Reviewerly/exHarmony huggingface dataset:
Stability Prediction Methodology to Manage Vaccines and Pharmaceutical Products

A major challenge for the pharmaceutical industry is to anticipate product stability, regardless of the time/temperature profile during its manufacturing and supply. Based on official guidelines, empirical stability protocols are performed to ensure drug product stability in defined conditions (usually 5, 25, 37/40°C). However, these protocols usually have a limited predictive power to assess a product's stability under different conditions, e.g. in case of cold chain breaks. To address this shortcoming, and especially when complex bio-pharmaceutical products such as vaccines are involved, stability kinetic modelling based on the adequate data sets and adapted models can help predict product stability with required levels of confidence. The aim of this paper is to introduce the mathematical kinetics modeling of vaccine stability with dedicated methods/algorithms developed on different vaccines/pharmaceutical case studies [1]. In short, this approach consists of analyzing experimental datasets through selected phenomenological models, related to observed or expected degradations of vaccines. This approach, exemplified by the influenza vaccine, makes it possible to predict the stability of the vaccine in any thermal situation or during a change of process/formulation/strain. Special attention is paid to model discrimination and the increase of precision in models’ prediction through the optimal design of experimental strategies.
Training management in the context of digital transformation - a case study in National Academy of Education Management

In recent years, the number of research projects in Vietnam and abroad on digital transformation in higher education has significantly increased with a variety of research topics, but the number of in-depth research topics and comprehensive management of training activities of higher education institutions in the context of digital transformation has been limited. To address this research gap, the article focuses on analyzing the current situation of training management in the context of digital transformation at the National Academy of Education Management on the basis of a theoretical framework for digital transformation in higher education institutions. The article employs mixed research approach and convergent parallel mixed method design (Creswell, 2013), which uses and analyzes 29 secondary data, questionnaire survey with 244 students, indepth interviews 8 staff, lecturers and 9 students. Research results have shown the characteristics of training management in the context of digital transformation on five aspects: leadership and strategy; facilities, infrastructure, technology; regulations, operation; people; organizational culture. The research results are meaningful for public higher education institutions that are in the early stages of digital transformation.
Developing collaborative skill scale of students trend to the 21st century skills approach

Collaborative skill is the most mentioned among skills in 21st century skills research because it plays a very importance role in learning as well as working. Although it has received many studies, but researches on the scale to evaluate this skill have still limited. Based on the theoretical related to collaborative skill in the trend to the 21st century approach, the criteria to measure this skill was developed and structured into the scale. Then, this scale was analyzed in two stages: (1) pilot test to analyze the reliability of the scale on self-assessment of 92 students and the got comments and suggestions from 5 lecturers as well as students was conducted; (2) The formal research stage is mainly used the exploratory factor as well as the reliability analysis from 467 student responses. The research results determined that there are 22 criteria divided into 4 factors belonging to 3 components. In which, 4 criteria of the knowledge component, 5 criteria of the attitude\values\ethical component, 13 criteria of the skills component include the element interacts and works effectively with others\ multicultural team (4 criteria) and project management in a multicultural environment, guiding and managing others (9 criteria) were selected.
Smart University: from the Educational and Technological view

This article reviews from the emerging concept of smart university. The purpose is to present some views about smart university, hypothesis and conceptual framework about the term and characteristics of smart universities published in the scientific literature, from which the role of the IoT (Internet of Thing) is emphasized as a fundamental element in the conception and implementation of projects and help traditional universities to understand, identify and evaluate paths for a transformation into a smart university.
Research for building a set of standards for assessment of school culture at the Hanoi Metropolitan University in the current context

The school culture will help the school truly become a cultural and educational center, a place where intellectual strength and compassion in society converge, contributing to a comprehensive educational product. Therefore, school culture has a great role in changing and developing schools. Building school culture is an indispensable requirement of educational activities in schools. In order to evaluate and recognize a school as meeting cultural standards, it is essential to have a set of school cultural criteria. In addition, the set of school cultural criteria is also the basis for schools to develop goals, contents, and action plans on building school culture. Therefore, the set of school cultural criteria is not only to evaluate the school culture but also to be the basis for preserving and developing the core cultural values of the school, and at the same time has the effect of orienting and regulating the school culture. make the cultural face of the school better and ensure the sustainable development of each school. The article will focus on studying the theory and reality of school culture at Hanoi Metropolitan University as a basis for proposing a set of standards for school culture assessment, contributing to realizing cultural development activities in the school.
Geochemistry and Petrology of the Unimodal Versus Bimodal Neogene Volcanism in the Konya Volcanic Field, Central Anatolia, Turkey

