DSP-free and real-time NRZ transmission of 50Gb/s over 15km SSMF and 64Gb/s back-to-back with a 1.3um VCSEL

We demonstrate and analyze 50 Gb/s non-return-to-zero (NRZ) transmission over 15 km of standard single-mode fiber (SSMF), 60-Gb/s NRZ transmission over 5 km of SSMF and up to 64-Gb/s NRZ back-to-back using a directly modulated short-cavity long-wavelength single-mode vertical-cavity surface-emitting laser (VCSEL) emitting at 1326 nm. Owing to an analog 6-tap transmit feedforward equalizer, the link can operate without digital signal processing. In all three cases, real-time bit error ratio measurements below the 7% overhead hard-decision forward error correction threshold are demonstrated when transmitting a pseudorandom bit sequence with a period of 2(7) - 1 bits. In addition, we analyze the interplay between the residual fiber chromatic dispersion at the operating wavelength of the VCSEL and the chirp due to direct modulation. These results demonstrate how O-band, short-cavity long-wavelength single-mode VCSELs can be used in intradata center networks, as well as in interdata center networks at reaches below 15 km

Experimental Demonstration of 503.61-Gbit/s DMT over 10-km 7-Core Fiber with 1.5-\mu m SM-VCSEL for Optical Interconnects

We experimentally demonstrate a net-rate 503.61-Gbit/s discrete multitone (DMT) transmission over 10-km 7-core fiber with 1.5-\mu m single mode VCSEL, where low-complexity kernelrecursive-least-squares algorithm is employed for nonlinear channel equalization.Comment: 3 pages, 44th European Conference on Optical Communication (ECOC 2018), Rome, Italy, 201

726.7-Gb/s 1.5-µm single-mode VCSEL discrete multi-tone transmission over 2.5-km multicore fiber

A 107Gb/s net-rate DMT optical signal was generated using a single-mode longwavelength VCSEL with a modulation bandwidth of 23GHz. We experimentally demonstrated a total net-rate up to 726.7Gb/s at 1.5μm over 2.5km 7-core dispersion-uncompensated MCF.</p

Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand

Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations

Wind-driven ventilation improvement with plan typology alteration: a CFD case study of traditional Turkish architecture

Aligned with achieving the goal of net-zero buildings, the implementation of energy-saving techniques in minimizing energy demands is proving more vital than at any time. As practical and economic options, passive strategies in ventilation developed over thousands of years have shown great potential for the reduction of residential energy demands, which are often underestimated in modern building’s construction. In particular, as a cost-effective passive strategy, wind-driven ventilation via windows has huge potential in the enhancement of the indoor air quality (IAQ) of buildings while simultaneously reducing their cooling load. This study aims to investigate the functionality and applicability of a common historical Turkish architectural element called “Cumba” to improve the wind-driven ventilation in modern buildings. A case study building with an archetypal plan and parameters was defined as a result of a survey over 111 existing traditional samples across Turkey. Buildings with and without Cumba were compared in different scenarios by the development of a validated CFD microclimate model. The results of simulations clearly demonstrate that Cumba can enhance the room’s ventilation rate by more than two times while harvesting wind from different directions. It was also found that a flexible window opening strategy can help to increase the mean ventilation rate by 276%. Moreover, the room’s mean air velocity and ventilation rate could be adjusted to a broad range of values with the existence of Cumba. Thus, this study presents important findings about the importance of plan typology in the effectiveness of wind-driven ventilation strategies in modern dwellings

Selective Serotonin Reuptake Inhibitor (SSRI) Antidepressants in Pregnancy and Congenital Anomalies: Analysis of Linked Databases in Wales, Norway and Funen, Denmark

Background: Hypothesised associations between in utero exposure to selective serotonin reuptake inhibitors (SSRIs) and congenital anomalies, particularly congenital heart defects (CHD), remain controversial. We investigated the putative teratogenicity of SSRI prescription in the 91 days either side of first day of last menstrual period (LMP). Methods and Findings: Three population-based EUROCAT congenital anomaly registries- Norway (2004–2010), Wales (2000–2010) and Funen, Denmark (2000–2010)—were linked to the electronic healthcare databases holding prospectively collected prescription information for all pregnancies in the timeframes available. We included 519,117 deliveries, including foetuses terminated for congenital anomalies, with data covering pregnancy and the preceding quarter, including 462,641 with data covering pregnancy and one year either side. For SSRI exposures 91 days either side of LMP, separately and together, odds ratios with 95% confidence intervals (ORs, 95%CI) for all major anomalies were estimated. We also explored: pausing or discontinuing SSRIs preconception, confounding, high dose regimens, and, in Wales, diagnosis of depression. Results were combined in meta-analyses. SSRI prescription 91 days either side of LMP was associated with increased prevalence of severe congenital heart defects (CHD) (as defined by EUROCAT guide 1.3, 2005) (34/12,962 [0.26%] vs. 865/506,155 [0.17%] OR 1.50, 1.06–2.11), and the composite adverse outcome of 'anomaly or stillbirth' (473/12962, 3.65% vs. 15829/506,155, 3.13%, OR 1.13, 1.03–1.24). The increased prevalence of all major anomalies combined did not reach statistical significance (3.09% [400/12,962] vs. 2.67% [13,536/506,155] OR 1.09, 0.99–1.21). Adjusting for socio-economic status left ORs largely unchanged. The prevalence of anomalies and severe CHD was reduced when SSRI prescriptions were stopped or paused preconception, and increased when >1 prescription was recorded, but differences were not statistically significant. The dose-response relationship between severe CHD and SSRI dose (meta-regression OR 1.49, 1.12–1.97) was consistent with SSRI-exposure related risk. Analyses in Wales suggested no associations between anomalies and diagnosed depression. Conclusion: The additional absolute risk of teratogenesis associated with SSRIs, if causal, is small. However, the high prevalence of SSRI use augments its public health importance, justifying modifications to preconception care

A Classifier-based approach to identify genetic similarities between diseases

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease

100 Gbit/s serial transmission using a silicon-organic hybrid (SOH) modulator and a duobinary driver IC

100 Gbit/s three-level (50 Gbit/s 00K) signals are generated using a silicon-organic hybrid modulator and a BiCMOS duobinary driver IC at a BER of 8.5x10(-5)(<10(-12)). We demonstrate dispersion-compensated transmission over 5 km

100 Gbit/s serial transmission using a silicon-organic hybrid (SOH) modulator and a duobinary driver IC

100 Gbit/s three-level (50 Gbit/s 00K) signals are generated using a silicon-organic hybrid modulator and a BiCMOS duobinary driver IC at a BER of 8.5x10(-5)(<10(-12)). We demonstrate dispersion-compensated transmission over 5 km