bluuebunny/crossref_metadata_2025 huggingface dataset:
Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?
Authors: Eichelberger Robert , Sheng Victor
Published: June 2022

One-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. In this paper, we are interested in the performance of both one-against-all and one-against-one for classification algorithms, such as decision tree, naïve bayes, support vector machine, and logistic regression. Since both one-against-all and one-against-one work like creating a classification committee, they are expected to improve the performance of classification algorithms. However, our experimental results surprisingly show that one-against-all worsens the performance of the algorithms on most datasets. One-against-one helps, but performs worse than the same iterations of bagging these algorithms. Thus, we conclude that both one-against-all and one-against-one should not be used for the algorithms that can perform multiclass classifications directly. Bagging is better approach for improving their performance.
A Mediation Mechanism for Automated Negotiating Agents Whose Utility Changes over Time
Authors: Hara Keisuke , Ito Takayuki
Published: June 2022

Multi-issue negotiation protocols are an important field of study because real-world negotiation problems are often complex and involve multiple issues. Although much previous work has only addressed linear utility function, that is, simple negotiations involving independent issues, recently, non-linear utility functions for complex negotiations involing interdependent issues have gained attention. Most studies, however, do not focus on the changes in utility space over time. In economic theory, it is often assumed that the utility function changes dynamically over time. It is important to seek the Pareto front, which refers to the set of Pareto optimal points, in negotiation problems. Therefore, in this paper we propose a complex utility space that changes over time and a negotiation mechanism in which the mediator takes the lead in negotiation based on the genetic algorithm (GA). The experimental results show that our approach is suitable for utility that dynamically changes over time, and finds and follows the Pareto front effectively.
Simplified Lattice Models for Protein Structure Prediction: How Good Are They?
Authors: Shatabda Swakkhar , Newton M. A. , Sattar Abdul
Published: June 2022

In this paper, we present a local search framework for lattice fit problem of proteins. Our algorithm significantly improves state-of-the-art results and justifies the significance of the lattice models. In addition to these, our analysis reveals the weakness of several energy functions used.
Phase Transition and Network Structure in Realistic SAT Problems
Authors: Kambhampati Soumya , Liu Thomas
Published: June 2022

Previous research has shown that 3-SAT problems are easy to solve both when the “constrainedness” (the ratio of the number of clauses to the number of variables) is low and when it is high, abruptly transitioning from easy to hard in a very narrow region of constrainedness. Most of these “phase transition” studies were done on SAT instances that follow uniform random distribution. In such a distribution, variables take part in clauses with uniform probability, and clauses are independent (uncorrelated). The assumptions of uniform random distribution are, however, not satisfied when we consider SAT instances that result from real problems. Our project aims for a deeper understanding of the hardness of SAT problems that arise in practice. In particular, we study two key questions: (1) How does the phase transition behavior change with more realistic and natural distributions of SAT problems? and (2) Can we gain an understanding of the phase transition in terms of the network structure of these SAT problems? Our hypothesis is that the network properties help predict and explain how the easy-to-hard problem transition for realistic SAT problems differs from those for uniform random distribution.
An Effective Approach for Imbalanced Classification: Unevenly Balanced Bagging
Authors: Liang Guohua , Cohn Anthony
Published: June 2022

Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effective ensemble learning methods for improving the performance of prediction models; however, there is a major drawback on extremely imbalanced data-sets. It is unclear under which conditions bagging is outperformed by other sampling schemes in terms of imbalanced classification. These issues motivate us to propose a novel approach, unevenly balanced bagging (UBagging) to boost the performance of the prediction model for imbalanced binary classification. Our experimental results demonstrate that UBagging is effective and statistically significantly superior to single learner decision trees J48 (SingleJ48), bagging, and equally balanced bagging (BBagging) on 32 imbalanced data-sets.
Bayesian Nonparametric Multi-Optima Policy Search in Reinforcement Learning
Authors: Bruno Danilo , Calinon Sylvain , Caldwell Darwin
Published: June 2022