Orogenic magmatic settings are mainly characterized by the unimodal volcanism with abundant intermediate, andesitic rocks, but those of melt inclusions display bimodal distribution with the rarity of intermediate compositions. The compositional gaps may actually be present in most magmatic settings, but this feature can be masked by mixing processes, producing intermediate compositions between the mafic and felsic end-members. Here, we show how mixing processes control the compositional modality in the Konya volcanic field (KoVF) from the Central Anatolia, by using new and published data of whole-rock major-trace elements and Sr-Nd-Pb isotopes. The Erenlerdağ-Alacadağ Volcanic Complex (ErAVC) and Sulutas Volcanic Complex (SVC) are two Neogene volcanic centers in the KoVF, overlying two distinct tectonic blocks. Both complex is chacterized by basaltic (<55 wt% SiO2) to rhyodacitic (~68 wt% SiO2) high-K calc-alkaline volcanic rocks with the orogenic geochemical signatures except for small occurrence of the alkaline rocks (e.g. sodic alkali basalts and potassic lamprophyres) in the SVC. The ErAVC is an example of unimodal volcanic suite with abundant andesite-rhyodacite lavas, but the SVC is represented by bimodal suite with the scarcity of andesitic rocks. The ErAVC and SVC high-K suites plot on the same major and trace element trend, and they converge towards a common parental magma, suggesting a common mantle source. Mass-balance modeling suggests that their major element variations can be largely explained by fractional crystallization of amphibole, plagioclase and Fe-Ti oxides. However, Sr-Nd-Pb isotopes show correlations with a differentiation index (e.g. Rb) indicating that open-system processes played a role in their differentiation. Modeling of open-system processes (e.g. AFC with recharge situation) shows that a low-degree crustal assimilation (e.g. rate of assimilation/rate of fractional crystallization, r<0.2 and crust/magma ratio, ρ: 15-16%) of lower and upper crust-like material involved in the differentiation of the ErAVC and SVC, respectively. However, the AFC with recharge situation modeling suggests that magma recharge process (e.g. β: rate of magma recharge/rate of assimilation) is more efficient in the ErAVC (β: 3.45, % ~52.5 rate of recharge) relative to those of the SVC (e.g. β: 2.15, % ~36.55 rate of recharge). Hence, we think that common parental magmas derived from the same source followed distinct differentiation paths in the crust for the ErAVC and SVC, and their compositional modality was mainly controlled by magma recharge process.
Social Network Data Publishing Model Satisfying Differential Privacy

Social networks contain sensitive information, and direct press conferences disclose people’s privacy. The published social network data needs privacy protection. The currently released social network data has seriously damaged the network structure due to privacy protection. This paper proposes a social network graph publishing model that preserves the social network structure. This model builds an uncertainty graph based on privacy protection probability to generate social network data for publication. It is proved theoretically that the social network graph publishing model satisfies the definition of differential privacy. This paper uses three real social network datasets to evaluate the effectiveness of the proposed method, showing that the method effectively retains the network structure.
Design of Network Teaching System for The Literature Course Based on C/S Architecture

Information technology is highly developed in today’s society, and digital construction using computer technology has become a main means to improve teaching efficiency. The network teaching system can realize cross-regional and real-time interactive teaching by using the hardware environment such as computer and optical fiber network, and relying on the professional network on-site teaching platform. A variety of network platform systems based on B/S architecture are integrated into the classroom. Because the B/S architecture relies on browsers, it is difficult to realize personalized functions; the network system of B/S architecture has higher performance requirements on the server, and its overhead is limited. The popularization and promotion of the network system. In order to solve the above problems, the system in this paper is based on the C/S architecture, which deeply customizes the client with personalized functions for the literature classroom, reducing the performance requirements for the server. On the basis of in-depth study of GDI+ technology, TCP/IP technology and IP multicast technology, the realization technology method of the system is studied. This system analyzes functional requirements and non-functional requirements, divides the system into several parts, such as frame design, overall system design, and administrator subsystem design, and gives design schemes respectively.
Identifying Critical Nodes Via Link Equations and Deep Reinforcement Learning

