Approaching the socialist factory and its workforce: considerations from fieldwork in (former) Yugoslavia

The socialist factory, as the ‘incubator’ of the new socialist (wo)man, is a productive entry point for the study of socialist modernization and its contradictions. By outlining some theoretical and methodological insights gathered through field-research in factories in former Yugoslavia, we seek to connect the state of labour history in the Balkans to recent breakthroughs made by labour historians of other socialist countries. The first part of this article sketches some of the specificities of the Yugoslav self-managed factory and its heterogeneous workforce. It presents the ambiguous relationship between workers and the factory and demonstrates the variety of life trajectories for workers in Yugoslav state-socialism (from model communists to alienated workers). The second part engages with the available sources for conducting research inside and outside the factory advocating an approach which combines factory and local archives, print media and oral history.     

More Sinned Against than Sinning? The Case of the Arab Office, Washington, 1945-1948

In 1945, as a final settlement of the Palestine question drew near, the Arab states established the Arab Office, Washington, as part of their unprecedented effort to influence public and elite opinion on this matter in the United States. It was staffed by many of the leading young Arab intellectuals of the era. This article charts the Arab Office's attempt to reduce American support for the creation of a Jewish state in Palestine. In particular, it examines the accusations, made at the time, that the Arab Office, in pursuing its anti-Zionist agenda had co-operated with leading American anti-Semites and was under the control of the notorious former Mufti of Jerusalem, who had collaborated with Hitler during the Second World War.     

Nanoscale analysis of the interaction between cyanoacrylate and vacuum metal deposition in the development of latent fingermarks on low-density polyethylene

Vacuum metal deposition (VMD) has been previously demonstrated as an effective development technique for latent fingermarks and in some cases has been shown to enhance prints developed with cyanoacrylate (CA) (superglue) fuming. This work utilizes scanning electron microscopy (SEM) to investigate the interactions of the two development techniques when applied to latent fingermarks on low-density polyethylene. CA is shown to act principally on the eccrine deposits around sweat pores, where polymerization results in long polymer fibrils a few 100 nm in width. Subsequent VMD processing results in additional areas of development, for example, between pores. However, the primary mode of deposition of zinc is by interaction with the polymerized CA, the fibrils of which become decorated with zinc nanoparticles. Areas with limited CA deposition and no significant polymerization are also enhanced with the VMD process, resulting in increased print development.     

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.     

A study of DNA transfers onto plastic packets placed in personal bags

The ability to detect low level DNA brings with it the uncertainty of whether the detected DNA is a result of transfer. To address this uncertainty, a simulation study was conducted in which a mock illicit drug packet was placed into the personal bags of individuals. When the average transit time of the packets was increased from around 2 h to more than 14 h, the percentage of the DNA profiles recovered from the packets which could be attributed to the individuals increased greatly from 5.3% to 48.6%. We found that drug packers who were poor shedders could not be included as contributors to the DNA profiles from the drug packets at all and there was a higher chance that individuals other than themselves could be included as contributors to the DNA profile recovered from drug packets. We also found that it was equally likely that the drug packers who had direct contact with the drug packets and bag owners who did not, could be included as contributors to the DNA profiles recovered from the packets. The results in this study highlight the importance of taking into consideration the transit time of drug packet, the shedder status of the alleged packer and the history of an item, when evaluating DNA evidence in the context of illicit drug activities.     

A novel chitosan/tripolyphosphate/L‐lysine conjugates for latent fingerprints detection and enhancement

Most chemical and physical methods employed in visualizing latent marks have shown detrimental effects on human health and, therefore, some research groups have directed their attention to the utilization of various (bio)polymers with the aforementioned purpose. Although chitosan is widely used in medicine, pharmacy, food, and drug delivery systems, there are very few studies that address this biopolymer utilization in forensic applications, such as the detection of latent fingerprints. We used chitosan crosslinked with sodium tripolyphosphate and conjugated with L‐lysine to enhance the visualization of latent fingerprints, due to its ability to interact with fingerprint sweat residues. These conjugates were prepared using different (w/w) amounts of chitosan and tripolyphosphate (6/1; 4/1; 1/1; 1/4; and 1/6), and those with the most promising results (i.e., 6/1 formulation) were investigated in detail. Fourier transform infrared (FT‐IR) spectroscopy confirmed interactions between components of the systems. Optical microscopy and scanning electron microscope (SEM) analysis showed that prepared powder formulations were uniform in size and confirmed that chitosan/tripolyphosphate/lysine conjugates bind easily to the sweat and lipid residues present in the latent fingerprints. The testing of prepared conjugates demonstrated the potential of these systems as bio‐based powder substitution for commercially available powders.     

Rapid and nondestructive analysis of lipstick on different substrates using ATR-FTIR spectroscopy and chemometrics

Lipstick can be an important piece of evidence in crimes like murders, rapes, and suicides. Due to its prevalence, it can be an important corroborative evidence in crime reconstruction. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. We report the use of nondestructive ATR-FTIR spectroscopy combined with chemometrics for the classification of 10 brands of lipsticks with nine samples each. Chemometric method of partial least square-discriminant analysis (PLS-DA) has been employed to interpret the data and classify the samples into their respective classes. The PLS-DA model provides an AUC figure above 0.99 in all brands except one; for which it is slightly less at 0.94. We have also tested the traces of these lipstick samples on different substrates treating them as unknowns in the already trained PLS-DA model. 100% of the samples on nine substrates viz. a cotton, nylon, plastic, dry tissue, denim (blue jeans), wet tissue, nitrile gloves, white paper, and polyester were correctly attributed to their source brand. In conclusion, the results suggest that ATR-FTIR combined with the chemometrics is a rapid, nondestructive, and accurate method for the discrimination and source attribution of lipstick. This study has potential for use in actual forensic casework conditions.     

Constructing patient–psychiatrist relations in psychiatric hospitals: the role of space and personal action

This essay investigates the role of space and personal action in the construction of patient–psychiatrist relations at psychiatric hospitals. In order to explore such a theme, the writings of R.D. Laing prove to be salutary. This is namely accredited to Laing's tenet that the staff and patients of a psychiatric hospital are institutionalised by both physical structures and personal action. A central approach taken in this essay is to explore Laing's theory through an inter-textual reading of Michel Foucault's Madness and Civilization (1967) and Erving Goffman's Asylums (1961).     

Bruise dating using deep learning

The bruise dating can have important medicolegal implications in family violence and violence against women cases. However, studies show that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This research proposes a model, based on deep convolutional neural networks, for bruise dating using only images, by age ranges, ranging from 0–2 days to 17–30 days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, 20 classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross‐validation, and data augmentation were used. Numerical experiments show that classification models based on MnasNet have better results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding 40% precision reported in the literature. Also, it was observed that the precision of the model decreases with the age of the bruise.