Skills can often be performed in many different ways. In order to provide robots with human-like adaptation capabilities, it is of great interest to learn several ways of achieving the same skills in parallel, since eventual changes in the environment or in the robot can make some solutions unfeasible. In this case, the knowledge of multiple solutions can avoid relearning the task. This problem is addressed in this paper within the framework of Reinforcement Learning, as the automatic determination of multiple optimal parameterized policies. For this purpose, a model handling a variable number of policies is built using a Bayesian non-parametric approach. The algorithm is first compared to single policy algorithms on known benchmarks. It is then applied to a typical robotic problem presenting multiple solutions.
On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications
Authors: Ghoshdastidar Debarghya , Dukkipati Ambedkar
Published: June 2022

The role of kernels is central to machine learning. Motivated by the importance of power-law distributions in statistical modeling, in this paper, we propose the notion of power-law kernels to investigate power-laws in learning problem. We propose two power-law kernels by generalizing Gaussian and Laplacian kernels. This generalization is based on distributions, arising out of maximization of a generalized information measure known as nonextensive entropy that is very well studied in statistical mechanics. We prove that the proposed kernels are positive definite, and provide some insights regarding the corresponding Reproducing Kernel Hilbert Space (RKHS). We also study practical significance of both kernels in classification and regression, and present some simulation results.
Smart Multi-Task Bregman Clustering and Multi-Task Kernel Clustering
Authors: Zhang Xianchao , Zhang Xiaotong
Published: June 2022

Multitask Bregman Clustering (MBC) alternatively updates clusters and learns relationship between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effect, it may also cause negative effect. Another issue of MBC is that it cannot deal with nonlinear separable data. In this paper, we show that MBC's process of using cluster relationship to boost the updating clusters phase may cause negative effect, i.e., cluster centroid may be skewed under some conditions. We propose a smart multi-task Bregman clustering (S-MBC) algorithm which identifies negative effect of the boosting and avoids the negative effect if it occurs. We then extend the framework of S-MBC to a smart multi-task kernel clustering (S-MKC) framework to deal with nonlinear separable data. We also propose a specific implementation of the framework which could be applied to any Mercer kernel. Experimental results confirm our analysis, and demonstrate the superiority of our proposed methods.
Unsupervised Cluster Matching via Probabilistic Latent Variable Models
Authors: Iwata Tomoharu , Hirao Tsutomu , Ueda Naonori
Published: June 2022

We propose a probabilistic latent variable model for unsupervised cluster matching, which is the task of finding correspondences between clusters of objects in different domains. Existing object matching methods find one-to-one matching. The proposed model finds many-to-many matching, and can handle multiple domains with different numbers of objects. The proposed model assumes that there are an infinite number of latent vectors that are shared by all domains, and that each object is generated using one of the latent vectors and a domain-specific linear projection. By inferring a latent vector to be used for generating each object, objects in different domains are clustered in shared groups, and thus we can find matching between clusters in an unsupervised manner. We present efficient inference procedures for the proposed model based on a stochastic EM algorithm. The effectiveness of the proposed model is demonstrated with experiments using synthetic and real data sets.
Data-Parallel Computing Meets STRIPS
Authors: Karpas Erez , Sagi Tomer , Domshlak Carmel , Gal Avigdor , Mendelson Avi , Tennenholtz Moshe
Published: June 2022

The increased demand for distributed computations on “big data” has led to solutions such as SCOPE, DryadLINQ, Pig, and Hive, which allow the user to specify queries in an SQL-like language, enriched with sets of user-defined operators. The lack of exact semantics for user-defined operators interferes with the query optimization process, thus putting the burden of suggesting, at least partial, query plans on the user. In an attempt to ease this burden, we propose a formal model that allows for data-parallel program synthesis (DPPS) in a semantically well-defined manner. We show that this model generalizes existing frameworks for data-parallel computation, while providing the flexibility of query plan generation that is currently absent from these frameworks. In particular, we show how existing, off-the-shelf, AI planning tools can be used for solving DPPS tasks.
Temporal Milestones in HTNs
Authors: Yaman Fusun , Benyo Brett , Mulvehill Alice
Published: June 2022

