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The purpose of the paper is to investigate how judges and the general population formulate judgments on legal cases, considering both legal and extralegal factors, with a focus on the significance of the defendant’s sex.

The first experiment aimed to determine if non-lawyers’ judgments are affected by the defendant’s sex, using brief excerpts from indictments with the defendant’s sex interchanged. Study 2 aimed to verify if this effect applies to future lawyers, suggesting a peculiar approval granted by men to women displaying illegal sexual behaviour towards young men.

The findings showed that the sex of the offender only influenced judgments in sexual offences, with male participants being more lenient towards female offenders.

The originality/value of the paper lies in its examination of the influence of the defendant’s sex on judgments made by both judges and the general population, specifically focussing on non-lawyers’ judgments. While previous studies have shown that judges tend to be more lenient towards women in certain cases, this paper adds novelty by investigating whether a similar effect is observed among non-lawyers. Moreover, the research sheds light on the relevance of the defendant's sex in cases of sexual offences and identifies a gender-specific leniency towards female offenders, particularly among male participants. The study also explores how this effect might extend to future lawyers, providing insights into societal attitudes regarding illegal sexual behaviour involving women and young men. Overall, the paper contributes valuable information to the understanding of how sex-based biases can influence legal judgments and decision-making processes.

Exhibiting an unprecedented rate of advancement, technology’s progression over the past two decades has regrettably led to a disturbing increase in the distribution of child sexual abuse materials (CSAM) online. Compounded by the emergence of an underground cryptocurrency market, which serves as a primary distribution channel for these materials, the investigation and sanctioning of CSAM present a complex and unique set of challenges. The purpose of this study is to accurately diagnose the CSAM sentencing landscape and build a more comprehensive, evidence-based legal framework in penology.

The study collected and analyzed case details regarding CSAM sanctions in a database sourced from the US Department of Justice for 2020. Various factors were analyzed such as the victim’s age, offender typology and previous conviction, accompanied by an analysis of how these factors affect the sentence length.

The study found that the hierarchical agency-level interactions give insight into resource allocation prioritization, as well as confirming a close relationship between prior conviction history and sentence length, with the victim’s age inversely related to sentence length. Leveraging data-driven insights, the study paves the way for more targeted and effective sanctions, ultimately contributing to the broader goal of safeguarding children from online sexual exploitation.

The paper provides a critical analysis of the complex landscape surrounding CSAM distribution and judicial sentencing. By examining case details and leveraging data-driven insights, it offers valuable contributions to understanding the interplay between various factors such as victim age, offender typology and prior convictions on sentencing outcomes. This comprehensive approach not only sheds light on the dynamics of CSAM sanctions but also lays the groundwork for evidence-based legal frameworks in penology. Its originality lies in its nuanced examination of hierarchical agency interactions and its potential to inform more targeted interventions for safeguarding children from online exploitation.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.