This paper focuses on potential interaction between the mortgage underwriting and pricing decisions for fair lending analysis of mortgage pricing. We argue that the loan approval or denial decision determines the loan origination population and therefore the underwriting policies might affect the fair lending assessment of the subsequent pricing decisions. The paper also finds that sample selec...

background: manual load lifting is the most common and stressful activity that imposes high biomechanical pressures on the body, particularly on the back. diverse studies have shown that load lifting can cause work-related musculoskeletal disorders among workers. this study was conducted to assess manual lifting activity using niosh equation and wisha index and compare the results of the two me...

BACKGROUND AND PURPOSE Flow-sensitive alternating inversion recovery (FAIR) MR imaging is a technique for depicting cerebral perfusion without contrast enhancement. Our purpose was to determine whether quantification at FAIR imaging can be used to assess regional cerebral blood flow (rCBF) in a manner similar to [iodine 123]-iodoamphetamin ((123)I-IMP) single photon emission CT (SPECT). METHO...

All societies face the need to make judgments about what interventions (both public health and personal medical) to provide to their populations under reasonable resource constraints. Their decisions should be informed by good evidence and arguments from health technology assessment (HTA). But if HTA restricts itself to evaluations of safety, efficacy, and cost-effectiveness, it risks being vie...

Existing so-called conflict-based models of executive control aim to explain how an agent, without constantly controlling its own processing (what is both cognitively costly and inefficient), can know when to apply strong control, but when to withdraw it. These models predict that the strength of control is adjusted proportionally to the level of conflict among competing stimuli/response tenden...

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has on centralized learning, leaving decentralized methods underexplored. Federated Learning is a form of where clients train local models with server aggregating them to obtain shared global model. Data heterogeneity amongst common characteristic Learning, which may induce or exacerbate discrim...