Identifying an optimal set of nodes whose activation would lead to a maximal spread of influence in the network is a major concern in network science. It has numerous applications such as epidemic control and rumor containment. However, limited by the high computational costs, most of the existing techniques fail to address million-sized graphs. Moreover, the previous approaches have primarily focused on the structural characteristics of the network while the characteristics of information diffusion are ignored. This paper proposes a deep reinforcement learning framework, DeepELE, to bridge these gaps. DeepELE incorporates a graph embedding technique to represent the graph states and applies a deep reinforcement learning method to learn the policy automatically. Note that we assess the contribution of links to spreading processes and further account for the diffusion-related contribution along with the graph structure information into convolutional neural and the Q network. Extensive experiments on both synthetic and real-world networks validate the efficiency and efficacy of DeepELE. The results demonstrate that DeepELE significantly outperforms the state-of-the-art methods, especially for large-scale networks with millions of nodes.
Getting Started

This chapter covers the initial considerations for getting started with your code club or maker space. I have included examples from the experiences from our earlier code club years with challenges faced and give an overview of our strategies to address these. Chapter 8 will dive into exploring these strategies in detail, using specific how-to examples and useful templates. As you’ve probably guessed, there’s no technical content in this chapter, but rather it is a way of outlining some of the more essential lessons we learned after several years of running a code club.
Adaptable address parser with active learning

Address parsing, decomposing address strings to semantically meaningful components, is a measure to convert unstructured or semi-structured address data to structured one. Flexibility and variability in real-world address formats make parser development a non-trivial task. Even after all the time and effort dedicated to obtaining a capable parser, updating or even re-training is required for out-of-domain data and extra costs will be incurred. To minimise the cost of model building and updating, this study experiments with active learning for model training and adaptation. Models composed of character-level embedding and recurrent neural networks are trained to parse address in Taiwan. Results show that by active learning, 420 additional instances to the training data are sufficient for a model to adapt itself to unfamiliar data while its competence in the original domain is retained. This suggests that active learning is helpful for model adaptation when data labelling is expensive and restricted.
Collection Skeletons: Declarative Abstractions for Data Collections

Modern programming languages provide rich abstractions for data collections as part of their standard libraries, e.g. C++ STL Containers or Java Collections Framework. Typically, these collections frameworks are organised as hierarchies that provide programmers with common abstract data types (ADTs) such as lists. While convenient, this approach introduces problems which ultimately affect application performance due to users over-specifying collection data types limiting implementation flexibility. In this article, we develop Collection Skeletons which provide a novel, declarative approach to data collections. Using our framework, programmers explicitly select properties for their collections, thereby truly decoupling specification from implementation. By making collection properties explicit immediate benefits materialise in form of reduced risk of over-specification and increased implementation flexibility. We have prototyped our declarative abstractions for collections as a C++ library, and demonstrate that benchmark applications rewritten to use Collection Skeletons incur little or no overhead. We also show how Collection Skeletons help shielding the application developer from parallel implementation details, either by encapsulating implicit parallelism or through explicit properties that capture the requirements of parallel algorithmic skeletons. Across several benchmarks we observe performance speedups (avg. 2.57-2.93, max. 16.37) resulting from use of Collection Skeletons, while also enhancing performance portability across three different platforms.
Dynamic Feature Centric Schematic Tree Based Improved Big Data Management Using Efficient Etl

The problem of big data management in cloud has been well studied. The increased form of data heterogeneity increases the challenge in maintaining the data in most readable and relational form. There exist different approaches which perform Extraction-Transformation-Learning from the given big data towards obtaining information in most feasible way. There are methods which use basic metadata in maintaining the data and they extract data and transform them in form of same set of relational data. However, the transformed data has not supported the learning problem which in turn should produce higher accuracy in knowledge extraction. To solve this issue, a dynamic feature centric schematic tree based ETL model (DFST) has been presented in this article. This approach transforms the big data given in form of structured schematic tree to support higher performance of big data management as well as knowledge mining. The proposed model extracts the features from the data given and finds the schema of any data towards various relational schemas available. Extracted data has been measured for Feature Centric Schematic Relation Score (FCSRS) towards various schemas available. The value of FCSRS is measured towards different relational schema maintained and identifies the most similar schema in feature level. If the schema of any data given does not match with any of the schema, then it generates a own schema to update the schema set. For the schema identified or generated, the method generates a schematic tree and adds the features under specific leaf by generating a node. Similarly, for an existing schema, the method adds a node under the leafs available according to the schema of the data given. At the learning stage, the method generates a rule set according to the tree available and the values of different features on the tree given. The learning process computes feature centric similarity (FCS) on various dimensional trees to find the tree and based on that, the method identifies the features to generate rule sets. Generated rule sets are populated as results towards analysis with higher accuracy
Getting Tactile with Python