We present temporal milestones for hierarchical task networks to enable the complex synchronization of tasks. A temporal milestone of a task is an intermediate event that occurs during the execution of a complex task, e.g., the start time, the end time or a milestone of any of its subtasks. Unlike landmark variables, introduced in existing work, temporal milestones respect the task abstraction boundaries and preserve structural properties enabling much more efficient reasoning. Furthermore, temporal milestones are as expressive as landmark variables. We provide analytical and empirical evidence to support these claims.
Multi-Cycle Query Caching in Agent Programming
Authors: Alechina Natasha , Behrens Tristan , Dastani Mehdi , Hindriks Koen , Hubner Jomi , Logan Brian , Nguyen Hai , Van Zee Marc
Published: June 2022

In many logic-based BDI agent programming languages, plan selection involves inferencing over some underlying knowledge representation. While context-sensitive plan selection facilitates the development of flexible, declarative programs, the overhead of evaluating repeated queries to the agent's beliefs and goals can result in poor run time performance. In this paper we present an approach to multi-cycle query caching for logic-based BDI agent programming languages. We extend the abstract performance model presented in (Alechina et al. 2012) to quantify the costs and benefits of caching query results over multiple deliberation cycles. We also present results of experiments with prototype implementations of both single- and multi-cycle caching in three logic-based BDI agent platforms, which demonstrate that significant performance improvements are achievable in practice.
An Agent Design for Repeated Negotiation and Information Revelation with People
Authors: Peled Noam , Gal Ya'akov , Kraus Sarit
Published: June 2022

Many negotiations in the real world are characterized by incomplete information, and participants' success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how they reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able to outperform people. In particular, it learned (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The approach generalizes to new settings without the need to acquire additional data. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.
Backdoors to Normality for Disjunctive Logic Programs
Authors: Fichte Johannes , Szeider Stefan
Published: June 2022

Over the last two decades, propositional satisfiability (SAT) has become one of the most successful and widely applied techniques for the solution of NP-complete problems. The aim of this paper is to investigate theoretically how SAT can be utilized for the efficient solution of problems that are harder than NP or co-NP. In particular, we consider the fundamental reasoning problems in propositional disjunctive answer set programming (ASP), BRAVE REASONING and SKEPTICA REASONING, which ask whether a given atom is contained in at least one or in all answer sets, respectively. Both problems are located at the second level of the Polynomial Hierarchy and thus assumed to be harder than NP or co-NP. One cannot transform these two reasoning problems into SAT in polynomial time, unless the Polynomial Hierarchy collapses. We show that certain structural aspects of disjunctive logic programs can be utilized to break through this complexity barrier, using new techniques from Parameterized Complexity. In particular, we exhibit transformations from BRAVE and SKEPTICAL REASONING to SAT that run in time $O(2^k n^2)$ where k is a structural parameter of the instance and n the input size. In other words, the reduction is fixed-parameter tractable for parameter k. As the parameter k we take the size of a smallest backdoor with respect to the class of normal (i.e., disjunction-free) programs. Such a backdoor is a set of atoms that when deleted makes the program normal. In consequence, the combinatorial explosion, which is expected when transforming a problem from the second level of the Polynomial Hierarchy to the first level, can now be confined to the parameter k, while the running time of the reduction is polynomial in the input size n, where the order of the polynomial is independent of k. We show that such a transformation is not possible if we consider backdoors with respect to tightness instead of normality. We think that our approach is applicable to many other hard combinatorial problems that lie beyond NP or co-NP, and thus significantly enlarge the applicability of SAT.
External Memory Best-First Search for Multiple Sequence Alignment
Authors: Hatem Matthew , Ruml Wheeler
Published: June 2022

