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The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

The prevalence of exposure to adversity is elevated in autistic populations, compared to neurotypical peers. Despite this, the frequency and nature of early adverse experiences are not well understood in autistic adults, with several underlying methodological limitations in the available literature. The purpose of this study is to systematically synthesise and analyse the prevalence of childhood adversity in this marginalised population, in accordance with the adverse childhood experiences (ACEs) framework.

Peer-reviewed empirical research articles were systematically searched for from electronic databases and screened against established inclusion criteria. Pooled prevalence rates for individual ACE types were calculated.

Four papers were included (N = 732), all of which used a predominantly or exclusively female sample. Only sexual abuse was reported in all papers, with a pooled prevalence rate of 38%. Physical abuse and emotional abuse were less frequently explored, with two papers reporting on these ACEs, though obtained comparable and higher pooled prevalence rates (39% and 49%, respectively). Pooled prevalence rates could be calculated for neither neglect nor “household” ACEs because of insufficient data. The limited state of the evidence, in conjunction with high levels of heterogeneity and poor sample representativeness found, positions the ACEs of autistic adults as a critical research priority.

To the best of the authors’ knowledge, this study is the first to systematically synthesise the prevalence of early childhood adversities, as conceptualised in accordance with the ACEs framework, in adults with autistic traits.

The purpose of this paper is to assess sub-national business cycle (BC) synchronization's impact on national cycles in four emerging markets economies with inflation targeting (IT-EMEs): Brazil, Colombia, South Korea and Mexico.

The authors use panel data models with fixed-effects and distributed lags.

The authors disclosed that sub-national synchronization increased national cycle amplitudes during expansion and recession phases. The authors also noticed that South Korea exhibited a more pronounced effect compared to Latin American countries, and this seemed to be associated with differences in the homogeneity of the production structures in the regions of these countries.

The authors cautioned that contrasting the findings with prior research on the effects of regional BC synchronization in IT-EMEs or with studies in different geographical contexts, is not possible due to the absence of prior research endeavors with this specific focus.

This study constitutes a first attempt to explain the impact of subnational cycle synchronization on the magnitude of national cycles in four IT-EMEs.