Background and Objectives: Depression is the most common mood disorder, which may be experienced by most of the people during their life. Food insecurity may result in mental disorders such as anxiety and depression. The aim of this study was to assess depression status and its relation to household food insecurity in women living in Northwest of Iran. Materials and Methods:In this cross-sectional community-based study, 480 women with high-school children were selected from East Azerbaijan Province, Iran, using random sampling method. Beck depression inventory, 18-item food security questionnaires and socio-economic questionnaires were completed by the participants and then weight and height of the participants were measured. Independent sample t-test, chi-square test and binary multiple logistic regressions were used for data analysis. The P<0.05 was considered statistically significant. Results:In this study, frequencies of depression and household food insecurity included 43.1 and 48.3%, respectively.Results indicated significant positive correlations between the food insecurity and depression in women. Of the studied socio-economic variables, age, family size, economic status, occupational status of women and their husbands and educational levels were significantly associated with depression. Body mass index (BMI) of women was negatively associated with depression. Conclusions:Results showed that frequencies of depression and household food insecurity were high in participants. It seems that the improvement of socioeconomic status and subsequently improvement of the women's food security can positively affect their mental health.

The rise of data-intensive applications exposed the limitations of conventional processor-centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth demand. Therefore, recent innovations in computer architecture advocate computein-memory (CIM) and compute-near-memory (CNM), non-von-Neumann paradigms achieving orders-of-magnitude improvements in performance and energy consumption. Despite significant technological breakthroughs in the last few years, the programmability of these systems is still a serious challenge. Their programming models are too low-level and specific to particular system implementations. Since such future architectures are predicted to be highly heterogenous, developing novel compiler abstractions and frameworks become necessary. To this end, we present CINM (Cinnamon), a first end-to-end compilation flow that leverages the hierarchal abstractions to generalize over different CIM and CNM devices and enable device-agnostic and device-aware optimizations. Cinnamon progressively lowers input programs and performs optimizations at each level in the lowering pipeline. To show its efficacy, we evaluate CINM on a set of benchmarks for the well-known UPMEM CNM system and the memristors-based CIM accelerators. We show that Cinnamon, supporting multiple hardware targets, generates highperformance code comparable to or better than state-of-the-art implementations. CCS CONCEPTS• Hardware → Emerging architectures; Emerging tools and methodologies; Emerging languages and compilers; • Computing methodologies → Parallel computing methodologies.1 PIM, CIM, and in-memory computing (IMC) are used alternatively in the literature. We will use CIM in this paper.

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&lt;p&gt;Total Electron Content (TEC) measured by the Global Positioning System (GPS) is useful to register the pre-earthquake ionospheric anomalies appearing before a large earthquake. In this paper, the TEC value was predicted using the deep neural network. Also, the anomaly is detected utilizing this predicted value and the definition of the threshold value, leading to the use of the anomaly as a precursor. In neural networks, Convolutional Neural Network (ConvNets or CNNs) is one of the main categories to do images recognition, image classifications, object detections, facial recognition, etc. In this study, the CNNs has been applied to the ionospheric TEC of the Global Ionosphere Maps (GIM) data on a powerful earthquake in Chile on the 1st of April in 2014. In this method, a two-hour TEC observation is converted into a time series for this region for several consecutive days before and after the occurrence of an earthquake. The prediction of the non-linear time series is formulated as a method for specific pattern recognition in the input data using ConvNets. Results indicate that under suitable conditions the TEC values can be estimated properly in the aforementioned days and hours by ConvNets. In order to show the efficiency of this method in predicting the time series, the results obtained from this research were compared with those from other researches.&lt;/p&gt;