Miracle-dz/UniFAR-FTUs huggingface dataset:
Zinc Induced Alterations in the Photosystem II Mediated Photochemistry of Cyanobacterium Spirulina Platensis

In this investigation an attempt has been made to identify and correlate the polarographic measurements with the observations of fluorescence kinetics to identify the target site present in photosystem (PSII) II catalyzed electron transport in the cyanobacterium Spirulina platensis. Oxygen exchange measurements using Clark type oxygen electrode have showed 35% loss in whole chain activity and 45% loss in PSII activity at 120 μM concentration of zinc sulphate. Further to identify the specific targets sites within the PSII photochemistry, the reaction mixture component water was replaced with hydroxylamine and the results indicated that there could be damage at water oxidation complex. PAM Chl a fluorescence kinetic measurements revealed the decrease in variable fluorescence and increase in Initial fluorescence indicating that light harvesting complex of PSII could be the target for zinc stress at 120 μM. Thus loss of manganese in water oxidation complex and alterations in light harvesting complex of PSII are responsible for the altered PSII photochemistry in the above cyanobacterium under zinc stress.
ENHANCING CONCRETE PROPERTIES USING NANO MATERIALS

As most sciences, material science was obviously affected by Nanotechnology and its promising applications. Many researchers had investigated the improvement in concrete mechanical and durability properties induced by incorporation of nano-materials. This paper reviews the effect of various nanomaterials on mechanical, durability and microstructure characteristics of concrete. It highlights the current and potential applications of nano-materials in concrete. This will contribute to existed knowledge about nano-modified concrete and pave the way for wider acceptance and implementation in public infrastructures.
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

We introduce a Large Scale Multi-Illuminant (LSMI) Dataset that contains 7,486 images, captured with three different cameras on more than 2,700 scenes with two or three illuminants. For each image in the dataset, the new dataset provides not only the pixel-wise ground truth illumination but also the chromaticity of each illuminant in the scene and the mixture ratio of illuminants per pixel. Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination. Therefore, our dataset captures natural composition in the real-world setting with wide field-of-view, providing more extensive dataset compared to existing datasets for multi-illumination white balance. As conventional single illuminant white balance algorithms cannot be directly applied, we also apply per-pixel DNN-based white balance algorithm and show its effectiveness against using patch-wise white balancing. We validate the benefits of our dataset through extensive analysis including a user-study, and expect the dataset to make meaningful contribution for future work in white balancing.
Reproductive aspects of the female jaguar (Panthera onca L.)

Objective: To describe the reproductive characteristics of female jaguars and assisted reproduction techniques. Methodology: A literature review about reproductive characteristics and assisted reproduction techniques was done to document information on this subject. Results: Jaguars are the largest feline native to America; females sizes range from 1.57 to 2.19 m in length and weigh 45 to 82 kg. Jaguar females are ready for mating at about 2 years old and have an estrous cycle every 37 days. The reproductive behaviour patterns of the jaguar in captivity are grouped into four categories: 1) sexual, 2) affiliative, 3) agonists and 4) individual. The gestation period lasts between 91 to 111 days, and usually give birth to one to four young. For assisted reproduction, artificial insemination contributes to the genetic distribution or variability of germplasm. Limitations on study: The destruction of forests and jungles, habitat fragmentation, indiscriminate hunting, the lack of prey and food sources and the conflict with ranchers put Jaguars on the brink of extinction. Also, there is few information about their reproductive characteristics, which is why in Mexico, it is imperative to generate this information, mainly for free-living jaguars. Conclusions: The reproductive characteristics and assisted reproductive techniques in the female jaguar were documented. However, work is needed on assisted reproduction techniques in this species, which will contribute to the conservation of oocytes or embryos. It is advisable to design protocols to control and induce ovulation in the female Panthera onca.
Peer-to-Peer Multienergy and Communication Resource Trading for Interconnected Microgrids Microgrids

