An investigation of auctions in the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters

An investigation of auctions in the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters

Mining for nonribosomal peptide synthetase and polyketide synthase genes revealed a high level of diversity in the sphagnum bog metagenome

Sphagnum bog ecosystems are one of the oldest vegetation forms harbouring a specific microbial community, which is known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome indicate a rich secondary metabolism, the genes are not yet explored. To analyse non-ribosomal peptide synthetases (NRPS) and polyketide synthases (PKS) the diversity of NRPS and PKS genes in Sphagnum-associated metagenome was investigated by in silico data mining and sequence-based screening (PCR-amplification of 9500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPS, 346 PKS, as well as 40 PKS-NRPS hybrid gene sequences. Occurrence of NRPS sequences was strongly dominated by the phyla Protebacteria, especially by the genus Burkholderia, while PKS sequences were mainly affiliated to Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR-amplification screening, displaying amino acid sequence identities of 48 to 91% to annotated sequences of the phyla Proteobacteria, Actinobacteria and Cyanobacteria. Some of the identified metagenomic clones showed closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of yet undescribed bioactive metabolites. This study highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications

Analisis Unit Cost Pelayanan Rawat Inap Postpartum Di Rumah Sakit Umum Dewi Sartika Dengan Menggunakan Metode Activity Based Costing (ABC) System

Iklim kompetitif tidak hanya terjadi pada Perusahaan yang berorientasi profit, namun juga berdampak padaperusahaan yang berorientasi nonprofit, salah satunya adalah rumah sakit. Rumah sakit yang berada di SulawesiTenggara pada tahun 2014 berjumlah 36, sedangkan kota Kendari memiliki 13 rumah sakit dan harus melayani347.496 penduduk. Hal ini menjadikan persaingan bisnis rumah sakit baik umum maupun swasta kini semakinketat. Salah satu solusi untuk memenangkan persaingan adalah dengan cara menentukan tarif yang lebih rendahdan kualitas atau jasa yang lebih tinggi dari pada pesaing. Solusi tersebut tentunya menjadikan biaya operasionalrumah sakit akan semakin besar sehingga sistem akuntansi manajemen di rumah sakit harus efektif dan efisiensehingga menghasilkan informasi yang akurat dalam pengambilan keputusan. Rumah sakit cenderung masihmenggunakan system akuntansi tradisional yang memiliki distorsi biaya. Penelitian ini menganalisis perhitungantarif rawat inap pelayanan postpartum di RSU Dewi Sartika menggunakan metode activity based costing system.Penelitian ini menggunakan metode deskriptif komparatif dengan pendekatan kuantitatif, data yang digunakanadalah seluruh data keuangan pada Oktober 2015-September 2016 dan aktivitas yang diobservasi yaitu ruangperawatan kelas VIP, kelas I, kelas II, kelas III dan bangsal. Hasil perhitungan menunjukkan hasil yang berbeda daritarif yang diterapkan. Tarif kelas VIP, kelas I dan kelas III lebih rendah dari tarif rumah sakit sedangkan tarif kelas IIdan bangsal lebih tinggi

An investigation of auctions in the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters

High-Resolution Color Transparent Display Using Superimposed Quantum Dots

In this paper, a high-resolution full-color transparent monitor is designed and fabricated using the synthesized quantum dots for the first time. For this purpose, about 100 compounds that had the potential to emit blue, green, and red lights were selected, and simulation was performed using the discrete dipole approximation (DDA) method, in which the shell layer was selected to be SiO2 or TiO2 in the first step. Among the simulated compounds with SiO2 or TiO2 shells, Se/SiO2 and BTiO3/SiO2 were selected as blue light emitters with high intensity and narrow bandwidth. Accordingly, CdSe/SiO2 nanoparticles were selected as green light emitters and Au/TiO2 for the red light. As the surface of the nanoparticles in their optical properties is important, reactivation of the nanoparticles’ surface is required to reach the high-intensity peak and resolution. To this end, in the second step, the surface of Se and CdSe nanoparticles reacted with ethanolamine, which can make a strong bond with cadmium atoms. The band structure and optical properties were obtained by the density functional theory (DFT) method. The Se/Ethanolamine and CdSe/Ethanolamine were experimentally synthesized to evaluate the theoretical results, and their optical properties were measured. To fabricate a transparent monitor, Se/Ethanolamine, CdSe/SiO2, and Au/TiO2 nanoparticles were dispersed in polyvinyl alcohol (PVA) solved in water and deposited on the glass by the doctor blading technique. Finally, high-resolution videos and images were displayed on the fabricated monitor

Modeling and determining the best combination of nitrogen and irrigation levels for achieving high yield in sweet corn

In order to model the combination of nitrogen application and irrigation level on corn yield, a three-years experiment (2018, 2019, and 2020) was perfumed based on a split-plot design. Four levels of Nitrogen fertilizer (0, 75, 125, 175, and 225 kg ha−1) and three levels of irrigation (100%, 80%, and 60% FC) were applied in all three years. Different methods and models were tested to simulate the relationship between yield and nitrogen along with irrigation and the final results indicated that interaction term between irrigation regimes and nitrogen fertilizer levels is the most influential source on kernel yield of sweet corn. This is the first time considering a model that includes this interaction term in modeling the kernel yield of sweet corn. In addition, our results showed that a complete polynomial model would complex the explanation of the model and it can be replaced with an adjusted model in which irrigation, the square of nitrogen levels, and their interaction are the only sources with no significant loss of goodness of fit. The negative coefficient of squared nitrogen treatment indicated that under a lower irrigation level (around 60% FC) higher nitrogen fertilizer level than about 180 might lead to slightly decrease in the kernel yield. Overall, providing water of about 90% to 100% FC and a nitrogen level of about 180 kg ha−1 is recommended to reach a high kernel yield and higher economic efficiency.12 month embargo; published online: 28 April 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Data from: Mining for NRPS and PKS genes revealed a high diversity in the Sphagnum bog metagenome

Sphagnum bog ecosystems are among the oldest vegetation forms harboring a specific microbial community and are known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome shows a rich secondary metabolism, the genes have not yet been explored. To analyze nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs), the diversity of NRPS and PKS genes in Sphagnum-associated metagenomes was investigated by in silico data mining and sequence-based screening (PCR amplification of 9,500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPSs and 346 PKSs, as well as 40 PKS-NRPS hybrid gene sequences. The occurrence of NRPS sequences was strongly dominated by the members of the Protebacteria phylum, especially by species of the Burkholderia genus, while PKS sequences were mainly affiliated with Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR amplification screening, displaying amino acid identities of 48% to 91% to annotated sequences of members of the phyla Proteobacteria, Actinobacteria, and Cyanobacteria. Some of the identified metagenomic clones showed the closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of as-yet-undescribed bioactive metabolites. This report highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications