Soybeans are rich in proteins and lipids and have become a staple part of the human diet. Besides their nutritional excellence, they have also been shown to contain various functional components, ...including isoflavones, and have consequently received increasing attention as a functional food item. Isoflavones are structurally similar to 17-β-estradiol and bind to estrogen receptors (ERα and ERβ). The estrogenic activity of isoflavones ranges from a hundredth to a thousandth of that of estrogen itself. Isoflavones play a role in regulating the effects of estrogen in the human body, depending on the situation. Thus, when estrogen is insufficient, isoflavones perform the functions of estrogen, and when estrogen is excessive, isoflavones block the estrogen receptors to which estrogen binds, thus acting as an estrogen antagonist. In particular, estrogen antagonistic activity is important in the breast, endometrium, and prostate, and such antagonistic activity suppresses cancer occurrence. Genistein, an isoflavone, has cancer-suppressing effects on estrogen receptor-positive (ER+) cancers, including breast cancer. It suppresses the function of enzymes such as tyrosine protein kinase, mitogen-activated kinase, and DNA polymerase II, thus inhibiting cell proliferation and inducing apoptosis. Genistein is the most biologically active and potent isoflavone candidate for cancer prevention. Furthermore, among the various physiological functions of isoflavones, they are best known for their antioxidant activities. S-Equol, a metabolite of genistein and daidzein, has strong antioxidative effects; however, the ability to metabolize daidzein into S-equol varies based on racial and individual differences. The antioxidant activity of isoflavones may be effective in preventing dementia by inhibiting the phosphorylation of Alzheimer’s-related tau proteins. Genistein also reduces allergic responses by limiting the expression of mast cell IgE receptors, which are involved in allergic responses. In addition, they have been known to prevent and treat various diseases, including cardiovascular diseases, metabolic syndromes, osteoporosis, diabetes, brain-related diseases, high blood pressure, hyperlipidemia, obesity, and inflammation. Further, it also has positive effects on menstrual irregularity in non-menopausal women and relieving menopausal symptoms in middle-aged women. Recently, soybean consumption has shown steep increasing trend in Western countries where the intake was previously only 1/20–1/50 of that in Asian countries. In this review, Ihave dealt with the latest research trends that have shown substantial interest in the biological efficacy of isoflavones in humans and plants, and their related mechanisms.
We propose a deep convolutional neural network (CNN) inference processor based on a novel enhanced output stationary (EOS) dataflow. Based on the observation that some activations are commonly used ...in two successive convolutions, the EOS dataflow employs dedicated register files (RFs) for storing such reused activation data to eliminate redundant memory accesses for highly energy-consuming SRAM banks. In addition, processing elements (PEs) are split into multiple small groups such that each group covers a tile of input activation map to increase the usability of activation RFs (ARFs). The processor has two different voltage/frequency domains. The computation domain with 512 PEs operates at near-threshold voltage (NTV) (0.4 V) and 60-MHz frequency to increase energy efficiency, while the rest of the processors including 848-KB SRAMs run at 0.7 V and 120-MHz frequency to increase both on-chip and off-chip memory bandwidths. The measurement results show that our processor is capable of running AlexNet at 831 GOPS/W, VGG-16 at 1151 GOPS/W, ResNet-18 at 1004 GOPS/W, and MobileNet at 948 GOPS/W energy efficiency.
It is estimated that more than two-thirds of the population in sub-Saharan Africa (SSA) must leave their home to collect water, putting them at risk for a variety of negative health outcomes. There ...is little research, however, quantifying who is most affected by long water collection times.
This study aims to a) describe gender differences in water collection labor among both adults and children (< 15 years of age) in the households (HHs) that report spending more than 30 minutes collecting water, disaggregated by urban and rural residence; and b) estimate the absolute number of adults and children affected by water collection times greater than 30 minutes in 24 SSA countries.
We analyzed data from the Demographic Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) (2005-2012) to describe water collection labor in 24 SSA countries.
Among households spending more than 30 minutes collecting water, adult females were the primary collectors of water across all 24 countries, ranging from 46% in Liberia (17,412 HHs) to 90% in Cote d'Ivoire (224,808 HHs). Across all countries, female children were more likely to be responsible for water collection than male children (62% vs. 38%, respectively). Six countries had more than 100,000 households (HHs) where children were reported to be responsible for water collection (greater than 30 minutes): Burundi (181,702 HHs), Cameroon (154,453 HHs), Ethiopia (1,321,424 HHs), Mozambique (129,544 HHs), Niger (171,305 HHs), and Nigeria (1,045,647 HHs).