Multiple sequence alignment (MSA) is a central problem in computational biology. It is well known that MSA can be formulated as a shortest path problem and solved using heuristic search, but the memory requirement of A* makes it impractical for all but the smallest problems. Partial Expansion A* (PEA*) reduces the space complexity of A* by generating only the most promising successor nodes. However, even PEA* exhausts available memory on many problems. Another alternative is Iterative Deepening Dynamic Programming, which uses an uninformed search order but stores only the nodes along the search frontier. However, it too cannot scale to the largest problems. In this paper, we propose storing nodes on cheap and plentiful secondary storage. We present a new general-purpose algorithm, Parallel External PEA* (\xppea), that combines PEA* with Delayed Duplicate Detection to take advantage of external memory and multiple processors to solve large MSA problems. In our experiments, \xppea\ is the first algorithm capable of solving the entire Reference Set 1 of the standard BAliBASE benchmark using a biologically accurate cost function. This work suggests that external best-first search can effectively use heuristic information to surpass methods that rely on uninformed search orders.
The Automated Acquisition of Suggestions from Tweets
Authors: Dong Li , Wei Furu , Duan Yajuan , Liu Xiaohua , Zhou Ming , Xu Ke
Published: June 2022

This paper targets at automatically detecting and classifying user's suggestions from tweets. The short and informal nature of tweets, along with the imbalanced characteristics of suggestion tweets, makes the task extremely challenging. To this end, we develop a classification framework on Factorization Machines, which is effective and efficient especially in classification tasks with feature sparsity settings. Moreover, we tackle the imbalance problem by introducing cost-sensitive learning techniques in Factorization Machines. Extensively experimental studies on a manually annotated real-life data set show that the proposed approach significantly improves the baseline approach, and yields the precision of 71.06% and recall of 67.86%. We also investigate the reason why Factorization Machines perform better. Finally, we introduce the first manually annotated dataset for suggestion classification.
A First-Order Formalization of Commitments and Goals for Planning
Authors: Meneguzzi Felipe , Telang Pankaj , Singh Munindar
Published: June 2022

Commitments help model interactions in multiagent systems in a computationally realizable yet high-level manner without compromising the autonomy and heterogeneity of the member agents. Recent work shows how to combine commitments with goals and apply planning methods to enable agents to determine their actions. However, previous approaches to modeling commitments are confined to propositional representations, which limits their applicability in practical cases. We propose a first-order representation and reasoning technique that accommodates templatic commitments and goals that may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, but also supports many practical patterns.
Assessing the Predictability of Hospital Readmission Using Machine Learning
Authors: Hosseinzadeh Arian , Izadi Masoumeh , Verma Aman , Precup Doina , Buckeridge David
Published: June 2022

Unplanned hospital readmissions raise health care costs and cause significant distress to patients. Hence, predicting which patients are at risk to be readmitted is of great interest. In this paper, we mine large amounts of administrative information from claim data, including patients demographics, dispensed drugs, medical or surgical procedures performed, and medical diagnosis, in order to predict readmission using supervised learning methods. Our objective is to gain knowledge about the predictive power of the available information. Our preliminary results on data from the provincial hospital system in Quebec illustrate the potential for this approach to reveal important information on factors that trigger hospital readmission. Our findings suggest that a substantial portion of readmissions is inherently hard to predict. Consequently, the use of the raw readmission rate as an indicator of the quality of provided care might not be appropriate.
Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method
Authors: Zheng Yonglei , Won Weng-Keen , Guan Xinze , Trost Stewart
Published: June 2022

Accurate and detailed measurement of an individual’s physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.
Early Prediction of Coronary Artery Calcification Levels Using Machine Learning
Authors: Natarajan Sriraam , Kersting Kristian , Ip Edward , Jacobs David , Carr Jeffrey
Published: June 2022

Coronary heart disease (CHD) is a major cause of death worldwide. In the U.S. CHD is responsible for approximated 1 in every 6 deaths with a coronary event occurring every 25 seconds and about 1 death every minute based on data current to 2007. Although a multitude of cardiovascular risks factors have been identified, CHD actually reflects complex interactions of these factors over time. Today’s datasets from longitudinal studies offer great promise to uncover these interactions but also pose enormous analytical problems due to typically large amount of both discrete and continuous measurements and risk factors with potential long-range interactions over time. Our investigation demonstrates that a statistical relational analysis of longitudinal data can easily uncover complex interactions of risks factors and actually predict future coronary artery calcification (CAC) levels — an indicator of the risk of CHD present subclinically in an individual — significantly better than traditional non-relational approaches. The uncovered long-range interactions between risk factors conform to existing clinical knowledge and are successful in identifying risk factors at the early adult stage. This may contribute to monitoring young adults via smartphones and to designing patient-specific treatments in young adults to mitigate their risk later.
Generative Adversarial Network for Abstractive Text Summarization
Authors: Liu Linqing , Lu Yao , Yang Min , Qu Qiang , Zhu Jia , Li Hongyan
Published: June 2022