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.
Comparison of Adaptive and Robust Controllers for Fully-Constrained and Redundant Planar Cable Robots

This work deals with the design and comparison of two adaptive position control schemes with a classical PID controller for fully constrained and redundant planar robots. First, a novel method based on inclusion of virtual cables facilitates the linear separation of the uncertain parameters from the input-output signals. Then, two Lyapunov based adaptive controllers based on the sliding mode and PD schemes are designed to compensate for the structure matrix uncertainties, which result from errors in the anchor point locations. Finally, the adaptive controllers are evaluated and compared with a classical PID controller through simulations for a desired 2D singularity-free pose of the mobile platform. The simulation results have shown that the adaptive PD control scheme has the best performance for both fully constrained and redundant cases.Copyright © 2014 by ASME
Collateral Reallocation in Commercial Real Estate in the Shadow of COVID-19∗

We examine collateral reallocation in commercial real estate markets in the shadow of COVID-19. Office, retail and hotel property types located in dense urban centers have been particularly impacted. We show that use-type redeployment—redeveloping assets from one use to another—occurs frequently in property markets, and that debt slows the reallocation process. We argue that things are somewhat different this time, in the sense that forbearance in response to loan default is not necessarily an optimal policy response. Rather, certain use-types in certain locations may merit an activist lender response to facilitate capital reallocation. A model is developed to distinguish between policy-induced delay and delay due to market uncertainty. Several bank regulatory policy implications are highlighted.
Exploring Thai EFL University Students’ Growth Language Mindsets: The Beliefs About the Role of Talent and Effort

This research aims to explore language mindsets of Thai English as a Foreign Language (EFL) learners and to examine whether there is a significant difference in terms of age and year of study. Language Mindset Inventory (LMI) developed by Lou and Noel (2017) which consists of 18 five-Likert scale items divided into three categories i.e., general language intelligence beliefs (GLB), second language aptitude beliefs (L2B), and age sensitivity beliefs (ASB), was used to collect the data. Participants are 160 Thai EFL students from various year of study and universities who were enrolling in general English courses. The results show that Thai EFL university students have mixed language mindset (M = 3.09, SD = .51) of all three categories, namely GLB (M = 3.20, SD = .54), L2B (M = 2.94, SD = .43), and ASB (M = 3.14, SD = .53). One-way ANOVA results show that there is no significant difference between language mindset and age, as well as year of study. That means, Thai EFL university students’ generally have various beliefs about the role of talent and effort in their language learning abilities. Giving the benefits of growth mindset in long-term language learning, it is important to develop a learning approach that promotes growth mindset in English language learning.
Commercial Building Loads Providing Ancillary Services in PJM

The adoption of low carbon energy technologies such as variable renewable energy and electric vehicles, coupled with the efficacy of energy efficiency to reduce traditional base load has increased the uncertainty inherent in the net load shape. Handling this variability with slower, traditional resources leads to inefficient system dispatch, and in some cases may compromise reliability. Grid operators are looking to future energy technologies, such as automated demand response (DR), to provide capacity-based reliability services as the need for these services increase. While DR resources are expected to have the flexibility characteristics operators are looking for, demonstrations are necessary to build confidence in their capabilities. Additionally, building owners are uncertain of the monetary value and operational burden of providing these services. To address this, the present study demonstrates the ability of demand response resources providing two ancillaryservices in the PJM territory, synchronous reserve and regulation, using an OpenADR 2.0b signaling architecture. The loads under control include HVAC and lighting at a big box retail store and variable frequency fan loads. The study examines performance characteristics of the resource: the speed of response, communications latencies in the architecture, and accuracy of response. It also examines the frequency and duration of events and the value in the marketplace which can be used to examine if the opportunity is sufficient to entice building owners to participate.
Deep learning for the partially linear Cox model