In the 24 SSA countries studied, an estimated 3.36 million children and 13.54 million adult females were responsible for water collection in households with collection times greater than 30 minutes. We suggest that accessibility to water, water collection by children, and gender ratios for water collection, especially when collection times are great, should be considered as key indicators for measuring progress in the water, sanitation and hygiene sector.
A scalable deep-learning accelerator supporting the training process is implemented for device personalization of deep convolutional neural networks (CNNs). It consists of three processor cores ...operating with distinct energy-efficient dataflow for different types of computation in CNN training. Unlike the previous works where they implement design techniques to exploit the same characteristics from the inference, we analyze major issues that occurred from training in a resource-constrained system to resolve the bottlenecks. A masking scheme in the propagation core reduces a massive amount of intermediate activation data storage. It eliminates frequent off-chip memory accesses for holding the generated activation data until the backward path. A disparate dataflow architecture is implemented for the weight gradient computation to enhance PE utilization while maximally reuse the input data. Furthermore, the modified weight update system enables an 8-bit fixed-point computing datapath. The processor is implemented in 65-nm CMOS technology and occupies 10.24 mm 2 of the core area. It operates with the supply voltage from 0.63 to 1.0 V, and the computing engine runs in near-threshold voltage of 0.5 V. The chip consumes 40.7 mW at 50 MHz with the highest efficiency and achieves 47.4 <inline-formula> <tex-math notation="LaTeX">\mu \text{J} </tex-math></inline-formula>/epoch of training efficiency for the customized CNN model.
In addition to providing nutrients, food can help prevent and treat certain diseases. In particular, research on soy products has increased dramatically following their emergence as functional foods ...capable of improving blood circulation and intestinal regulation. In addition to their nutritional value, soybeans contain specific phytochemical substances that promote health and are a source of dietary fiber, phospholipids, isoflavones (e.g., genistein and daidzein), phenolic acids, saponins, and phytic acid, while serving as a trypsin inhibitor. These individual substances have demonstrated effectiveness in preventing chronic diseases, such as arteriosclerosis, cardiac diseases, diabetes, and senile dementia, as well as in treating cancer and suppressing osteoporosis. Furthermore, soybean can affect fibrinolytic activity, control blood pressure, and improve lipid metabolism, while eliciting antimutagenic, anticarcinogenic, and antibacterial effects. In this review, rather than to improve on the established studies on the reported nutritional qualities of soybeans, we intend to examine the physiological activities of soybeans that have recently been studied and confirm their potential as a high-functional, well-being food.
Peptides present in foods are involved in nutritional functions by supplying amino acids; sensory functions related to taste or solubility, emulsification, etc.; and bioregulatory functions in ...various physiological activities. In particular, peptides have a wide range of physiological functions, including as anticancer agents and in lowering blood pressure and serum cholesterol levels, enhancing immunity, and promoting calcium absorption. Soy protein can be partially hydrolyzed enzymatically to physiologically active soy (or soybean) peptides (SPs), which not only exert physiological functions but also help amino acid absorption in the body and reduce bitterness by hydrolyzing hydrophobic amino acids from the C- or N-terminus of soy proteins. They also possess significant gel-forming, emulsifying, and foaming abilities. SPs are expected to be able to prevent and treat atherosclerosis by inhibiting the reabsorption of bile acids in the digestive system, thereby reducing blood cholesterol, low-density lipoprotein, and fat levels. In addition, soy contains blood pressure-lowering peptides that inhibit angiotensin-I converting enzyme activity and antithrombotic peptides that inhibit platelet aggregation, as well as anticancer, antioxidative, antimicrobial, immunoregulatory, opiate-like, hypocholesterolemic, and antihypertensive activities. In animal models, neuroprotective and cognitive capacity as well as cardiovascular activity have been reported. SPs also inhibit chronic kidney disease and tumor cell growth by regulating the expression of genes associated with apoptosis, inflammation, cell cycle arrest, invasion, and metastasis. Recently, various functions of soybeans, including their physiologically active functions, have been applied to health-oriented foods, functional foods, pharmaceuticals, and cosmetics. This review introduces some current results on the role of bioactive peptides found in soybeans related to health functions.
