953 research outputs found
Efficient Density Matrix Renormalization Group algorithm to study Y-Junctions with integer and half-integer spin
An efficient density matrix renormalization group (DMRG) algorithm is
presented and applied to Y-junctions, systems with three arms of sites that
meet at a central site. The accuracy is comparable to DMRG of chains. As in
chains, new sites are always bonded to the most recently added sites and the
superblock Hamiltonian contains only new or once renormalized operators.
Junctions of up to sites are studied with
antiferromagnetic (AF) Heisenberg exchange between nearest-neighbor spins
or electron transfer between nearest neighbors in half-filled Hubbard
models. Exchange or electron transfer is exclusively between sites in two
sublattices with . The ground state (GS) and spin densities at site are quite different for junctions with =
1/2, 1, 3/2 and 2. The GS has finite total spin for even (odd)
and for in the spin manifold, at sites
of the larger (smaller) sublattice. = 1/2 junctions have delocalized states
and decreasing spin densities with increasing . = 1 junctions have four
localized states at the end of each arm and centered on the
junction, consistent with localized states in = 1 chains with finite
Haldane gap. The GS of = 3/2 or 2 junctions of up to 500 spins is a spin
density wave (SDW) with increased amplitude at the ends of arms or near the
junction. Quantum fluctuations completely suppress AF order in = 1/2 or 1
junctions, as well as in half-filled Hubbard junctions, but reduce rather than
suppress AF order in = 3/2 or 2 junctions.Comment: 11 pages, 11 Figures and submitted to PR
Regulation of cytoplasmic polyadenylation can generate a bistable switch
<p>Abstract</p> <p>Background</p> <p>Translation efficiency of certain mRNAs can be regulated through a cytoplasmic polyadenylation process at the pre-initiation phase. A translational regulator controls the polyadenylation process and this regulation depends on its posttranslational modifications e.g., phosphorylation. The cytoplasmic polyadenylation binding protein (CPEB1) is one such translational regulator, which regulates the translation of some mRNAs by binding to the cytoplasmic polyadenylation element (CPE). The cytoplasmic polyadenylation process can be turned on or off by the phosphorylation or dephosphorylation state of CPEB1. A specific example could be the regulation of Calcium/Calmodulin-dependent protein kinase II (αCaMKII) translation through the phosphorylation/dephosphorylation cycle of CPEB1.</p> <p>Result</p> <p>Here, we show that CPEB1 mediated polyadenylation of αCaMKII mRNA can result in a bistable switching mechanism. The switch for regulating the polyadenylation is based on a two state model of αCaMKII and its interaction with CPEB1. Based on elementary biochemical kinetics a high dimensional system of non-linear ordinary differential equations can describe the dynamic characteristics of the polyadenylation loop. Here, we simplified this high-dimensional system into approximate lower dimension system that can provide the understanding of dynamics and fixed points of original system. These simplified equations can be used to develop analytical bifurcation diagrams without the use of complex numerical tracking algorithm, and can further give us intuition about the parameter dependence of bistability in this system.</p> <p>Conclusion</p> <p>This study provides a systematic method to simplify, approximate and analyze a translation/activation based positive feedback loop. This work shows how to extract low dimensional systems that can be used to obtain analytical solutions for the fixed points of the system and to describe the dynamics of the system. The methods used here have general applicability to the formulation and analysis of many molecular networks.</p
Can a local, PKMξ dependent translational switch account for the maintenance of synaptic plasticity?
Factors affecting intention to adopt Green Building Practices: A Journey towards meeting Sustainable Goals
Abstract
Purpose
The construction industry and its activities harmfully affect the environment. Hence, adopting green building (GRB) practices can be helpful in achieving sustainable development goals. Therefore, this study aims to identify the factors affecting the intention to adopt GRB practices by extending theory of planned behavior (TPB).
Design/methodology/approach
Using non-probability purposive sampling technique, data was gathered from consultant and contractor engineers in the construction industry through a questionnaire. The analysis was done using partial least square-structural equation modeling technique on a useful sample of 290.
Findings
Findings revealed that the core constructs of TPB [i.e. attitude (AT), subjective norms (SUBN) and perceived behavioral control (PBC)] significantly affect the intention to adopt GRB practices. Moreover, government support and knowledge of green practices (KNGP) were found to be critical influencing factors on AT, SUBNs and PBC. Lastly, the findings confirmed that environmental concerns (ENC) play as a moderating between SUBN and intention to adopt GRB practices, as well as AT and intention to adopt GRB practices.
Practical implications
This study contributes to existing knowledge on GRB, offering evidence base for policy choices regarding climate change adaptation and mitigation in the construction industry.
