Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of medicinal herbs, Andrographis paniculata, Cissus quadrangularis and Eclipta alba) on growth, digestive enzymes, biochemical constituents and protein profile of the freshwater prawn Macrobrachium rosenbergii

Introduction World aquaculture industry witnessed a phenomenal growth by way of increment in production from 24.38 million tonnes in 1995 to more than double in the year 2006, 51.70 million tones, and India with its aquaculture production of 3.12 million tones is placed second only after China (FAO, 2009). Freshwater crustaceans including prawns though produced in meager quantities (around 953,198 tonnes) have gained significance as high value commodity by standing as fifth highest valued species group. One of the major prawn species now dominating the freshwater aquaculture in India is the giant freshwater prawn (scampi), Macrobrachium rosenbergii, and it is one of the major contributors of national economy as well (Akand and Hasan, 1992; Ahmed, 2001; Muir, 2003). The production of M. rosenbergii in India increased from 7150 tonnes during the year 1999-2000 to 27, 262 tonnes in 2007-2008 (MPEDA, 2008). Farming of M. rosenbergii is spreading fast to all Indian states due to its large size attainment, tolerance to water quality changes, ability to cope with handling stress and ability to feed on unconventional feeds. The farming of scampi received a big boost lately owing to the demand and attractive price fetched in the international market and the slump in tiger shrimp production due to outbreak of disease. There is a developing interest in using medicinal herbs as a kind of immune stimulant in aquaculture (Luo, 1997; Chansue et al., 2000). Administration of medicinal herbs with shrimp diet increases the specific activity of amylase and protease in the shrimp’s digestive gland and improves growth performance. Medicinal herbs increased apparent digestibility of lipid and phosphorus, but decreased apparent digestibility of crude protein (Lin et al., 2006). Previous studies on Macrobrachium revealed that feeds formulated with cereals, pulses, groundnut oilcake, feeds incorporated with vegetable wastes, and herbals have resulted in appreciable growth and sustainability (Bhavan et al., 2010, 2011a, 2011b; Rebecca and Bhavan et al., 2011). Therefore few locally available medicinal herbs, Andrographis paniculata, Cissus quadrangularis and Eclipta alba were chosen based on their active principles reported to demonstrate whether these herbs have growth promoting ability on M. rosenbergii PL. A. paniculata (Nilavembu) is one of the most important medicinal herbs and having been widely used in Ayurvedic medicine for the treatment of gastric disorders, infectious
Students’ images of STEM professionals: a study in the Portuguese context

ABSTRACT The shortage of students in STEM (Science-Technology-Engineering-Mathematics) areas is a global concern and one of the reasons why students do not want to pursue STEM careers is the unenthusiastic image that they have about STEM areas. This study aims to examine the images that 135 Portuguese elementary students (7-12 years old) have about STEM professionals. Data collection was performed using a draw-and-write instrument: the students were asked to draw someone that works in each STEM area, and to answer two questions: ‘Where does that person work? ’ and ‘What does that person do? ’. Checklists, based on instruments previously described in the literature, were developed for each STEM discipline. A descriptive analysis of the results was made, and the statistically significant differences between male and female students’ drawings were evaluated. The results show that students still retain stereotypical images of STEM professionals and have a lack of perception about the appearance and work that STEM professionals do. In conclusion, there is a demand for changes in teaching practices to change the erroneous and unrealistic impressions that students may have about STEM areas.
Structural-Information-based Robust Corner Point Extraction for Camera Calibration under Lens Distortions and Compression Artifacts

Previous camera calibration methods often use a checkerboard to capture images and estimate the camera parameters from the correspondences between images and the checkerboard. The corner points in the checkerboard images are used as useful features for correspondence matching. Therefore, it is essential to precisely find the corner points in the checkerboard images. In many previous works, the corner points are extracted assuming that the checkerboard images are not distorted by its lens. Instead, image blurring and Gaussian noise on the images are usually considered, but other cases are not dealt with. However, the captured checkerboard images are often corrupted by lens distortions and compression artifacts, which leads to performance degradation of corner point extraction. Moreover, the corner points are extracted individually in the previous methods without considering their geometric relations. To better handle the corner point extraction problem under lens distortions, in our corner point extraction optimization, the distorted locations of the pixels on checkerboard images are corrected with the camera parameters, and the structural constraints for checkerboard image grids are then applied under the line-to-line mapping. Also, to robustly find the blurred edges between corner points due to JPEG compression, an edge surface model is newly proposed that models the transitions with over- and under-shoots around the blurred edges. Extensive experimental results show that our method significantly outperforms the state-of-the-art method with average 88.3% and 54.3% reduction in RMSE for corner point reprojection and camera parameter estimation, respectively under compression and lens distortions for synthetic and real data.
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification

Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of neural networks. However, bound propagation cannot fully handle the neuron split constraints introduced by BaB commonly handled by expensive linear programming (LP) solvers, leading to loose bounds and hurting verification efficiency. In this work, we develop $\beta$-CROWN, a new bound propagation based method that can fully encode neuron splits via optimizable parameters $\beta$ constructed from either primal or dual space. When jointly optimized in intermediate layers, $\beta$-CROWN generally produces better bounds than typical LP verifiers with neuron split constraints, while being as efficient and parallelizable as CROWN on GPUs. Applied to complete robustness verification benchmarks, $\beta$-CROWN with BaB is up to three orders of magnitude faster than LP-based BaB methods, and is notably faster than all existing approaches while producing lower timeout rates. By terminating BaB early, our method can also be used for efficient incomplete verification. We consistently achieve higher verified accuracy in many settings compared to powerful incomplete verifiers, including those based on convex barrier breaking techniques. Compared to the typically tightest but very costly semidefinite programming (SDP) based incomplete verifiers, we obtain higher verified accuracy with three orders of magnitudes less verification time. Our algorithm empowered the $\alpha,\! \beta$-CROWN (alpha-beta-CROWN) verifier, the winning tool in VNN-COMP 2021. Our code is available at http://PaperCode.cc/BetaCROWN
The President’s Role in Financial Management and Fundraising

Among the top challenges facing Oklahoma public comprehensive university presidents is the quest for adequate funding of their institutions, including adequate levels of support from state resources. Low levels of public support by the state have become a pattern, rather than an anomaly and statewide reductions in appropriations for higher education funding have had a disproportionate effect on Oklahoma’s public comprehensive universities because they have fewer diversified revenue sources. This pattern has forced presidents to become more adept at budgeting, financial management, fundraising, and finding the resources necessary for their institutions to not only be successful, but to simply survive. Study findings indicated that Oklahoma public comprehensive university presidents spend most of their time on budgeting and financial management, followed closely by fundraising. The study concluded that few presidents have a background in financial management and fundraising, and most of their experience in these areas was acquired in their previous role prior to becoming president or while on the job. Correspondence related to this article should be directed to Mark Shields, Director of Operations, Northeastern State University, shield01@nsuok.edu The American Council on Education (2017) study, the seventh study in nearly 40 years on the American college president, pointed out that college presidents from all types of higher education institutions including public and private, as well as associate degree granting to doctoral level research universities spend most of their time on budgeting and financial management followed closely by fundraising. The American Council on Education study demonstrated the need to differentiate the role and needs of university presidents at specific types of institutions, including public comprehensive universities. This study demonstrates the need to differentiate and examine the role and needs of university presidents by focusing at the state level on the role of public comprehensive university presidents in financial management and fundraising in the mid-western state of Oklahoma. Oklahoma Public Comprehensive Universities (OPCUs) and their president’s face many challenges. Among the top challenges facing these institutions and their leaders is the quest for adequate funding of their institutions, including adequate levels of support from state resources (Oklahoma State Regents for Higher Education, 2017). Over the last decade, Oklahoma’s public higher education system has found itself in an increasingly difficult situation as business and political leaders have demanded more from colleges and universities at a time when state funding has declined to its lowest level in nearly 20 years (Krehbiel, 2017). Over the past two decades, a dramatically shrinking share of the higher education budget has come from the state (Fine,
Biophysical chemistry of EcoKI in physiological solutions: emulating the cell interior

