological interventions for the treatment of depression, anxiety and post-traumatic stress disorder among adolescents in sub-Saharan Africa: A systematic review of randomized controlled trials

Gender-based violence and its associated factors among internally displaced women in Northwest Ethiopia: a cross-sectional study

Prevalence of undernourishment and associated factors among adults with major depressive disorder at two public hospitals in Northwest Ethiopia: a cross-sectional study

Acceptability and feasibility of peer-administered group interpersonal therapy for depression for people living with HIV/AIDSβ€”a pilot study in Northwest Ethiopia

Level of anxiety symptoms and its associated factors among nurses working in emergency and intensive care unit at public hospitals in Addis Ababa, Ethiopia

Major depressive disorder and its association with adherence to antiretroviral therapy and quality of life: cross-sectional survey of people living with HIV/AIDS in Northwest Ethiopia

Adaptation of the WHO group interpersonal therapy for people living with HIV/AIDS in Northwest Ethiopia: A qualitative study

Effectiveness of psychological treatments for depressive symptoms among people living with HIV/AIDS in low- and middle-income countries: A systematic review and meta-analysis

Multimorbidity of chronic non-communicable diseases and its models of care in low- and middle-income countries: a scoping review protocol

 

 

 

Chuanxi Liu | Cross-disciplinary Synthesis | Cross-disciplinary Excellence Award

Chuanxi Liu | Cross-disciplinary Synthesis | Cross-disciplinary Excellence Award

Mr Chuanxi Liu, State Nuclear Power Information Technology Company.LTD, China

Chuanxi Liu is a prolific researcher with a strong background in astrophysics and artificial intelligence. He has co-authored pivotal papers on Gamma-Ray Burst (GRB) X-Ray flare properties and statistical studies of Swift X-Ray Flash and X-Ray Rich GRBs. Liu’s contributions to electric power technology include developing neural network algorithms for waveform data processing and using image processing techniques for fault detection. He has also innovated in wind power inspection with advancements in oil leakage detection and crack recognition. His notable achievements include a patent on traveling wave ranging and success in the Shandong Provincial AI Competition. πŸ“‘πŸ›°οΈπŸ’‘πŸ’»πŸ”

Publication profile

scopus

Education

I earned my BSc in Applied Physics from Shandong Jianzhu University, studying from September 2011 to June 2015. During my time there, I took a variety of courses including Thermodynamics, Mechanics, Optics, Electromagnetism, and Quantum Mechanics πŸ“š. My coursework also covered Higher Mathematics, Linear Algebra, Statistics and Probability Theory, Theoretical Mechanics, Machining Drawing, and Fundamentals of Digital and Analog Circuit Technology πŸ“. Following this, I pursued an MSc in Astrophysics at the University of Chinese Academy of Sciences from September 2015 to June 2019. My graduate studies included courses such as Image Processing, Semiconductor Device Physics, and Astronomical Data Processing 🌌. I also gained expertise in Numerical Simulation Methods for Magnetohydrodynamics and used Linux systems and the Astronomy Software Package IDL for my research πŸ–₯️.

Experience

From September 2015 to June 2019, I pursued a Master of Science at Yunnan Observatory, where I conducted extensive research on gamma-ray bursts. My work involved analyzing their spectra and light to validate astrophysical models. During this period, I co-authored several papers, including “GRB X-Ray Flare Properties among Different GRB Subclasses” published in the Astrophysical Journal in 2019 πŸ“š, and “Statistical Study of the Swift X-Ray Flash and X-Ray Rich Gamma-Ray Bursts” in 2018 🌟. Additionally, I contributed to the “Research Progress of GRB X-Ray Flare” in Progress in Astronomy in 2020 🌌.

Research focus

Liu, C., involved in multiple research areas, appears to have a diverse focus. In the field of object detection, Liu contributes to enhancing detection techniques through the development of the TBFF-DAC model, which leverages deformable attention and convolution for improved feature fusion πŸ€–. Additionally, Liu is engaged in electrical engineering, specifically in fault location methods for railway systems, utilizing non-contact measurements πŸš„. In the realm of astrophysics, Liu explores the properties of gamma-ray bursts and X-ray flares, analyzing their characteristics across different subclasses 🌌. This multidisciplinary approach underscores Liu’s expertise in both technological advancements and astrophysical phenomena.

