Solar Disinfection of Turbid Hygiene Waters in Lexington, KY, USA

Solar disinfection (SODIS) could be a key to providing a clean, hygiene water for birthing uses, but the recommended climate zone is limited, the microbial indicators are related to gastrointestinal illness and not wound infections. SODIS feasibility was investigated to remove Escherichia coli from turbid water at temperatures less than 50 °C in Lexington, KY. Increasing turbidity from 0 to 200 NTU decreased E. coli inactivation from 5 to 1 log. With the same experimental protocol, more than 4-log inactivation of Staphylococcus aureus and Staphylococcus epidermidis (common human-skin microorganisms related to serious post-partum infections of both mother and child) was achieved at different turbidity levels with a maximum, in-bottle temperature of 49.2 °C after 5.5 h. The thermal inactivation of the bacterial indicators was assessed without UV radiation and turbidity in water at 37 and 47 °C. Skin bacteria were inactivated completely after 9.5 h at 47 °C, but only 58% removal happened for thermo-tolerant E. coli. These results suggest that SODIS application may be expanded geographically to treat water for hygiene purposes. However, as E. coli is also capable of causing wound infections, UV with thermal inactivation may be required to produce safe hygiene water by SODIS outside of recommended latitudes

AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators

An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated by testing on different network structures and applications, e.g., image reconstruction and classification tasks. The results showed an average of 60% improvement in convolutional neural network (CNN) performance on CIFAR10 dataset after 10000 inference operations as well as 78.6% error reduction in image reconstruction.Comment: This paper is submitted to IEEE Transactions Circuits and Systems II: Express Brief

Identifying Metal Resistance Genes in Staphylococcus Species Isolated from Wastewater and Streams Receiving Treated Effluent

Staphylococcus aureus and other staphylococci share mobile genetic elements that code for resistance to antibiotics and metals, allowing them to survive outside of their hosts in the environment, and become less sensitive to environmental factors and more resistant to antimicrobial agents. This study investigated the presence of select metal resistance genes (MRGs) originating from S. aureus and other staphylococci utilizing whole genomic sequencing and BlastN analysis of isolate genetic sequences obtained by selective enrichment of sewage-impacted samples over a 2-year period. Results from 26 isolate sequences recovered from 2 wastewater treatment plants, and creek sediments upstream and downstream, showed that MRGs carried in treatment plant influent S. aureus isolates were present in effluent Staphylococcus warneri isolates. The same MRGs were found in Enterococcus faecalis isolates recovered from the receiving creek sediments downstream of the plant effluent. The presence of MRGs specific to Staphylococcus delphini was identified in S. warneri, S. aureus, and E. faecalis isolates, indicative of horizontal gene transfer. This study has identified the potential role of coagulase-negative S. warneri in supporting the environmental spread of MRGs from coagulase-positive staphylococcus species present in sewage influent to bacteria in the receiving stream sediments, and highlights the need to control the spread of resistance genes of S. aureus by increasing treatment effectiveness for removing gram-positive bacteria and transference vectors

Modeling the effect of process variations on the delay and power of the digital circuit using fast simulators

Process variation has an increasingly dramatic effect on delay and power as process geometries shrink. Even if the amount of variation remains the same as in previous generations, it accounts for a greater percentage of process geometries as they get smaller. So an accurate prediction of path delay and power variability for real digital circuits in the current technologies is very important; however, its main drawback is the high runtime cost. In this paper, we present a new fast EDA tool which accelerates Monte Carlo based statistical static timing analysis (SSTA) for complex digital circuit. Parallel platforms like Message Passing Interface and POSIX® Threads and also the GPU-based CUDA platform suggests a natural fit for this analysis. So using these platforms, Monte Carlo based SSTA for complex digital circuits at 32, 45 and 65 nm has been performed. and of the pin-to-output delay and power distributions for all basic gates are extracted using a memory lookup from Hspice and then the results are extended to the complex digital circuit in a hierarchal manner on the parallel platforms. Results show that the GPU-based platform has the highest performance (speedup of 19�). The correctness of the Monte Carlo based SSTA implemented on a GPU has been verified by comparing its results with a CPU based implementation

In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives

International audienc

Memristive Devices for Neuromorphic and Deep Learning Applications

Neuromorphic and deep learning (DL) algorithms are important research areas gaining significant traction of late. Due to this growing interest and the high demand for low-power and high-performance designs for running these algorithms, various circuits and devices are being designed and investigated to realize efficient neuromorphic and DL architectures. One device said to drastically improve this architecture is the memristor. In this chapter, studies investigating memristive implementations into neuromorphic and DL designs are summarized and categorized based on the switching mechanicsms of a few prominent memristive device technologies. Furthermore, the simulation platforms used to model both neuromorphic and DL hardware implementations, which use memristors, are summarized and discussed. This chapter can provide a quick reference for readers interested in learning the latest advancements in the areas of memristive devices and systems for use in neuromorphic and DL systems

AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators

International audienc

Accurate charge transport model for nanoionic memristive devices

Abstract not availableAmirali Amirsoleimani, Jafar Shamsi, Majid Ahmadi, Arash Ahmadi, Shahpour Alirezaee, Karim Mohammadi, Mohammad Azim Karami, Chris Yakopcic, Omid Kavehei, Said Al-Saraw

Wastewater Surveillance for Identifying SARS-CoV-2 Infections in Long-Term Care Facilities, Kentucky, USA, 2021–2022

Persons living in long-term care facilities (LTCFs) were disproportionately affected by COVID-19. We used wastewater surveillance to detect SARS-CoV-2 infection in this setting by collecting and testing 24-hour composite wastewater samples 2–4 times weekly at 6 LTCFs in Kentucky, USA, during March 2021–February 2022. The LTCFs routinely tested staff and symptomatic and exposed residents for SARS-CoV-2 using rapid antigen tests. Of 780 wastewater samples analyzed, 22% (n = 173) had detectable SARS-CoV-2 RNA. The LTCFs reported 161 positive (of 16,905) SARS-CoV-2 clinical tests. The wastewater SARS-CoV-2 signal showed variable correlation with clinical test data; we observed the strongest correlations in the LTCFs with the most positive clinical tests (n = 45 and n = 58). Wastewater surveillance was 48% sensitive and 80% specific in identifying SARS-CoV-2 infections found on clinical testing, which was limited by frequency, coverage, and rapid antigen test performance

Functional Oxides for Photoneuromorphic Engineering : Toward a Solar Brain

New device concepts and new computing principles are needed to balance our ever-growing appetite for data and information with the realization of the goals of increased energy efficiency, reduction in CO emissions, and the circular economy. Neuromorphic or synaptic electronics is an emerging field of research aiming to overcome the current computer's Von-Neumann bottleneck by building artificial neuronal systems to mimic the extremely energy efficient biological synapses. The introduction of photovoltaic and/or photonic aspects into these neuromorphic architectures will produce self-powered adaptive electronics but may also open new possibilities in artificial neuroscience, artificial neural communications, sensing, and machine learning which would enable, in turn, a new era for computational systems owing to the possibility of attaining high bandwidths with much reduced power consumption. This perspective is focused on recent progress in the implementation of functional oxide thin-films into photovoltaic and neuromorphic applications toward the envisioned goal of self-powered photovoltaic neuromorphic systems or a solar brain