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.
Sentiment Lexicon Enhanced Attention-Based LSTM for Sentiment Classification
Authors: Lei Zeyang , Yang Yujiu , Yang Min
Published: June 2022

Deep neural networks have gained great success recently for sentiment classification. However, these approaches do not fully exploit the linguistic knowledge. In this paper, we propose a novel sentiment lexicon enhanced attention-based LSTM (SLEA-LSTM) model to improve the performance of sentence-level sentiment classification. Our method successfully integrates sentiment lexicon into deep neural networks via single-head or multi-head attention mechanisms. We conduct extensive experiments on MR and SST datasets. The experimental results show that our model achieved comparable or better performance than the state-of-the-art methods.
Dialogue Generation With GAN
Authors: Su Hui , Shen Xiaoyu , Hu Pengwei , Li Wenjie , Chen Yun
Published: June 2022

This paper presents a Generative Adversarial Network (GAN) to model multiturn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.
Influence of mechanochemical activation on the rheological, fresh and mechanical properties of one-part geopolymer grout
Authors: Hamid Abed Mukhtar , Abbas Israa Sabbar , Canakci Hanifi
Published: June 2022

One-part geopolymer (OPG) grout offers advantages over two-part geopolymer grout in the handling and storage of feedstocks, mainly because of the large quantities of caustic solutions that must be handled for producing two-part geopolymer. However, OPG often suffers from a low degree of reaction, a high number of crystalline by-products and, as a result, low strength. Here, one-part geopolymer was assisted by mechanochemical activation (MC-OPG) to produce grout. The precursor materials (slag and fly ash) and sodium silicate were mechanochemically activated utilising co-grinding to produce a geopolymeric precursor, after which water is the only additive required to initiate the geopolymerisation reaction. The effects of slag/fly ash ratios on grout mixtures were also investigated. A series of tests was examined, including rheological characteristics, setting time, bleeding, unconfined compressive strength (UCS), ultrasonic pulse velocity, scanning electron microscopy and X-ray diffraction. The results showed that the mechanochemical activation technique increased the rheological characteristics and reduced the fresh properties (setting time and bleeding) of geopolymer grout compared to the one-part geopolymer. Considering mechanical properties, the UCS of MC-OPG grout was higher (41–73%) than that of OPG grout. Furthermore, slag content significantly affected the rheological, fresh and mechanical properties of all the geopolymer grouts regardless of the activation method.
Corrigendum: The alignment of projects dealing with wetland restoration and alien control: A challenge for conservation management in South Africa
Authors: Sieben Erwin J.J. , Procheş Şerban , Mashau Aluoneswi C. , Moshobane Moleseng C.
Published: June 2022

Original article: https://doi.org/10.17159/sajs.2022/11540
Errors that appear in the Discussion of the Research Article by Sieben et al. are corrected here. Dr Graham Harding (Registered PCO, Invader Plant Specialists (Pty) Ltd) is acknowledged for drawing the authors’ attention to these errors.
In-Person Education During the Early COVID-19 Pandemic at Wichita Collegiate School
Authors: Mantovani Emma , Meyer Christopher , Sandid Adam , Weeks Kerri , Dedeaux Julian , Assi Maha
Published: June 2022

Introduction. The COVID-19 pandemic forced most schools in Kansas in 2020-2021 to adopt remote or hybrid learning. Wichita Collegiate School proceeded with an in-person teaching model. The purpose of this study was to determine if in-person learning can be done safely during the COVID-19 pandemic prior to vaccine use.

