State business: gender, sex and marriage in Tajikistan

This article examines the relation of the state to masculinity and sexuality by way of an exploration of the sexual problems of a young man and his wife in Tajikistan at the end of the Soviet era. It suggests that the regime’s inattention to this kind of issue was bound up with the importance to the state of projecting appropriate versions of masculinity. It further posits the idea that the continued refusal of the independent Tajik state to offer appropriate treatments for sexual dysfunction is consistent with the image of modernity President Rahmon wishes to present to the world. The article shows that as masculinity discursively occupies the superior gender position, with men expected to dominate, the state is itself impotent to respond when they are, in fact, unable to do so in sexual practice. However, the myth of male dominance persists to the point that it may prevent women from seeing beyond their subordination and finding mutually beneficial solutions in their familial and sexual relationships

A resource usage efficient distributed allocation algorithm for 5G Service Function Chains

International audienceRecent evolution of networks introduce new challenges for the allocation of resources. Slicing in 5G networks allows multiple users to share a common infrastructure and the chaining of Network Function (NF)s introduces constraints on the order in which NFs are allocated. We first model the allocation of resources for Chains of NFs in 5G Slices. Then we introduce a distributed mutual exclusion algorithm to address the problem of the allocation of resources. We show with selected metrics that choosing an order of allocation of the resources that differs from the order in which resources are used can give better performances. We then show experimental results where we improve the usage rate of resources by more than 20% compared to the baseline algorithm in some cases. The experiments run on our own simulator based on SimGrid

Paleoseismic History of the Dead Sea Fault Zone

International audienceThe aim of this entry is to describe the DSF as a transform plate boundary pointing out the rate of activedeformation, fault segmentation, and geometrical complexities as a control of earthquake ruptures. Thedistribution of large historical earthquakes from a revisited seismicity catalogue using detailedmacroseismic maps allows the correlation between the location of past earthquakes and fault segments.The recent results of paleoearthquake investigations (paleoseismic and archeoseismic) with a recurrenceinterval of large events and long-term slip rate are presented and discussed along with the identification ofseismic gaps along the fault. Finally, the implications for the seismic hazard assessment are also discussed

A review of bioanalytical techniques for evaluation of cannabis (Marijuana, weed, Hashish) in human hair

Cannabis products (marijuana, weed, hashish) are among the most widely abused psychoactive drugs in the world, due to their euphorigenic and anxiolytic properties. Recently, hair analysis is of great interest in analytical, clinical, and forensic sciences due to its non-invasiveness, negligible risk of infection and tampering, facile storage, and a wider window of detection. Hair analysis is now widely accepted as evidence in courts around the world. Hair analysis is very feasible to complement saliva, blood tests, and urinalysis. In this review, we have focused on state of the art in hair analysis of cannabis with particular attention to hair sample preparation for cannabis analysis involving pulverization, extraction and screening techniques followed by confirmatory tests (e.g., GC–MS and LC–MS/MS). We have reviewed the literature for the past 10 years’ period with special emphasis on cannabis quantification using mass spectrometry. The pros and cons of all the published methods have also been discussed along with the prospective future of cannabis analysis

Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks

Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm2, respectively, with a corresponding mean absolute percentage error (MAPE) of 7 and 9%, respectively. On qualitative analysis of 32 test samples, 30 predicted segmentations from the U-net algorithm were accepted while 2 were rejected. Based on the results from quantitative and qualitative evaluation, this study demonstrates that CNN-based deep learning algorithms are effective at segmenting extraocular muscles and measuring muscles sizes