“You can't shoot another bullet until you've reloaded the gun”: Coaches' perceptions, practices and experiences of deloading in strength and physique sports

Deloading refers to a purposeful reduction in training demand with the intention of enhancing preparedness for successive training cycles. Whilst deloading is a common training practice in strength and physique sports, little is known about how the necessary reduction in training demand should be accomplished. Therefore, the purpose of this research was to determine current deloading practices in competitive strength and physique sports. Eighteen strength and physique coaches from a range of sports (weightlifting, powerlifting, and bodybuilding) participated in semi-structured interviews to discuss their experiences of deloading. The mean duration of coaching experience at ≥ national standard was 10.9 (SD = 3.9) years. Qualitative content analysis identified Three categories: definitions, rationale, and application. Participants conceptualised deloading as a periodic, intentional cycle of reduced training demand designed to facilitate fatigue management, improve recovery, and assist in overall training progression and readiness. There was no single method of deloading; instead, a reduction in training volume (achieved through a reduction in repetitions per set and number of sets per training session) and intensity of effort (increased proximity to failure and/or reduction in relative load) were the most adapted training variables, along with alterations in exercise selection and configuration. Deloading was typically prescribed for a duration of 5 to 7 days and programmed every 4 to 6 weeks, although periodicity was highly variable. Additional findings highlight the underrepresentation of deloading in the published literature, including a lack of a clear operational definition

A review on the application of machine learning for combustion in power generation applications

Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost