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Comparing this at service level, the RN/RM (99.4 per cent) and Army (97.6 per cent) both remained fairly stable, but RAF (99.2 per cent) had an increase of 2.1 percentage points. All Officers in the RN/RM and RAF have returned to work after maternity leave. In the Army 98.8 per cent returned. Figure 19: Sexual Orientation declaration rate across UK Regular Forces and Future Reserves 2020 split by service. Future Reserves 2020 The knee bone anatomy and injury mechanism. (a) Structure of knee ACL injury. (b) Osteoarthritis due to joint space reduction mechanism.

As at 1 April 2020, 25.0 per cent of the UK Regular Forces and 11.6 per cent of the Future Reserves 2020 were under 25 years old. In the last year, this has increased for the UK Regulars by 1.2 percentage points, and decreased by 0.3 percentage points in the Future Reserves 2020. Of the Officers, 7.4 per cent of the UK Regular Forces and 5.0 per cent of the Future Reserves 2020 were under 25 years old. The Kalman filter is widely applied in the state estimation of target motion [ 23– 25]. Due to noise during the measurement of target motion, Kalman filter can effectively remove noise by using the motion information of the target and obtain the optimal estimation of the target position. Of the overall female personnel returning to work after maternity leave in 2015, nearly 12 per cent returned for a year or less before leaving. This proportion has remained broadly flat across the succeeding years to 2016-18. Equivalent analysis cannot be undertaken for personnel that took maternity in 2018 as enough time has not elapsed.

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Using the diagonalizable property of the circulant matrix, the expression of equation ( 6) in the frequency domain can be represented as follows: where represent the Fourier transform of , respectively, and represents the conjugate matrix of . Suppose the coordinate of the target point M in W is , the coordinate of its projection m in I is ( x i, y i), and the coordinate of the origin o i of I in G is . Then, the relationship between G and I can be expressed as follows: where dx and dy are the physical dimensions of the unit pixel on the x i axis and the y i axis, respectively. The literature suggested that machine learning, the ensemble of classifiers, and boosting are known to increase the accuracy of solving the class imbalance problem. Our study uses a machine learning classification model on structured data for three classes and differs from most other studies examined in the related work. Some of the studies applied machine learning to structured data. Still, our approach differs from these studies because we compared the performance of machine learning models before and after class balancing.

This statistical release presents information relating to the gender, ethnicity, nationality, religion and age of Military Personnel employed by the Ministry of Defence (MOD). This publication meets the Department’s obligations under the Public Sector Equality Duty to provide information on its workforce identified by the Equality Act 2010. Declaration of ethnic origin is not mandatory for Armed Forces personnel. These statistics relate to personnel with a known ethnicity. At 1 October 2020, the percentage of personnel who declared an ethnicity was: UK Regular Forces To increase female personnel to 15 per cent of total intake by 2020. This target has been set bu the Minister of State for the Armed Forces. Recently comparative analysis approaches in classifying imbalanced and balanced datasets are widespread in the literature. The study by Vijayvargiya et al. [ 38] was used various machine learning models on the original normal and abnormal subjects about knee from electromyography (EMG) data. The extra tree classifier found the best accuracy after oversampling at 93.3%. There was no improvement in the performance metrics through various class balancing techniques. The motion state equation of the system is as follows: where A k is the state transition matrix of the system at time k, x k-1 is the state vector of the system at time k-1, B k is the control input matrix of the system at time k, u k is the control vector of the system at time k, and is the noise of the system at time k.

Intake to Future Reserves 2020 Forces comprises new entrants, Regular to Reserve transfers, Reserve re-joiners, and Reserve personnel joining from another part of the Reserves that are not included in the FR20 target population. Female Intake This publication contains diversity information on the strength, intake and outflow of the UK Regular Forces and the Future Reserves 2020 ( FR20) population, both overall and for each of the Services, including the Royal Navy/Royal Marines (RN/RM), Army and Royal Air Force ( RAF). Statistics are for trained and untrained personnel unless otherwise stated. Ethnic origin and religion data are self-reported on the Joint Personnel Administration ( JPA) database. Any percentages or figures quoted within this report relate to those with a known ethnic origin, religion or nationality unless otherwise stated. MOD publicises the importance placed on the Armed Forces being appropriately representative of the diverse society they exist to defend; and states that ‘operational effectiveness depends on cohesion and teamwork, which are enhanced by treating everyone fairly, with dignity and respect’. Army Trained Strength The population of England and Wales is growing at its fastest rate since the 1960s - Neil Park from the ONS says this is predominantly being driven by international migration