Publication top notes

TBFF-DAC: Two-branch feature fusion based on deformable attention and convolution for object detection

Research on Traveling Wave Fault Location Method of Railway Automatic Blocking/Power Continuous Line Based on Noncontact Measurement

GRB X-Ray Flare Properties among Different GRB Subclasses

Statistical Study of the Swift X-Ray Flash and X-Ray Rich Gamma-Ray Bursts

Neda Fatima | Cross-disciplinary Synthesis | Women Researcher Award

Neda Fatima | Cross-disciplinary Synthesis | Women Researcher Award

Dr Neda Fatima, Manav Rachna International Institute of Research and Studies, India

🌟 Dr. Neda Fatima, an accomplished Knowledge Engineer, aspires to innovate SMART devices in CSE and IT via IoT. With 6 years’ experience as an Assistant Professor, she’s adorned with accolades like NET and GATE qualifications, PhD, and numerous research papers. Adept in AI, ML, Data Science, and IoT, she’s mentored in hackathons and workshops, showcasing expertise in academia and industry. Passionate about education, she’s won awards, led cultural events, and demonstrated leadership as a Placement Coordinator. With a Ph.D. from JMI, she’s a dynamic professional excelling in both academics and extracurriculars. πŸš€

Publication profile

google scholar

Education

Dr Neda Fatima, a distinguished scholar, proudly holds a Ph.D. from Jamia Millia Islamia (JMI), an achievement recognized and awarded for its excellence. Their academic journey showcases a remarkable trajectory, marked by stellar accomplishments. With a Master’s in Technology from JMI, attained in 2019 with a remarkable CGPA of 9.52, and a Bachelor’s in Technology from the same esteemed institution in 2015, where they graduated with a notable CPI of 9.13. Their linguistic prowess is equally impressive, having completed an Advanced Diploma in Spanish, considered equivalent to a Spanish degree, from JMI in 2015, where they excelled as the course topper. Additionally, they showcased their linguistic flair by topping courses in Portuguese as well. Their academic brilliance traces back to their school days, where they consistently shone. In 2010, they secured a first division in their XII CBSE examinations from Delhi Public School, R.K. Puram, with a focus on Physics, Chemistry, Mathematics, Biology, and English. Even earlier, in 2008, they emerged as the school topper in X CBSE examinations from Father Agnel School, boasting an impressive aggregate of 96%. πŸ†πŸŽ“βœ¨

Experience

With a diverse portfolio spanning from faculty development programs πŸ“š to hackathons πŸ’», internships 🀝, and workshops πŸ› οΈ, my journey epitomizes a holistic engagement with cutting-edge technologies. From mentoring teams in cybersecurity and AI/ML hackathons to participating in national-level military hackathons πŸŽ–οΈ, my dedication to innovation is evident. Moreover, I’ve contributed as a resource person in research colloquia, showcasing expertise in integrating IoT with blockchain. With hands-on experience in diverse programming languages and technologies πŸ–₯️, coupled with a strong conceptual understanding, I’m poised to continue making impactful strides in the realms of AI, IoT, cybersecurity, and beyond.

Research focus

N Fatima’s research spans across various domains, primarily focusing on healthcare and environmental monitoring 🌿πŸ₯. Her work includes designing IoT-based systems for disease prediction in smart greenhouses and human health, such as dermatological disease prediction and stress detection systems. Additionally, she contributes to border security and vehicle recognition technologies. Fatima’s recent endeavors involve utilizing deep learning techniques for disease prediction, including SARS-CoV-2 variant detection and mortality prediction. Through her research, she aims to leverage machine learning and IoT technologies to address pressing challenges in healthcare, agriculture, and security sectors, showcasing a versatile and impactful research portfolio.

Publication top notes

IoT-based smart greenhouse with disease prediction using deep learning

Neural Network based Smart Weed Detection System

IoT-based disease prediction using machine learning

Chest X-ray and CT scan classification using ensemble learning through transfer learning

Iot based border security system using machine learning

Comparative analysis of propagation path loss models in lte networks

Comparative Performance Analysis of Digital Modulation Schemes with Digital Audio Transmission through AWGN Channel

Smart air pollution monitoring system with smog prediction model using machine learning

Design of driver alcohol detection system with automatic engine locking

SARS-CoV-2 virus variant detection and mortality prediction through symptom analysis using machine learning

Dermatological disease prediction and diagnosis system using deep learning

Design of Smart Heart Rate Monitoring and Stress Detection System with Cloud Data Storage and Privacy

COVID-19 Vaccination Progress in India and its Neighbors

Vehicle Number Plate Recognition via MATLAB

Detection and Segmentation of Brain Tumor using MRI images