Assessment of water quality in Canaanland, Ota, Southwest Nigeria

In this study, water points in Canaanland, Ota, and nearby Iju River were analyzed for biological and physicochemical properties including heavy metal content. Water quality parameters examined were pH, alkalinity, salinity, conductivity, turbidity, total hardness, total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (Do), biochemical oxygen demand (BOD), iron (Fe), lead (Pb), zinc (Zn) and chromium (Cr). All the water samples were slightly acidic (5.96 – 6.54) except the bottled/ sachet Hebron water and Iju River water. The results were compared against drinking water quality standards laid by World Health Organization (WHO) and Nigerian Standard for Drinking Water (NSDW). The potable water samples were within the standards for consumable water and so are considered safe for human consumption. The surface waters, on the other hand, have high levels of total dissolved solids, conductivity and salinity. The BOD of the effluent water showed that the water was contaminated and the use of the water for domestic purposes by the inhabitants could lead to hazardous side effects

Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy. In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4x to 2.5x) and fully-connected layers (at least 1.3x for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis

The reliability of the ankle brachial index : a systematic review

Background: The ankle brachial index (ABI) is widely used in clinical practice as a non-invasive method to detect the presence and severity of peripheral arterial disease (PAD). Current guidelines suggest that it should be used to monitor potential progression of PAD in affected individuals. As such, it is important that the test is reliable when used for repeated measurements, by the same or different health practitioners. This systematic review aims to examine the literature to evaluate the inter- and intra-rater reliability of the ABI. Methods: A systematic search of MEDLINE, EMBASE and CINAHL Complete was conducted to 20 January 2019. Two authors independently reviewed and selected relevant studies and extracted the data. Methodological quality was determined using the Quality Appraisal of Reliability (QAREL) Checklist. Results: Fifteen studies of ABI reliability in a range of patient populations were identified as suitable for inclusion in the review: seven considered inter-rater reliability, four intra-rater reliability, and four studies evaluated both inter- and intra-rater reliability. Inter-rater reliability was found to be highly variable, with intraclass correlation coefficients (ICC's) ranging from poor to excellent (ICC 0.42-1.00), while intra-rater also demonstrated considerable variation, with ICCs from 0.42-0.98. Meta-analysis was not possible due to the lack of statistical information reported. Conclusions: Results of included studies suggest the inter- and intra-tester reliability of the ABI is acceptable. However, inconsistencies in obtaining systolic pressure measurements, calculating ABI values, and incomplete reporting of methodologies and statistical analysis make it difficult to determine the validity of the results of included studies. Further research, with more consistent reliability methodology, statistical analysis and reporting conducted in populations at risk of PAD is needed to conclusively determine the ABI reliability

From Starburst to Quiescence: Testing AGN feedback in Rapidly Quenching Post-Starburst Galaxies

Post-starbursts are galaxies in transition from the blue cloud to the red sequence. Although they are rare today, integrated over time they may be an important pathway to the red sequence. This work uses SDSS, GALEX, and WISE observations to identify the evolutionary sequence from starbursts to fully quenched post-starbursts in the narrow mass range $\log M(M_\odot) = 10.3-10.7$, and identifies "transiting" post-starbursts which are intermediate between these two populations. In this mass range, $\sim 0.3\%$ of galaxies are starbursts, $\sim 0.1\%$ are quenched post-starbursts, and $\sim 0.5\%$ are the transiting types in between. The transiting post-starbursts have stellar properties that are predicted for fast-quenching starbursts and morphological characteristics that are already typical of early-type galaxies. The AGN fraction, as estimated from optical line ratios, of these post-starbursts is about 3 times higher ($\gtrsim 36 \pm 8 \%$) than that of normal star-forming galaxies of the same mass, but there is a significant delay between the starburst phase and the peak of nuclear optical AGN activity (median age difference of $\gtrsim 200 \pm 100$ Myr), in agreement with previous studies. The time delay is inferred by comparing the broad-band near NUV-to-optical photometry with stellar population synthesis models. We also find that starbursts and post-starbursts are significantly more dust-obscured than normal star-forming galaxies in the same mass range. About $20\%$ of the starbursts and $15\%$ of the transiting post-starbursts can be classified as the "Dust-Obscured Galaxies" (DOGs), while only $0.8\%$ of normal galaxies are DOGs.The time delay between the starburst phase and AGN activity suggests that AGN do not play a primary role in the original quenching of starbursts but may be responsible for quenching later low-level star formation during the post-starburst phase.Comment: 30 pages, 18 figures,accepted to Ap

