Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

How to improve compliance with protective health measures during the covid-19 outbreak. Testing a moderated mediation model and machine learning algorithms

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance

Multiple chemical sensitivity syndrome. A principal component analysis of symptoms

Multiple Chemical Sensitivity (MCS) is a chronic and/or recurrent condition with somatic, cognitive, and affective symptoms following a contact with chemical agents whose concentrations do not correlate with toxicity in the general population. Its prevalence is not well defined; it mainly affects women between 40 and 50 years, without variations in ethnicity, education and economic status. We aimed to assess the core symptoms of this illness in a sample of Italian patients. Two physicians investigated different symptoms with a checklist compilation in 129 patients with MCS (117 women). We conducted a categorical Principal Component Analysis (CATPCA) with Varimax rotation on the checklist dataset. A typical triad was documented: hyperosmia, asthenia, and dyspnoea were the most common symptoms. Patients also frequently showed cough and headache. The CATPCA showed seven main factors: 1, neurocognitive symptoms; 2, physical (objective) symptoms; 3, gastrointestinal symptoms; 4, dermatological symptoms; 5, anxiety-depressive symptoms; 6, respiratory symptoms; 7, hyperosmia and asthenia. Patients showed higher mean prevalence of factors 7 (89.9%), 6 (71.7%), and 1 (62.13%). In conclusion, MCS patients frequently manifest hyperosmia, asthenia, and dyspnoea, which are often concomitant with other respiratory and neurocognitive symptoms. Considering the clinical association that is often made with anxiety, more studies are necessary on the psychosomatic aspects of this syndrome. Further analytical epidemiological studies are needed to support the formulation of aetiological hypotheses of MCS

Psychometric Properties of the Perinatal Anxiety Screening Scale Administered to Italian Women in the Perinatal Period

Literature stressed the importance of using valid, reliable measures to assess anxiety in the perinatal period, like the self-rated Perinatal Anxiety Screening Scale (PASS). We aimed to examine the psychometric properties of the Italian PASS version in a sample of Italian women undergoing mental health screening during their third trimester of pregnancy and its diagnostic accuracy in a control perinatal sample of psychiatric outpatients. Sample comprised 289 women aged 33.17 ± 5.08, range 19–46 years, undergoing fetal monitoring during their third trimester of pregnancy, with 49 of them retested 6 months postpartum. Controls were 60 antenatal or postnatal psychiatric outpatients aged 35.71 ± 5.02, range 22–50 years. Groups were assessed through identical self- and clinician-rating scales. Confirmatory Factor Analysis (CFA), Principal Component Analysis (PCA), Pearson's correlations and receiver operating characteristic were conducted for PASS. PCA and CPA confirmed four-factor structure with slight differences from the original version. Construct validity and test-retest reliability were supported. Cut-off was 26. The PASS correlated with principal anxiety scales. Despite small sample size, findings confirm reliability and validity of the Italian PASS version in assessing anxiety symptoms in the perinatal period. Its incorporation in perinatal care will improve future mother and child psychological health

Black box modelling of a latent heat thermal energy storage system coupled with heat pipes

This paper presents black box models to represent a LHTESS (Latent Heat Thermal Energy Storage System) coupled with heat pipes, aimed at increasing the storage performance and at decreasing the time of charging/discharging. The presented storage system is part of a micro solar CHP plant and the developed model is intended to be used in the simulation tool of the overall system, thus it has to be accurate but also fast computing. Black box data driven models are considered, trained by means of numerical data obtained from a white box detailed model of the LHTESS and heat pipes system. A year round simulation of the system during its normal operation within the micro solar CHP plant is used as dataset. Then the black box models are trained and finally validated on these data. Results show the need for a black box model that can take into account the different seasonal performance of the LHTESS. In this analysis the best fit was achieved by means of Random Forest models with an accuracy higher than 90%

PON1 polymorphisms can predict generalized anxiety and depressed mood in patients with multiple chemical sensitivity

Background: Multiple chemical sensitivity (MCS) is a chronic condition with somatic, cognitive and affective symptoms that follow contact with chemical agents at usually non toxic concentrations. We aimed to assess the role of genetic polymorphisms involved in oxidative stress on anxiety and depression in MCS. Materials & methods: Our study investigated the CAT rs1001179, MPO rs2333227, PON1 rs662 and PON1 rs705379 polymorphisms in MCS. Results: The AG genotype of the PON1 rs662 and the TT and CT genotypes of the PON1 rs705379 were involved in anxiety and depression. Discussion: These results are in line with existing evidence of PON1 involvement in MCS and suggest a further role of this gene in the exhibition of anxiety and depression in this disease

Hybrids: on the crime-terror nexus

Terrorist organizations may complement their military capability with functioning infrastructures and profitable activity in economic ventures as well as in crime. This leads many commentators to focus on the increasing overlap between terrorism and crime, including and particularly organized crime. The present paper is devoted to the analysis of this controversial overlap, and after providing a concise outline of definitions of organized crime and terrorism found in criminology, highlights similarities and differences between the two forms of criminality, along with the ambiguity of the very notion of ‘crime-terror nexus’

Drug Issues: An Italian Perspective

Il volume contiene: a. Social Policies against Drug Abuse in Italy, di Fernanda Contri; b. An Epidemiological Overview of the Situation of Illicit Drug Abuse in Italy, di F. Mariani, R. Guaiana e T. De Fiandra; c. The Psychopatology of Addiction: A Review, di L. Cancrini; d. Family Therapy with Drug Addicts’ Families, di M. Coletti; e. Therapeutic Communities, di G. Cancrini, F. De Gregorio e F. Cardella; f. Immunological, Clinical and Epidemiological Aspects of an HIV-1 Positive Cohort, di I. Mezzaroma et al.; g. Antidrug Legislation in Italy: Historical Background and Present Status, di G. Di Gennaro; h. Drug Addiction and Juvenile Justice, di G. Scardaccione; i. Drug and Alcohol Problems in the Workplace in Italy, di F. Bruno, j. The Criminal Responsibility of drug and alcohol abusers, di S. Ferracuti e M. Marasc

On the existence of some skew-normal stationary processes

Recently some authors have introduced in the literature stationary stochastic processes, in the time and in the spatial domains, whose finite-dimensional marginal distributions are multivariate skew-normal. Here we show with a counter-example that the characterizations of these processes are not valid and so that these processes do not exist. In particular, we show through a marginalization argument that the set of finite-dimensional marginal distributions of these processes is not self-coherent. Besides, we point our attention to some valid constructions of stationary stochastic processes which can be used to model skewed data