Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health

Fear of missing out(FoMO)is a unique term introduced in 2004 to describe a phenomenon observed on social networking sites.FoMO includes two processes;firstly,perception of missing out,followed up with a compulsive behavior to maintain these social connections.We are interested in understanding the complex construct of FoMO and its relations to the need to belong and form stable interpersonal relationships.It is associated with a range of negative life experiences and feelings,due to it being considered a problematic attachment to social media.We have provided a general review of the literature and have summarized the findings in relation to mental health,social functioning,sleep,academic performance and productivity,neuro-developmental disorders,and physical well-being.We have also discussed the treatment options available for FoMo based on cognitive behavior therapy.It imperative that new findings on FoMO are communicated to the clinical community as it has diagnostic implications and could be a confounding variable in those who do not respond to treatment as usual.

Optimizing sampling frequency of surface and downhole measurements for efficient stick-slip vibration detection

Drilling vibrations significantly impact drilling operations with high costs due to early downhole equipment failure and loss of productive time.Stick-slip vibrations,a severe form of torsional vibrations,is known to be present up to 50%of total drilling time,making it a topic of immense concern and research.An ongoing discussion in the industry is regarding the reliability of surface measurements for early detection of severe downhole bit sticking.Moreover,most surface measurements are sampled at lower frequency rates closer to 1 Hz.Recently,the implementation of advanced data acquisition modules in downhole subs has greatly improved our understanding of drilling vibrations through high resolution data,sampled up to 10 kHz.However,with a wide range of sampling frequency to choose from different available tools,a critical question remains unanswered.What is an optimal and adequate sampling frequency for early detection of downhole vibrations using both surface and downhole measurements?The paper addresses the question with a focus on stick-slip vibrations through an experimental investigation.Stick slip tests are repeated for different sampling frequencies of surface and downhole measurements and the stick slip index for each case is calculated.The stick-slip index varies for different sampling frequency even though the vibration tests remain completely identical.It was inferred that sampling frequency of measurements greatly impact the detection of downhole vibrations.Even though stick-slip vibrations are characteristically low frequency vibrations(2Hz),a minimum of 10Hz sampling frequency is recommended for detection of stick-slip vibrations.Moreover,all characteristics of stick-slip vibrations including bit sticking,bit RPM peaks and negative bit RPMs are clearly observed at a minimum of 100Hz sampling rate.

A review of torsional vibration mitigation techniques using active control and machine learning strategies

Drilling is one of the most challenging and expensive processes in hydrocarbon extraction and geothermal well development.Dysfunctions faced during drilling can increase the non-productive time(NPT)greatly,resulting in inflating the drilling cost and also pose a safety concern.One of the main problems faced during drilling that limits the life of drilling equipment and tools and decreases the overall productivity of the system is drilling vibrations.These vibrations can be categorized into three modes:axial,lateral,and torsional.Stick-slip vibrations are a type of torsional vibration in which the bottom hole assembly(BHA)periodically stops to rotate followed by a spike in the bottom hole RPM.This paper provides a comprehensive review of techniques used to control and mitigate torsional vibration with an emphasis on stick-slip.A brief introduction to drillstring and friction modeling is presented followed by a concise summary of passive control techniques to control stick-slip.Then the focus is shifted to an up-to-date review of active control and machine learning for stick-slip control and mitigation.The paper ultimately highlights the importance of adapting novel control and mitigation concepts to improve stick slip detection and improve the overall drilling process.A unique solution is insufficient to control a complex process such as drilling,but integration of various techniques has been found promising.