The Legalome: Microbiology, Omics and Criminal Justice

Microbial Biotechnology, Год журнала: 2025, Номер 18(3)

Опубликована: Март 1, 2025

ABSTRACT Advances in neuromicrobiology and related omics technologies have reinforced the idea that unseen microbes play critical roles human cognition behaviour. Included this research is evidence indicating gut microbes, through direct indirect pathways, can influence aggression, anger, irritability antisocial Moreover, manufacture chemicals are known to compromise cognition. For example, recent court decisions United States Europe acknowledge produce high levels of ethanol, without consumption alcohol by defendants. The dismissal driving while intoxicated charges these cases—so‐called auto‐brewery syndrome—highlights way which microbiome knowledge will enhance precision, objectivity fairness our legal systems. Here opinion essay, we introduce concept ‘legalome’—the application science forensic psychiatry criminal law. We argue rapid pace microbial discoveries, including those challenge ideas free moral responsibility, necessitate a reconsideration traditional doctrines justifications retributive punishment. implications extend beyond courtroom, challenging us reconsider how environmental factors—from diet socioeconomic conditions—might shape preventative rehabilitative efforts their effects on microbiome.

Язык: Английский

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Research on crime motivation identification and quantitative analysis methods based on EEG signals

Frontiers in Psychology, Год журнала: 2025, Номер 16

Опубликована: Март 18, 2025

Introduction Understanding and quantifying crime motivation is essential for developing effective interventions in criminology psychology. This research, closely aligned with quantitative psychology measurement, presents a novel approach to identifying analyzing motivations using EEG signals. Traditional methods often fail capture the intricate interplay of individual, social, environmental factors due data sparsity absence real-time adaptability. Methods In this study, we introduce Hierarchical Crime Motivation Network (HCM-Net), multi-layered framework that integrates signal analysis social temporal modeling. HCM-Net employs neural network-based individual feature encoders, graph networks interaction analysis, predictors evolution motivations. To enhance practical applicability, Dynamic Risk-Adaptive Strategy (DRAS) complements by incorporating adaptation, scenario-based simulations, targeted interventions. addresses challenges such as ethical considerations interpretability employing Shapley values attribution bias mitigation techniques. Results Experiments datasets demonstrate superior performance proposed classifying high-risk individuals compared state-of-the-art Discussion These findings highlight potential integrating advanced computational prevention psychological research.

Язык: Английский

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Criminal emotion detection framework using convolutional neural network for public safety

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 1, 2025

In the era of rapid societal modernization, issue crime stands as an intrinsic facet, demanding our attention and consideration. As communities evolve adopt technological advancements, dynamic landscape criminal activities becomes essential aspect that requires careful examination proactive approaches for public safety application. this paper, we proposed a collaborative approach to detect patterns emotions with aim enhancing judiciary decision-making. For same, utilized two standard datasets - dataset comprised different features crime. Further, emotion has 135 classes help AI model efficiently find emotions. We adopted convolutional neural network (CNN) get first trained on bifurcate non-crime images. Once is detected, faces are extracted using region interest stored in directory. Different CNN architectures, such LeNet-5, VGGNet, RestNet-50, basic CNN, used face. The models enhance framework evaluated evaluation metrics, training accuracy, loss, optimizer performance, precision-recall curve, complexity, time, inference time. detection, achieves remarkable accuracy 92.45% LeNet-5 outperforms other architectures by offering 98.6%.

Язык: Английский

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TBP-XFE: A transformer-based explainable framework for EEG music genre classification with hemispheric and directed lobish analysis

Applied Acoustics, Год журнала: 2025, Номер 239, С. 110855 - 110855

Опубликована: Май 30, 2025

Язык: Английский

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