Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively

Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation

Nowadays, due to technical and economic reasons, the distributed generation (DG) units are widely connected to the low and medium voltage network and created a new structure called micro-grid. Renewable energies (especially wind and solar) based DGs are one of the most important generations units among DG units. Because of stochastic behavior of these resources, the optimum and safe management and operation of micro-grids has become one of the research priorities for researchers. So, in this study, the optimal operation of a typical micro-grid is investigated in order to maximize the penetration of renewable energy sources with the lowest operation cost with respect to the limitations for the load supply and the distributed generation resources. The understudy micro-grid consists of diesel generator, battery, wind turbines and photovoltaic panels. The objective function comprises of fuel cost, start-up cost, spinning reserve cost, power purchasing cost from the upstream grid and the sales revenue of the power to the upstream grid. In this paper, the uncertainties of demand, wind speed and solar radiation are considered and the optimization will be made by using the GAMS software and mixed integer planning method (MIP).Article History: Received May 21, 2016; Received in revised form July 11, 2016; Accepted October 15, 2016; Available onlineHow to Cite This Article: Jasemi, M., Adabi, F., Mozafari, B., and Salahi, S. (2016) Optimal Operation of Micro-grids Considering the Uncertainties of Demand and Renewable Energy Resources Generation, Int. Journal of Renewable Energy Development, 5(3),233-248.http://dx.doi.org/10.14710/ijred.5.3.233-24

Functional Classification and Interaction Selectivity Landscape of the Human SH3 Domain Superfamily

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein–protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3 domain–PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate the binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs, with binding affinities ranging from 0.2 to 125 micromolar, out of 300 tested and potential new SH3 domain-SOS1 interactions. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into the potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3 domain–PRM interactions and be utilized as a general approach for other domain–peptide interactions.</p

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

The pseudo‐natural product rhonin targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment-based design. We describe the de novo combination of different 5-membered NP-derived N-heteroatom fragments to structurally unprecedented “pseudo-natural products” in an efficient complexity-generating and enantioselective one-pot synthesis sequence. The pseudo-NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP-derived chemotypes, and may have novel biological targets. Investigation of the pseudo-NPs in unbiased phenotypic assays and target identification led to the discovery of the first small-molecule ligand of the RHO GDP-dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP-bound RHO GTPases and alters the subcellular localization of RHO GTPases

The Effects of Music Therapy on Anxiety and Depression of Cancer Patients

Background and Purpose: Cancer patients often suffer from anxiety and depression. Various methods are used to alleviate anxiety and depression, but most of them have side effects. Music therapy can be used as a noninvasive method to reduce anxiety and depression. This study aimed to examine the effect of music therapy on anxiety and depression in patients with cancer. Materials and Methods: This quasi-experimental study was conducted attaching hospitals in Urmia city. A total number of sixty patients with depression and anxiety were recruited using random sampling method and divided into two groups of control and intervention. Patients in intervention group listened to light music at least 20 min per day for 3 days. The degree of patients' anxiety and depression was assessed by Hospital Anxiety and Depression Scale at baseline and 3 days after music therapy. Data were analyzed by SPSS version 13 using t-test, Pearson, and ANOVA tests. Results: The results showed no significant differences between demographic variable of intervention and control groups. Our findings indicated a significant decrease in the level of depression and anxiety among intervention group. There were significant relationships between anxiety, depression, and sex (P < 0.001, r = 0.42) as well as education (P = 0.003, r = 0.37). Conclusion: This study revealed positive effects of music therapy on decreasing level of depression and anxiety in patients with cancer. Therefore, it is recommended to include music therapy in the nursing care

Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively

Causes of mortality in a neonatal intensive care unit in Iran: one year data

BACKGROUND Neonatal mortality rate is a major health index. Approximately, 65 of all deaths in the first year of life occur during this 4-week period. The present study was conducted to investigate the mortality rates and causes of death in a neonatal intensive care unit (NICU) in Ahvaz, Iran in a year. METHODS This cross-sectional study was conducted in the NICU of Sina Hospital in Ahvaz. Medical records were studied, and data from 1,040 newborns admitted to the NICU within one year (March 2016 to March 2017) were collected following a checklist. Relevant data of 123 died newborns were collected. Data were analyzed using SPSS, version 20 (SPSS Inc., USA). RESULTS The mortality rate was 11.82 (123 cases) out of 1,040 newborns admitted to NICU. Most of the newborns (48.8) died on days 1-7. The causes of death were respiratory distress syndrome (RDS) (34.1), asphyxia (25.2), anomalies (10.6), sepsis (7.3), intracerebral hemorrhage (8.1), pulmonary hemorrhage (7.3), and other causes (6.4), such as hydrops, severe pneumothorax, severe renal failure, and others. CONCLUSIONS The mortality rate in the NICU of this center was similar to that in other Iranian provinces. The most common causes of NICU mortality included prematurity and its complications, such as asphyxia and RDS. Thus, a strategic plan for reducing preterm delivery and asphyxia are necessary

Partition pruning: Parallelization-aware pruning for dense neural networks

As recent neural networks are being improved to be more accurate, their model's size is exponentially growing. Thus, a huge number of parameters requires to be loaded and stored from/in memory hierarchy and computed in processors to perform training or inference phase of neural network processing. Increasing the number of parameters causes a big challenge for real-time deployment since the memory bandwidth improvement's trend cannot keep up with models' complexity growing trend. Although some operations in neural networks processing are computational intensive such as convolutional layer computing, computing dense layers face with memory bandwidth bottleneck. To address the issue, the paper has proposed Partition Pruning for dense layers to reduce the required parameters while taking into consideration parallelization. We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7.72x speedup of performance and a 2.73x reduction in the energy used for computing pruned fully connected layers in TinyVGG16 model in comparison to running the unpruned model on a single accelerator. Besides, our method showed a limited reduction in accuracy while partitioning fully connected layers