Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach.

Effective construction waste (CW) management, mainly concrete, brick, and steel, is a critical challenge due to its significant environmental and economic impacts. This study addresses this challenge by proposing multiple linear regression models to predict waste generation in residential buildings within the Egyptian construction industry, considering the influence of factors such as building design and site management features. Using data from 25 case studies, the models demonstrated high predictive accuracy, with adjusted R² values of 0.

Beyond burnout: A comprehensive investigation of burnout, resilience, and career continuity among Palestinian lawyers in a complex socio-political environment.

This article investigates burnout among lawyers and proposes systemic changes to reduce pressure and stress in the legal profession while enhancing resilience among lawyers. The article focuses on factors influencing career continuity among Palestinian lawyers within a socio-politically complex environment. It discusses elements contributing to resilience, including a positive mindset, a strong support system, training, and social support.

Testing organ-specific responses to therapies in tissues differentiated from Cystic Fibrosis patient derived iPSCs.

Cystic Fibrosis (CF) is a life-shortening disease that is caused by mutations in the CFTR gene, a gene that is expressed in multiple organs. There are several primary tissue models of CF disease, including nasal epithelial cultures and rectal organoids, that are effective in reporting the potential efficacy of mutation-targeted therapies called CFTR modulators. However, there is the well-documented variation in tissue dependent, therapeutic response amongst CF patients, even those with the same CF-causing mutation.

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.

An Evaluation of Whether Routine QTc Interval Screening Is Necessary Prior to Starting ADHD Medications: Experience from a Large Retrospective Study.

Background/objectives: Routine screening electrocardiograms (ECGs) prior to starting medications for attention-deficit/hyperactivity disorder (ADHD) remain controversial. This real-world study assessed corrected QT (QTc) interval data from pediatric patients who had a baseline ECG performed prior to initiating treatment with ADHD medications and ≥6 months of clinical follow-up.

Methods: A retrospective chart review of children aged 2-18 years diagnosed with ADHD with/without autism spectrum disorder (ASD) at child neurology clinics in Jordan (June 2019 and June 2021) was performed, and children were prescribed with ADHD medications to manage symptoms.