Change in Regulation is Necessary for Genetically Engineered Mosquitoes

Millions of genetically engineered (GE) mosquitoes could soon be released in Key West, Florida as an effort to eradicate wild mosquitoes that are transmitters of diseases such as malaria, dengue, and chikungunya. Both international and domestic regulations fail to provide effective regulatory schemes that can facilitate the application of this technology while ensuring all safety and environmental aspects are properly addressed. The Food and Drug Administration’s assertion of jurisdiction is based on its assessment that the GE mosquitoes are “animal drugs” under the Federal Food, Drug, and Cosmetic Act. This is especially troublesome because the end goal of using these mosquitoes is to prevent diseases in humans, which are not “animals” under the statute. Also, the current scheme only regulates the engineered gene inside the mosquito, but not the mosquito itself, and fails to account for the fact that the mosquito is a living animal that acts separately and independently from the engineered gene inside. Moreover, the U.S. Department of Agriculture’s voluntary abrogation of jurisdiction is questionable because it had asserted jurisdiction on other GE insects and accumulated extensive experience in dealing with such issues. Instead, Mexico’s approach of establishing a separate federal-level regulatory body specifically for genetically modified organisms could be instructive. No matter what the solution, some change in regulation addressing GE mosquitoes has become even more urgent with the recent spread of Zika virus in the U.S

Does IT Lead to Urban Agglomeration of Jobs?

Cities have been an important part of the modern economic system where a massive number of people work together to produce diverse goods and services. Some predicted that information technology (IT) would fundamentally change the spatial organization of jobs by eliminating the effects of physical distance. As a result, the “IT revolution” was expected to decrease the advantages of cities. However, cities have prospered despite the rapid technological changes over the past two decades. This study investigates the effect of IT on urban agglomeration from a coordination perspective. Specifically, we examine how IT affects workers’ locations by changing the nature of their jobs and how this effect is moderated by occupational characteristics. We empirically validate our arguments by using occupational-level data from US. This study contributes to the literature on agglomeration economies and routine-biased technical changes by demonstrating that IT facilitates urban agglomeration of jobs, especially for high-skilled workers

Does IT Increase Specialization? An Empirical Analysis

Specialization - the degree of narrowness of task content - has been a foundation for workers\u27 productivity and the prosperity of modern society. Despite the abundance of research on the antecedents to specialization, there has been no research on whether and how IT affects workers’ specialization. The impact of IT on workers’ specialization is not clear since specialization is complexly mingled with occupational roles determined by the structure of organizations. This study examines the impact of IT on workers’ specialization based on organization science and economics literature and panel data from US Bureau of Labor and Statistics. This study contributes to the literature by providing a measure of specialization and clarifying the role of IT in the specialization of workers across different occupations

Novel CdS Hole-Blocking Layer for Photostable Perovskite Solar Cells

Currently, the stability issue of the perovskite solar cells (PSCs) is one of the most critical obstacles in the commercialization of PSCs. Although incredible advances in the photovoltaic efficiencies of PSCs have been achieved in the past few years, research on the stability of PSCs has been relatively less explored. In this study, a new kind of CdS hole-blocking layer replacing the traditional compact TiO₂ layer is developed to improve the photostability of PSCs because the intrinsic oxygen vacancies of the TiO₂ surface are suspected to be the main cause for the photoinduced degradation of PSCs. As a result, PSCs with the CdS layer exhibit considerably improved photostability, maintaining over 90% of the initial efficiency after continuous sunlight illumination for 12 h, while the TiO₂ PSC retains only 18% of the initial efficiency under the same conditions. Charge-transfer characteristics related to photodegradation are investigated by various analyses including electrochemical impedance spectroscopy and open-circuit voltage decay and time-resolved photoluminescence decay measurements. the CdS PSC exhibits negligible degradation in the charge-carrier dynamics, while the TiO₂ PSC suffers from severely damaged characteristics like increased charge recombination rate, charge-transfer resistance, and reduced charge extraction rate

Core/Shell Structured TiO₂/CdS Electrode to Enhance the Light Stability of Perovskite Solar Cells

In this work, enhanced light stability of perovskite solar cell (PSC) achieved by the introduction of a core/shell-structured CdS/TiO₂ electrode and the related mechanism are reported. By a simple solution-based process (SILAR), a uniform CdS shell was coated onto the surface of a TiO₂ layer, suppressing the activation of intrinsic trap sites originating from the oxygen vacancies of the TiO₂ layer. As a result, the proposed CdS-PSC exhibited highly improved light stability, maintaining nearly 80% of the initial efficiency after 12 h of full sunlight illumination. From the X-ray diffraction analyses, it is suggested that the degradation of the efficiency of PSC during illumination occurs regardless of the decomposition of the perovskite absorber. Considering the light-soaking profiles of the encapsulated cells and the OCVD characteristics, it is likely that the CdS shell had efficiently suppressed the undesirable electron kinetics, such as trapping at the surface defects of the TiO₂ and preventing the resultant charge losses by recombination. This study suggests that further complementary research on various effective methods for passivation of the TiO₂ layer would be highly meaningful, leading to insight into the fabrication of PSCs stable to UV-light for a long time

Quality Assessment Method Based on a Spectrometer in Laser Beam Welding Process

For the automation of a laser beam welding (LBW) process, the weld quality must be monitored without destructive testing, and the quality must be assessed. A deep neural network (DNN)-based quality assessment method in spectrometry-based LBW is presented in this study. A spectrometer with a response range of 225–975 nm is designed and fabricated to measure and analyze the light reflected from the welding area in the LBW process. The weld quality is classified through welding experiments, and the spectral data are thus analyzed using the spectrometer, according to the welding conditions and weld quality classes. The measured data are converted to RGB (red, green, blue) values to obtain standardized and simplified spectral data. The weld quality prediction model is designed based on DNN, and the DNN model is trained using the experimental data. It is seen that the developed model has a weld-quality prediction accuracy of approximately 90%

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints

Prediction of Resistance Spot Weld Quality of 780 MPa Grade Steel Using Adaptive Resonance Theory Artificial Neural Networks

In this study, the weld quality of 780 MPa grade dual phase (DP) steel with 1.0 mm thickness was predicted using adaptive resonance theory (ART) artificial neural networks. The welding voltage and current signals measured during resistance spot welding (RSW) were used as the input layer data, and the tensile shear strength, nugget size, and fracture shape of the weld were used as the output layer data. The learning was performed by the ART artificial neural networks using the input layer and output layer data, and the patterns of learning result were classified by the setting of vigilance parameter, ρ. When the vigilance parameter is 0.8, the best-predicted results were obtained for the tensile shear strength, nugget size, and fracture shape of welds