The genomic DNA of bacteria exists in a compact and organised conformation ' ithin the cell. This conformation is maintained by macroniolecular crowding and binding of nucleoidassociated proteins, polyamines, and other non-specific DNA-binding ligands. Production of polyamines and nucleoid-associated proteins is tightly regulated and restructures the nucleoid under environmental conditions that induce DNA damage into an even more highly condensed conformation. These 'stressful' conditions can cause the specific methylation sequence of DNA to be lost, which leaves the DNA open to self-attack by restriction enzymes. One such enzyme is EcoKi, a type I restriction enzyme that protects the bacterial cell by destroying foreign invading DNA. Upon loss of specific niethylation, EcoKI could potentially destroy the host DNA and kill the bacteria. This damaging restriction is alleviated due to partial proteolysis of EcoKi by CIpXP, although a reduced ability to destroy incoming foreign DNA is maintained. However, this method of alleviation does not exist for all type I enzymes, implying that additional restriction alleviation is required to protect bacteria. In this thesis, it has been found that the condensed structure of DNA produced by the polyamine spermidine and the nucleoid-associated protein StpA, as well as the non-specific DNA-binding of the ligand YOYO, dramatically inhibit EcoKi ATP hydrolysis and restriction activities. These results show that condensation may be a method used by bacteria to protect the niicleoid from self-attack by EcoKI under DNA-damaging conditions, and therefore forms a second mechanism of restriction alleviation. Such a condensed DNA structure may inhibit access of the enzyme to its binding site as well as inhibiting the physical ability to translocate DNA. This is in contrast to invading foreign 'naked' DNA in the cytoplasm, which adopts a more open conformation, and therefore forms an ideal substrate for EcoKi translocation and restriction.
BOAT: Bilateral Local Attention Vision Transformer

Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Transformers such as ViT and DeiT adopt global self-attention, which is computationally expensive when the number of patches is large. To improve efficiency, recent Vision Transformers adopt local self-attention mechanisms, where self-attention is computed within local windows. Despite the fact that window-based local self-attention significantly boosts efficiency, it fails to capture the relationships between distant but similar patches in the image plane. To overcome this limitation of image-space local attention, in this paper, we further exploit the locality of patches in the feature space. We group the patches into multiple clusters using their features, and self-attention is computed within every cluster. Such feature-space local attention effectively captures the connections between patches across different local windows but still relevant. We propose a Bilateral lOcal Attention vision Transformer (BOAT), which integrates feature-space local attention with image-space local attention. We further integrate BOAT with both Swin and CSWin models, and extensive experiments on several benchmark datasets demonstrate that our BOAT-CSWin model clearly and consistently outperforms existing state-of-the-art CNN models and vision Transformers.
How Political Identity Influences COVID-19 Risk Perception: A Model of Identity-Based Risk Perception

Past research suggests that conservatives are usually more threat-sensitive than liberals are. Yet during the COVID-19 pandemic, conservatives consistently underestimated the risk from the virus. To reconcile this paradox, we introduce a model of identity-based risk perception (IRP). This model posits that risk perceptions depend not only on objective risk but also on people’s political identity and whether the risk pertains to their group identity (group risk) versus individual identity (individual risk). When asked about the group risk posed by a threat (“How many Americans will die of COVID-19? ”), conservatives focus on their national pride and underestimate the risk of contracting the virus compared to liberals. However, when asked about individual risk from the same threat (“What is the probability of an individual dying of COVID-19? ”), conservatives focus on individual mortality threat and overestimate the risk of succumbing to the virus compared to liberals. Three national surveys support the IRP model.
An Interrelated Decision-making Model for an Intelligent Decision Support System in Healthcare

The nature of decision making in healthcare is complex and crucial. It is essential to have a tool that helps with accurate and correct decisions based on real-time data. Moreover, the healthcare process itself is complex, comprising various stages from primary to palliative, closely related to each other, and the process is different depending on the type of disease. Each stage has a crucial decision to be made relying on other stages decisions. Thus, an intelligent decision support system (IDSS) model based on a data mining approach becomes a prominent solution. However, the existing IDSS and Group Decision Support System (GDSS) applied a single-stage approach and primarily focused on development at a certain stage for specific outcomes. In contrast, the nature of healthcare decision-making in each stage is related to the previous stages, which change dynamically. Therefore, this paper proposes an interrelated decision-making model (IDM) for IDSS in healthcare that aims to have an effective decision by utilizing knowledge from previous and following treatment stages known as IDM-IDSS-healthcare. The experiment was conducted using simulated diabetes treatments data that were validated by the medical expert. Eight data sets with distinct sizes were constructed and classified into two types of decision-making categories. Each data sets consists of primary and secondary care stages with a range of 25 to 58 attributes and 300-11,000 instances. The experiment results show algorithms J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet and AdaBoostM1 obtained the best accuracy in sequence from 46% to 99%. The result also shows the improvement of decision-making efficiency with the prediction model accuracy has increased up to 56%. In addition, all respondents agreed in a focus-group discussion with medical and information technology (IT) experts that the proposed IDM-IDSS-healthcare is practical as a healthcare solution. Moreover, the solution for the development of IDM-IDSS-healthcare should use the multi-agent approach.
An Intelligent Game Theory Framework for Detecting Advanced Persistent Threats

