Measurement Models For Sailboats Price vs. Features And Regional Areas

In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.Comment: 20 pages, 17 figure

Double-edged sword of technological progress to climate change depends on positioning in global value chains

Technological progress (TP) is a double-edged sword to global climate change. This study for the first time reveals rebound and mitigation effects of efficiency-related TP in global value chains (GVCs) on greenhouse gas (GHG) emissions. The integrated effects of TP depend on the positioning of sectors in GVCs. The cost-saving TP in upstream sectors would stimulate downstream demand. This produces stronger rebound effects than mitigation potentials and leads to global GHG emission increments (e.g. TP in the gas sector of China and petroleum and coal products sector of South Korea). In contrast, sectors located in the trailing end of GVCs have greater potentials for GHG emission mitigation through TP, mainly due to the reduction of upstream inputs. (e.g. the construction sector of China and dwelling sector of the United States). Global GHG emissions and production outputs can be either a trade-off or a win-win relationship on account of TP than rebound effects, because TP in different sectors could possibly increase or decrease the emission intensity of GVCs. This study could recognize the most productive spots for GHG emission mitigation through efficiency-related TP. It provides a new perspective for international cooperation to promote global GHG emission mitigation

DNA-based doping and fabrication of PN diodes

This paper reports the fabrication of silicon PN diode by using DNA nanostructure as the etching template for SiO2 and also as the n-dopant of Si. DNA nanotubes were deposited onto p-type silicon wafer that has a thermal SiO2 layer. The DNA nanotubes catalyze the etching of SiO2 by HF vapor to expose the underlying Si. The phosphate groups in the DNA nanotube were used as the doping source to locally n-dope the Si wafer to form vertical P-N junctions. Prototype PN diodes were fabricated and exhibited expected blockage behavior with a knee voltage of ca. 0.7 V. Our work highlights the potential of DNA nanotechnology in future fabrication of nanoelectronics

Single-site catalyst promoters accelerate metal- catalyzed nitroarene hydrogenation

Atomically dispersed supported metal catalysts are drawing wide attention because of the opportunities they offer for new catalytic properties combined with efficient use of the metals. We extend this class of materials to catalysts that incorporate atomically dispersed metal atoms as promoters. The catalysts are used for the challenging nitroarene hydro- genation and found to have both high activity and selectivity. The promoters are single-site Sn on TiO2 supports that incorporate metal nanoparticle catalysts. Represented as M/Sn- TiO2 (M = Au, Ru, Pt, Ni), these catalysts decidedly outperform the unpromoted supported metals, even for hydrogenation of nitroarenes substituted with various reducible groups. The high activity and selectivity of these catalysts result from the creation of oxygen vacancies on the TiO2 surface by single-site Sn, which leads to efficient, selective activation of the nitro group coupled with a reaction involving hydrogen atoms activated on metal nanoparticles

Metagenomic Profiling of the Bacterial Community Changes from Koji to Mash Stage in the Brewing of Soy Sauce

The improvement of soy sauce fermentation is restricted by the insufficient information on bacterial community. In this study, bacterial communities in the koji and mash stage were compared based on next-generation sequencing technology. A total of 29 genera were identi­fied in the koji stage, while 34 in the mash stage. After koji stage, 7 genera disappeared and 12 new genera appeared in the mash stage. The dominant bacteria were Kurthia, Weissella and Staphylococcus in the koji stage and Staphylococcus, Kurthia, Enterococcus and Leuconostoc in the mash stage. The results provided insights into the microbial communities involved in soy sauce fermentation

Advances in Molecular Quantum Chemistry Contained in the Q-Chem 4 Program Package

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design