While deep learning approaches to survival data have demonstrated empirical success in applications, most of these methods are difﬁcult to interpret and mathematical understanding of them is lacking. This paper studies the partially linear Cox model, where the nonlinear component of the model is implemented using a deep neural network. The proposed approach is ﬂexi-ble and able to circumvent the curse of dimensionality, yet it facilitates interpretability of the effects of treatment covariates on survival. We establish asymptotic theories of maximum partial likelihood estimators and show that our nonparametric deep neural network estimator achieves the minimax optimal rate of convergence (up to a poly-logarithmic factor). Moreover, we prove that the corresponding ﬁnite-dimensional estimator for treatment co-variate effects is √ n -consistent, asymptotically normal, and attains semi-parametric efﬁciency. Extensive simulation studies and analyses of two real survival datasets show the proposed estimator produces conﬁdence intervals with superior coverage as well as survival time predictions with superior concordance to actual survival times.
SHEAR BEHAVIOUR OF CAST-IN-PLACE ANCHORS AT LOW AND HIGH STRAIN RATES

The use of anchorage to concrete systems in construction is on the rise. Moreover, the anchorage to concrete systems are subjected to various loading conditions; including static, dynamic and shock. However, limited information is available on the behaviour of anchorage to concrete systems under impact and high strain rate loading. This paper presents the shear behaviour of cast-in-place anchorage to concrete systems using finite element analysis. Cast-in-place anchors were embedded in concrete with compressive strengths of 20 MPa, 30 MPa and 40 MPa were investigated at static (10-5 s-1) and high strain rate (103 s-1). The anchorage systems used two anchor diameters (12.7 mm and 19.1 mm) and two embedment depths (76.2 mm and 152.4 mm). The analyses results show that the ultimate shear load increased with the strain rate for the same concrete compressive strength. The increase in compressive strength led to increase in the ultimate shear load for the anchors tested when pryout failure mode was the dominant failure mode. A maximum ultimate shear load increase of 40.8% was achieved for concrete compressive strength increase from 20 MPa to 40 MPa at strain rate of 10-5 s-1. At high strain rate of 103 s1 maximum increase in the ultimate shear load of 2.7% was obtained for concrete compressive strength increase from 20 MPa to 40 MPa. Increase in the anchor diameter and embedment depth was also observed to increase the ultimate shear load. As well, the anchorage to concrete system failure mode was influenced by increase in strain rate while the increase in concrete compressive strength from 20 MPa to 40 MPa had no effect on the failure mode at high strain rate where steel failure is observed.
INTEGRATED MANAGEMENT OF MAINTENANCE IN THE METROPOLITAN EXPRESSWAY NETWORK

道路構造物のストックの増大と急激な老齢化に対し，計画的で効率的な維持管理をいかにして実践するかが道路管理者にとって重要な課題となっている．首都圏の大動脈として1日に平均115万台の利用がある首都高速道路においても，当初開通した区間は40年以上を経過し，維持管理の重要性が増す中で，同時に維持管理の更なる効率化が必要となってきている．本稿ではまず，首都高速道路ネットワークにおける舗装，塗装，橋梁の過去5年間の点検データを用いて劣化状態を定量評価するとともに，それらの個別事業において補修の優先順位付けを行う個別マネジメントシステムを構築した．そして，それらの結果を用いて維持管理目標の達成度評価，次年度の補修計画の作成，劣化予測に基づいた中期計画作成を行うための統合マネジメントについて提案を行った．
VAIR: A Novel Visualization System for Article Influence Ranking based on Citation Context