Originality/value
This study provides insights from the perspective of a developing economy and confirms the applicability of TPB in the adoption of GRB practices. Moreover, this study confirms the moderation role of ENC in between TPB constructs and intention to GRB that is not tested earlier in the context of GRB. This study also confirms that government sustainable support positively affects PBC, and KNGP significantly affects SUBNs
Evaluation of Records of Oral and Maxillofacial Surgery Cases Reported at Abbasi Shaheed Hospital and Karachi Medical and Dental College, Pakistan
Background: Oral and Maxillofacial Surgery department is a diverse field in dentistry. Record maintenance has been established as one of the key factors in the success and integrity of health care institutes.Objective: The objective of the study was to evaluate the records of oral and maxillofacial surgery casesreported to oral and maxillofacial surgery department, Abbasi Shaheed Hospital and oral surgery OPD ofKarachi Medical and Dental College.Methods: Cross sectional study was conducted in at ASH and KMDC from July 2019 to September 2019.The data from January 2017 to July 2019 was retrospectively noted through electronic surgical recordof ASH and records of the Oral Surgery OPD of KMDC. Inclusion criteria was patients records of bothgenders of 5–70 years age, having complaint of any oral or dental pathology or pathologies, trauma andimpactions. Data was calculated manually by calculating frequencies and percentages for the trauma,impaction and pathology cases of patients.Results: In 2017, 239 cases were treated under general anesthesia from which trauma 11. 45% (n=11),followed by 48. 11% (n=115) cases of oral pathologies, total 11.7% (n=28) cases of complicated exodontias. In2018, among 211, 51.1% (n=108) cases were trauma followed by 39.3% (n=83) cases of oral pathologies,whereas, total 9.4% (n=20) complicated exodontias cases were observed. During 2019 (January to July),168 cases 36.2% (n=62) cases were diagnosed as trauma, in oral pathology, overall 36.2% (n=62) caseswere surgically excised. Total 23.2% (n=39) complicated exodontias. In 2017, 25122 cases were reported in Surgery OPD of Karachi Medical and Dental College. Total 36.2% (n=9097) teeth were extracted from which 1.93% (n=486) cases were surgical impaction. On the other hand, 1.65% (n=416) patients were treated through minor oral surgeries. In 2018, 29008 cases were reported in Surgery OPD. Total 42.7% (n=12377) teeth were extracted from which 0.92% (n=268) cases were surgical impaction. On the other hand, 0.71% (n=208) patients were treated through minor surgeries. In 2019, January till July 13028 cases were reported in Surgery OPD. Total42.6% (n=5559) teeth were extracted from which 0.66% (n=87) cases were surgical impaction. On the other hand, 0.68% (n=89) patients were treated through minor surgeries.Conclusion: It has been concluded that evaluation of the records of oral and maxillofacial surgery casesreported to oral and maxillofacial surgery department, Abbasi Shaheed Hospital and oral surgery OPD ofKarachi Medical and Dental College were high and appropriate measures should be taken in order tomanage these problems timely and effectively
Paspalum notatum Grass-waste-based Adsorbent for Rhodamine B Removal from Polluted Water
The potential of Paspalum notatum grass waste to adsorb Rhodamine B dye from aqueous phase is reported in this research. The grass waste was activated and characterized through various techniques to analyze the chemical (FTIR), morphological (SEMEDX), and thermal (TGA) changes incorporated through the activation process. The pollutant removal efficiency of the raw and modified adsorbents was studied by varying different process parameters in a batch process. The maximum capacity of adsorption which was observed for grass waste and activated grass waste was 54 mg g–1 and 72.4 mg g–1 respectively. Among the various kinetic models, the pseudo-second order model gives the best regression results. However, the intraparticle diffusion-adsorption model showed that the diffusion within pores controlled the adsorption rate. Thermodynamic analysis of this process revealed that Rhodamine B adsorption was endothermic and spontaneous in nature. The results of this study show that grass waste has the potential to be used as an adsorbent for the treatment of colored water.
This work is licensed under a Creative Commons Attribution 4.0 International License
Energy-aware Theft Detection based on IoT Energy Consumption Data
With the advent of modern smart grid networks, advanced metering infrastructure provides real-time information from smart meters (SM) and sensors to energy companies and consumers. The smart grid is indeed a paradigm that is enabled by the Internet of Things (IoT) and in which the SM acts as an IoT device that collects and transmits data over the Internet to enable intelligent applications. However, IoT data communicated over the smart grid could however be maliciously altered, resulting in energy theft due to unbilled energy consumption. Machine learning (ML) techniques for energy theft detection (ETD) based on IoT data are promising but are nonetheless constrained by the poor quality of data and particularly its imbalanced nature (which emerges from the dominant representation of honest users and poor representation of the rare theft cases). Leading ML-based ETD methods employ synthetic data generation to balance the training the dataset. However, these are trained to maximise average correct detection instead of ETD. In this work, we formulate an energy-aware evaluation framework that guides the model training to maximise ETD and minimise the revenue loss due to mis-classification. We propose a convolution neural network with positive bias (CNN-B) and another with focal loss CNN (CNN-FL) to mitigate the data imbalance impact. These outperform the state of the art and the CNN-B achieves the highest ETD and the minimum revenue loss with a loss reduction of 30.4% compared to the highest loss incurred by these methods
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