"Hey, i'm having these experiences": Tumblr use and young people's queer (dis)connections

© 2019 Paul Byron, Brady Robards, Benjamin Hanckel, Son Vivienne, and Brendan Churchill. This article explores LGBTIQ+ young people's use of Tumblr-a social media platform often associated with queer youth cultures. Drawing on data from surveys (N = 1,304) and interviews (N = 23) with LGBTIQ+ young people in Australia, we argue that existing notions of "queer community" through digital media participation do not neatly align with young people's Tumblr practices. Our participants use Tumblr for connecting with others, yet these connections can be indirect, short term, and anonymous. Connections are often felt and practiced without directly communicating with other users, and many participants described their connections to the Tumblr platform itself as intense, pivotal to learning about genders and sexualities, and sometimes "toxic." We suggest that Tumblr use intensities reflect many young people's (dis)connections to queer life. Participant accounts of Tumblr use for identity, well-being, and (dis)connection practices can usefully inform health, education, and community workers engaging with LGBTIQ+ young people

Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision

Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining popularity in several computer vision algorithms, a significant energy consumption difference exists compared to traditional hand-crafted approaches. In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep Convolutional Neural Networks (CNN) and hand-crafted features such as Histogram of Oriented Gradients (HOG). This analysis is supported by measurements from two chips that implement these algorithms. Our goal is to understand the source of the energy discrepancy between the two approaches and to provide insight about the potential areas where CNNs can be improved and eventually approach the energy-efficiency of HOG while maintaining its outstanding performance accuracy

Lower limb vascular assessment techniques of podiatrists in the United Kingdom : a national survey

Background: Podiatric vascular assessment practices in the United Kingdom (UK) are currently unknown. This study aimed to describe the current practices for performing lower limb vascular assessments by podiatrists in the UK, and, to investigate the effect of practitioner characteristics, including education level and practice setting, on the choice of tests used for these assessments. Methods: A cross-sectional observational online survey of registered podiatrists in the UK was conducted using SurveyMonkey® between 1st of July and 5th of October 2018. Item content related to: practitioner characteristics, vascular testing methods, barriers to completing vascular assessment, interpretation of vascular assessment techniques, education provision and ongoing management and referral pathways. Descriptive statistics were performed, and multinomial logistic regression analyses were used to determine whether practitioner characteristics could predict the choice of vascular tests used. Results: Five hundred and eighty five participants accessed the online survey. After drop-outs and exclusions, 307 participants were included in the analyses. Comprehensive vascular assessments had most commonly been performed once (15.8%) or twice (10.4%) in the past week. The most common indicators for performing vascular assessment were symptoms of suspected claudication (89.3%), suspected rest pain (86.0%) and history of diabetes (85.3%). The most common barrier to performing vascular assessment was time constraints (52.4%). Doppler examination (72.3%) was the most frequently reported assessment type, with ankle-brachial index (31.9%) and toe brachial index (5.9%) less frequently performed. There were variable interpretations of vascular test results. The most common topic for education was smoking cessation (69.5%). Most participants (72.2%) were confident in determining ongoing management, with the majority referring to the patient's general practitioner (67.6%). Practitioner characteristics did not predict the types of vascular tests performed. Conclusion: The majority of vascular assessments currently performed by podiatrists in the UK are inconsistent with UK or international vascular guidelines and recommendations. Despite this, most podiatrists felt confident in diagnosing, referring and managing patients with peripheral arterial disease (PAD), however many felt they needed more education to feel confident to assist patients with PAD to manage their cardiovascular risk factors

Hardware for Machine Learning: Challenges and Opportunities

Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).United States. Defense Advanced Research Projects Agency (DARPA)Texas Instruments IncorporatedIntel Corporatio