The advanced persistent threat (APT) is a stealthy cyber attack perpetrated by a group that gains unauthorized access to a computer network and remains undiscovered to steal specific data and resources. Fast detection and defense of APT attacks are critical tasks in cyber security. Previous works use simple feature extraction and classification methods to distinguish APT information flow from the normal one. However, APT attacks are latent, with very little flow and mixed in many normal information flows. Moreover, APT attacks can adjust their behavior according to the environment, making it challenging to be discovered and extract features. Meanwhile, dynamic information flow tracking (DIFT) is a tool for tracking information flow, which can also adjust the marking strategy according to the environment and is often used to track and detect APT information flow. On the other hand, game theory is a mathematical model that expresses the game of two or more parties. Therefore, this motivates us to model a game theory to solve the above challenge. In this paper, to solve the above obstacles, we propose an intelligent game theory framework named DPS, which models the strategic interaction between APTs and DIFT and aims to get a high reward for DIFT. Our proposed DPS framework utilizes deep reinforcement learning to find the Nash equilibrium. The game model is a nonzero-sum, average reward stochastic game. Specifically, we design a subgraph pruning strategy and deep Q-network to guide the player in exploring new strategies in the information flow graph. Finally, we implement our framework to compute an optimal defender strategy to defend cyber security. Based on 2 real-world datasets, the experiment results demonstrate that the DPS framework can delay APT intrusions under equilibrium in 3 epochs and get a better reward than the Uniform policy.
Designing and Evaluating Student-facing Learning Dashboards: Lessons Learnt

Through the rise of on-line education, an abundance of learner data is generated and gathered. While Educational Data Mining provides insights algorithmically to better understand students, Learning Analytics (LA) attempts to leverage these traces to empower learners by increasing motivation, autonomy, effectiveness, and efficiency. One method to achieve this empowerment is that of Learning Dashboards, a personal informatics or Quantified Self approach, helping learners self-reflect and gain self-knowledge through the visualisation of these traces. Learning Dashboards are a welcome and much needed topic that shifts the research focus towards, and actively involves, teachers, advisers, and students, providing them with insights into behaviour of learners at both individual (a student) and group level (peer activities in courses, study programs, institutions). Investigating the potential impact of visualising these traces is important, but such research demands long-term deployments in realistic settings. Such an endeavour is challenging, as it requires commitment from institutions, teachers, and learners of (usually) unproven technology during sensitive, life-impacting situations, as well as running the risk of discovering problems with the dashboard designs after commitment, which raises ethical issues. Our research attempts to provide some leverage for such deployments by i) providing evidence of the perceived benefits and ii) providing design guidelines required to create useful and meaningful dashboards. To explore the required design choices, we take an iterative, design-based research approach, in close collaboration with experts, teachers, and students. The work starts by tackling following research questions: “How should we visualise learner data to support students to explore the path from effort to outcomes? (RQ1)”, and “How can we promote students, inside and outside the classroom, to actively explore this effort to outcomes path? (RQ2). ” To explore these questions, we have designed, deployed, and evaluated five learning dashboards in blended learning environments. This research resulted in several
Surface code quantum computing by lattice surgery

In recent years, surface codes have become a leading method for quantum error correction in theoretical large-scale computational and communications architecture designs. Their comparatively high fault-tolerant thresholds and their natural two-dimensional nearest-neighbour (2DNN) structure make them an obvious choice for large scale designs in experimentally realistic systems. While fundamentally based on the toric code of Kitaev, there are many variants, two of which are the planar- and defect-based codes. Planar codes require fewer qubits to implement (for the same strength of error correction), but are restricted to encoding a single qubit of information. Interactions between encoded qubits are achieved via transversal operations, thus destroying the inherent 2DNN nature of the code. In this paper we introduce a new technique enabling the coupling of two planar codes without transversal operations, maintaining the 2DNN of the encoded computer. Our lattice surgery technique comprises splitting and merging planar code surfaces, and enables us to perform universal quantum computation (including magic state injection) while removing the need for braided logic in a strictly 2DNN design, and hence reduces the overall qubit resources for logic operations. Those resources are further reduced by the use of a rotated lattice for the planar encoding. We show how lattice surgery allows us to distribute encoded GHZ states in a more direct (and overhead friendly) manner, and how a demonstration of an encoded CNOT between two distance-3 logical states is possible with 53 physical qubits, half of that required in any other known construction in 2D.