Article influence ranking is an effective way to reduce information redundancy and improve the efficiency of article retrieval. A large number of ranking models for network items have been employed for the ranking of article influence, such as PageRank and Spamming-resistant Expertise Analysis and Ranking. However, the effectiveness of article influence ranking based on the models of PageRank and SPEAR declines with the rapid growth of academic datasets, because of the increasing complexity of citation network. In order to take a rich set of contextual structures of citation context into consideration, we propose a visualization system VAIR for the citation context-based article influence ranking. Firstly, the word2vec model, a renowned technique in the field of natural language processing, is applied to transform articles into vectorized representations according to citation context. Then, a novel citation context-based article influence ranking model is designed according to the complex relationships quantified in a semantic vectorised space. Several visual designs are implemented, allowing users to perceive and compare the ranking results visually and intuitively. A set of user-friendly interactions are provided in the visualization framework, enabling users to explore the desirable article influence and obtain deep insights into the ranking model. Moreover, a series of case studies and comparison experiments are carried out based on real-world datasets, which further demonstrate the effectiveness of our algorithm for article influence ranking. INDEX TERMS influence ranking, visualization, visual analysis, citation network, word2vec
Effect of lyophilizate collapse on the stability of protein biopharmaceuticals

6 ACKNOWLEDGMENTS 7 GLOSSARY 8 1.0 INTRODUCTION 12 2.0 PRINCIPLES OF PROTEIN LYOPHILISATION 14 2.1 Freezing 17 2.1. 1 Mechanics of Product Freezing 18 2.1. 2 Nucleation 19 2.1. 3 Supercooling 20 2.1. 4 Frozen Matrix 21 2.1. 5 Ice Propagation 21 2.1. 6 Interstitial Region 22 2.1. 7 Sub ambient Glass Transition Temperature (Tg’) 22 2.1. 8 Annealing 22 2.2 Primary Drying 23 2.2.1 Phase Transitions of Water 24 2.2.2 Collapse 25 2.2.3 Collapse Temperature (Tc) 28 2.2.4 Meltback 29 2.2. 5 Eutectic Melting Temperature (Tm or Teut) 30 2.2. 6 Endpoint of Primary Drying 30 2.2. 7 Primary Drying Step Design 32 2.2. 8 Heat & Mass Transfer – Mathematical Modelling of Primary Drying . 33 2.3 Secondary Drying 38 2.3.1 Endpoint of Secondary Drying 39 2.3. 2 Secondary Drying Cycle Step design 41 2.4 Lyophilisation above the Collapse Temperature 41 3.0 PROTEIN STABILITY 43 3.1 Reactions resulting in Chemical Instability 43 3.1. 1 Deamidation 43 3.1. 2 Proteolysis 43
Using Natural Language to Predict Bias and Factuality in Media with a Study on Rationalization

Fake news is a widespread problem due to the ease of information spread online, and its ability to deceive large populations with intentionally false information. The damage it causes is exacerbated by its political links and loaded language, which make it polarizing in nature, and preys on peoples’ psychological biases to make it more believable and viral. In order to dampen the influence of fake news, organizations have begun to manually tag, or develop systems to automatically tag, false and biased information. However, manual efforts struggle to keep up with the rate at which content is published, and automated methods provide very little explanation to convince people of their validity. In an effort to address these issues, we present a system to classify media sources’ political bias and factuality levels by analyzing the language that gives fake news its contagious and damaging power. Additionally, we survey potential approaches for increasing the transparency of black-box fake news detection methods. Thesis Supervisor: James Glass Title: Senior Research Scientist
Identi cation of Benzothiazole‒Rhodanine Derivatives as α-Amylase and α-Glucosidase Inhibitors: Design, Synthesis, In-silico and In-Vitro Analysis

Revanasiddappa B C (  revan@nitte.edu.in ) Nitte Gulabi Shetty Memorial Institute of Pharmaceutical Sciences Mahendra Gowdru Srinivasa Nitte Gulabi Shetty Memorial Institute of Pharmaceutical Sciences Natasha Naval Aggarwal Nitte Gulabi Shetty Memorial Institute of Pharmaceutical Sciences Banylla Felicity Dkhar Gatphoh Nitte Gulabi Shetty Memorial Institute of Pharmaceutical Sciences Madan Kumar S University of Mysore Kannika B.R. University of Mysore Pujar G.V. JSS College of Pharmacy Mysore Sheshagiri Dixit JSS College of Pharmacy Mysore Shubhankar Mondal JSS College of Pharmacy Mysore Prashantha Kumar B.R. JSS College of Pharmacy Mysore Pukar Khanal KLE University's College of Pharmacy: KLE Society's College of Pharmacy
A critical review on nanoparticle-assisted enhanced oil recovery: Introducing scaling approach