Making things with your hands is engaging – one way to stop someone from fidgeting is to give them something to hold and play with. In this chapter, we add some crafty skills to the mix and leverage our other skills to tie everything together.
Intelligent Traffic Management Strategy for Traffic Congestion in Underground Loop

More and more network-type underground loops have been recently developed and constructed in conjunction with the interconnected underground space of urban CBD areas. At the same time, the high intensity of arrival and departure traffic, the significant tidal effect of traffic and the complex traffic organization in CBD areas, may bring challenges to the traffic operation of the underground loops. The accurate identification of traffic congestion patterns and the efficient traffic control of loop play an important role in improving the capacity and ensuring the safe operation of the underground loop system. In this paper, a simulation analysis of the morning and evening peak traffic scenarios of a designed underground loop is conducted to obtain its normal traffic congestion patterns and characteristics. And then, the potential causes of different congestion patterns are analyzed. Based on this, combination of traffic control strategies including "ramp control", "plot control" and "fast entry without railing" are developed, and then the different control schemes are evaluated and verified using simulation technology. The results of the study can provide reference for the formulation of traffic congestion control measures for the underground loop, which can help improve the capacity and ensure the safe operation of the underground loop system.
Extrapolative Beliefs About Bitcoin Returns

Using data on investor beliefs about future Bitcoin prices, we examine how investors form expectations about Bitcoin returns. We find that investors extrapolate from Bitcoin’s past returns, with more weight on recent returns. Compared to institutional investors, such extrapolative beliefs are stronger among individuals. Overall, past Bitcoin returns die out relatively slowly in investors’ memory. The extrapolative component of beliefs predicts future Bitcoin returns with a positive sign in the short term and a negative sign in the long term.
Introduction to Reinforcement Learning

The nature of human intelligence has attracted the special interest of scientists and researchers, as humans are the most intelligent species on the planet. The way that humans adapt their behavior as they interact with surrounding environments is a crucial learning paradigm, which can be demonstrated by how a person drives a car. When learning to drive, an inexperienced driver continuously performs steering, accelerating, and braking operations according to his perception of traffic environments.
RESEARCH PRODUCTIVITY OF FACULTY MEMBERS OF SAURASHTRA UNIVERSITY INDEXED IN SCOPUS DATABASE

Research output or productivity means research publications of researchers of any organization; it includes journal articles, books, book chapters, conference- seminar papers, reviews or any creative work. This study analyzed the research productivity of 53 faculty members from 10 science departments of Saurashtra University, Rajkot. Researcher analyzed total 1620 research publications published during 1981 to 2022 indexed in Scopus Database, one of the largest index and abstracting database. In 2017 faculties published maximum 144 publications. Department of Physics published maximum 570 publications, Average publications per faculty is 30.6 and department of physics have highest average publication per faculty is 81.4. In this paper researcher tried to analyze author productivity according to the number of publications, citations and highest h-Index. According to this rank list Dr. Kuberkar is on first rank in terms of maximum publications, citations and highest h-index means he is the most productive teaching faculty of Saurashtra University.
Model-Free Indirect RL: Temporal Difference

The estimation of value function is critical for model-free RL algorithms. Unlike Monte Carlo (MC) methods, temporal difference (TD) methods learn the value function by reusing existing value estimates. The underlying mechanism in TD is bootstrapping. The word “bootstrapping” originated in the early 19th century with the expression “pulling oneself up by one’s own bootstraps”. Initially, this expression implied an obviously impossible feat. Later, it became a metaphor for achieving success with self-assistance. In statistical learning, bootstrapping can be interpreted as a sample reuse technique that uses historical estimates in the update step for the same kind of estimated value. In temporal difference, bootstrapping is a mechanism to reuse historical value estimates to update current value function. Similar to MC, TD only uses experience to estimate the value function without knowing any prior knowledge of the environment dynamics. The advantage of TD lies in the fact that it can update the value function based on its current estimate. Therefore, TD learning algorithms can learn from incomplete episodes or continuing tasks in a step-by-step manner, while MC must be implemented in an episodeby- episode fashion.
Current status of writing experience initiatives of preschool teachers in some preschools in Cao Bang and Dien Bien

On the basis of studying the current status of writing experience initiatives of preschool teachers in some preschools in Caobang and Dienbien provinces on related issues, that is teachers' perceptions of writing experience initiatives, organization process, and causes affecting the results of teachers' experience initiatives. Since then, we have proposed a number of measures to improve the effectiveness of this activity.