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The increasing conviction rates of stalking in the UK have prompted efforts to identify factors that may influence individuals to engage in such behaviour. Over two million people in England and Wales experience stalking every year, with estimated reoffending rates for stalking being between 25 and 55% (ONS, 2022; McEwan et al., 2017). Research has identified risk factors that may contribute towards stalking behaviours, which has included obsessive relational pursuit and online impulsivity (Post et al., 2014a; Rocheleau, 2019). This has resulted in researchers postulating a link between facets of neurodiversity and stalking behaviour (Freckelton, 2013). The purpose of this study was to examine the evidence base for any link between neurodiversity and stalking.

The Systematic Review was performed according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Papers were screened for quality appraisal and risk of bias. The initial search yielded 3,880 articles. A total of 10 papers were deemed as meeting the inclusion criteria.

There is insufficient research quality regarding neurodiversity and stalking perpetration due to poor diagnostic reliability of neurodiversity and a lack of reliable tools being used in the research which do not meet the definitions of stalking. As such, the existing research about neurodiversity and stalking perpetration is inconclusive and predominantly unreliable. Tentative evidence indicated that people with neurodiversity were at greater risk of being victims of stalking and that for the minority of people with neurodiversity who engage in stalking the factors that contribute towards this mirror those of neurotypical individuals.

Overall, the factors contributing to stalking highlighted by the included studies, such as difficulties with communication, empathy, insight into social functioning, interpersonal competence and ways of forming relationships with others, seem to map onto the deficits of neurotypical stalking perpetrators (Canter and Youngs, 2012; Lewis et al., 2001; Mullen et al., 2006). However, tools that contextualise neurodiversity in the context of risk assessments for stalking may be helpful (Al-Attar, 2019, 2021).

Recommendations for further research are made to gain a robust understanding of any potential relationship between neurodiversity and stalking and in particular a risk of stalking victimisation.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.