With the ever-increasing parameter size of deep learning models, conventional ASIC-based accelerators in mobile environments suffer from low energy budget due to limited memory capacity and frequent ...data movements. Binary neural networks (BNNs) deployed in ReRAM-based in-situ accelerators provide a promising solution, and various related architectures have been proposed recently. However, their performances are largely compromised by the tremendous cost of domain conversion via analog-to-digital converters (ADCs), essential for mixed-signal processing in ReRAM. This paper identifies two root causes of the need for such ADCs and proposes effective solutions to address them. First, we minimize redundant operations in BNNs and reduce the number of ReRAM arrays with ADCs approximately by half. We also propose a partial-sum range adjustment technique based on a layer remapping to deal with the remaining ADCs. Proper handling of the partial-sum distribution allows ReRAM-based in-situ processing without domain conversion, completely bypassing the need for ADCs. Experimental results show that the proposed architecture achieves a 3.44x speedup and 91.5% energy savings, making it an attractive solution for on-device AI at the edge.
Job insecurity, the subjective perception of fear due to uncertainty about job continuity, could have long-term health effects. We aimed to examine the effect of job insecurity on depression among ...automobile sales workers in South Korea.
We analyzed data collected in 2007 and 2014 from a longitudinal cohort of 560 sales workers from an automobile company in South Korea. Change in job insecurity was classified into four groups: secure to secure; insecure to secure; secure to insecure; and insecure to insecure.
After adjusting for potential confounders, workers in the insecure to insecure group had a significantly higher likelihood of depression in 2014 than workers in the secure to secure group (OR: 2.74; 95%CI: 1.41, 5.31).
We found that chronic job insecurity may be a risk factor for developing depression among Korean automobile sales workers. This is the first longitudinal study examining the association between job insecurity and depression in South Korea.
Resistive RAM(ReRAM) is gaining attention as a suitable memory platform for accelerating deep neural networks(DNNs) in an energy-efficient way. However, energy-efficient ReRAM-based DNN accelerators ...suffer from serious Stuck-At-Fault(SAF) issues that significantly degrade the inference accuracy. SAF is a device-level non-ideality, and the problems of SAF worsen in the realistic ReRAM with low cell resolution. To address the problem in the realistic ReRAM, we present a framework for mitigating SAF on ReRAM-based accelerators(Fault-free). We first analyze the impact of SAF on low-resolution cells. Based on the analysis, we present offline compilation, which drastically reduces the impact of SAF on inference accuracy. At the first stage, we extract indices of distorted weights due to SAF. For extracted weights, fault-aware weight decomposition and closest value mapping are applied to minimize the error of weights. In the online phase, the target DNN model is executed on the ReRAM-based accelerator along with lightweight compensation units. The online compensation is selectively performed for a small portion of weights to reduce the hardware overhead. With the proposed framework, the ReRAM-based accelerator successfully ensures the inference accuracy of various DNN models with an average area of 5% and an energy overhead of 0.8% from an ideal ReRAM-based accelerator.
With the growing demand for processing deep learning applications on edge devices, on-device DNN training has become a major workload to execute a variety of vision tasks suited for users. Therefore, ...architectures employed with algorithm co-design to accelerate the training process have been steadily studied. However, previous solutions are mostly supported by extended versions of the inference studies, such as sparsity, data flow, quantization, etc. Moreover, most works examine their schemes on from-the-scratch training that cannot tolerate inaccurate computing. Accordingly, there are still factors that hinder the overall speed of the DNN training process that has not been addressed in practical workloads. In this work, we propose a runtime convergence monitor to achieve massive computational savings in the practical on-device training workloads (i.e., transfer-learning-based task adaptation). By monitoring the network output data, we determine the training intensity of incoming tasks and adaptively detect the convergence in iteration intervals for training diverse datasets. Furthermore, we enable the computation skip of converged images determined by the monitored prediction probability to enhance the training speed within an iteration. As a result, we perform an accurate but fast convergence in model training for the task adaptation with minimal overhead. Unlike the previous approximation methods, our monitoring system enables runtime optimization and can be easily applicable to any type of accelerator attaining significant speedup. Evaluation results on various datasets show geomean of <inline-formula> <tex-math notation="LaTeX">2.2\times </tex-math></inline-formula> speedup when applied in any systolic architectures and further enhancement of <inline-formula> <tex-math notation="LaTeX">3.6\times </tex-math></inline-formula> when applied in accelerators dedicated for on-device training.
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