* Corresponding Author Email: ezeddin.shirif@uregina.ca How to cite this article Rahman A. , Shirif E. , Torabi F. , Rahimbakhsh A. A critical review on nanoparticle-assisted enhanced oil recovery: Introducing scaling approach. Int. J. Nano Dimens. , 2022; 13(1): 1-30. Abstract Nanotechnology has the capability to modernize both the upstream and downstream oil and gas industry. It has been effectively used in exploration, drilling, production, refinery as well as in enhanced oil recovery (EOR) fields. Understanding the basics of scaling criteria development along with nanoparticle stabilized EOR mechanism will assist petroleum engineers in designing, analyzing, and evaluating nanoparticleassisted EOR techniques. This paper aims to deliver a critical review on nanoparticle-assisted EOR methods along with introducing scaling approaches and their applications in EOR. Scaling criteria can be employed to assess the performance of a specific EOR technique so that it can be accurately applied to the field scale. In this study, scaling criteria or dimensionless approaches are briefly summarized along with their applications in EOR. In addition, it reviews how scaling criteria can be derived using a mathematical model along with their benefits and shortcomings. This work concentrates on assessing the application of nanoparticles in EOR processes and addresses the process controlling parameters. This study briefly evaluates a few appropriate analytical and semi-analytical studies directly related to nanoparticle-assisted EOR techniques. Several nanoparticles assisted experimental works have been reviewed for both core flooding and micromodel systems.
A New Multi-objective Formulation for Optimal Placement of Shunt Flexible AC Transmission Systems Controller

Abstract The location of flexible AC transmission systems controllers for enhancement of voltage stability is an important consideration for practical power systems. In this article, a strategy for placement of shunt flexible AC transmission systems devices based on a fuzzy performance index is proposed. A fuzzy performance index based on three objectives—namely, increase in loading margin, improvement in voltage profile, and reduction of the system reactive power loss—is defined and proposed. The index can be used to find the most effective location of the shunt flexible AC transmission systems device. The proposed approach has been applied on IEEE 14-bus and 57-bus test systems. The application results are promising.
Learning DFAs by Evolving Short Sequences of Merges

The grammatical inference community has been studying evolutionary methods for DFA learning for almost three decades. These methods typically operate by learning a representation of the target DFA either as a partitioning the states of a preﬁx tree acceptor or as an encoding of its transition matrix. In this paper, we present an alternative approach for learning random DFAs over binary alphabets from sparse training data. We ﬁrst conducted several experiments on thousands of problem instances to study their behaviour and to better understand the conditions under which state merging algorithms succeed or fail. Motivated by these observations, we implemented an evolutionary algorithm in which the chromosomes encode short sequences of merges selected from a subset of high state-reduction merges. The ﬁtness of a chromosome is then measured by extending it using the EDSM heuristic and the size of the ﬁnal hypothesis is used to score the entire sequence. To improve runtime performance, we use a method that can reliably estimate the ﬁtness of a sequence of merges without extending it completely. We use the state-of-the-art EDSM algorithm as a baseline to compare our results to and observe that we can ﬁnd low-error hypotheses or the exact target DFAs with a considerably higher likelihood.
Supplemental Information Materials and Methods Image preprocessing

Before applying vector diffusion maps, images must be preprocessed so that the Euclidean distance between the image pixels is informative: we need images who are developmentally similar to have relatively small Euclidean distances, and images who are at disparate developmental time points to have relatively large distances. We must preprocess images to remove any experimental and imaging artifacts, so that most of the variation is relevant to the developmental dynamics. The relevant image operations are listed below for our general purposes, along with the relevant MATLAB functions.