Methods. Wichita Collegiate is a private school located in Sedgwick County, Kansas. The study population included 671 students (grades 1-12) and 130 staff.  The procedures implemented during the school year (8/19/20- 5/21/21) included: mandatory face coverings, six feet physical distancing, and daily temperature checks. A registered nurse performed contact tracing and executed quarantine requirements per the Center for Disease Control and Prevention (CDC) guidelines. 

Results. Over the study period, 487 students and staff were tested for COVID-19, and 18.5% (n=90) were positive. Overall, students and staff rate of COVID 19 infection was lower than the expected rate when compared to the surrounding community of Sedgwick County. Thorough contract tracing of positive cases revealed that 2.2% (n=2) individuals were likely exposed to COVID-19 at school. 

Conclusions. This study suggests that transmission of COVID-19 was infrequent in a school setting with in-person attendance, even before widespread vaccine availability. By following public health guidelines and utilizing contact tracing, this study suggests that it is possible to limit the spread of COVID-19 during in-person learning. This has immediate implications for how schools safely returned to in-person learning in the post vaccine era.
Cash Balance Management in Innovative Companies
Authors: Зарва Мария
Published: June 2022

Since the 1980s, innovative companies all over the world have been holding a substantial cash balance on their books dueto transactional, preventive, agency, tax-related and macroeconomic motives and by limitations in capital availability dueto information asymmetry. Our research examines the determinants that influence the analysis of cash holdings of hightechand non-high-tech companies.

Financial information for 38,386 unique companies was obtained from the 2009–2017 Compustat database. The final sampleversion comprised 12,083 companies, of which 2,909 were innovative. We used the panel regression method, selectingthe appropriate calculation model and a number of proxy variables.

Our research confirmed the existence of innovative companies’ significant cash holdings. Adding a macroeconomic factorvariable (GDP growth) to the research model was justified for innovative companies only. In spite of the insignificant impactof GDP, increased GDP growth resulted in a decreased cash ratio for innovative companies. The authors also revealthe insignificance of R&D expenditures for innovative companies and prove that ranking companies by the amount ofR&D expenditures and using this variable as innovation proxy was inexpedient. In addition, the authors confirm a positiverelationship between growth opportunities, company size and cash ratio and a negative relationship between dividendpayout and the amount of cash holdings.

An understanding of the reasons for cash accumulation facilitates prudential management of cash holdings in companies.This paper contributes new evidence to the study of corporate cash holding, focusing specifically on innovative companies,which have not been examined separately in the past.
The Effect of STEM Approach In Science Education On Academic Success: A Meta-Analysis Study
Authors: DEĞERLİ Mehmet , YAPICI Ümit
Published: June 2022

In this research, a meta-analysis study was conducted to examine the effect of the STEM approach in science education on academic success of students. In accordance with the criteria determined in the meta-analysis study, the studies included in the research are at the Master's and doctoral levels, and these studies were taken from the National Thesis Center database of the Council of Higher Education. In the study, a total of 147 studies carried out between 2010 and 2020 were examined and 31 Master's and 3 Doctoral studies fit the criteria of inclusion were included in the meta-analysis. The sample of a total of 34 studies consisted of 1962 students at the 4th, 5th, 6th, 7th and 8th grade levels. Analyses were made with the help of Comprehensive Meta-Analysis V2 statistical program. The random effects model was used because the structure was heterogeneous when the studies were analysed and combined. As a result of the research, the effect size of the STEM approach in science education on students' academic success was calculated as 1.420. These values are a large effect size according to the academic success variable according to the classification made. In this context, it has been concluded that the STEM approach in science education has a positive and wide-ranging effect on increasing the academic success of students. The size of the effect on academic success differed according to the grade level, and the highest effect was observed in the studies at the 4th grade level.
Walking the Creativity Tightrope: Teaching students to be appropriately radical
Authors: Wiebe Michelle
Published: June 2022

Creativity has a significant place in visual arts education (Bastos & Zimmerman, 2015; Katz-Buonincontro, 2018; Kinsella, 2018; Sawyer, 2017; Stone, 2015; Ulger, 2019; Veon, 2014) but an understanding of creativity leads to the recognition that for something to be deemed creative, it must be both novel and appropriate in context (Amabile, 2018; Cropley, 2016; Goetz Zwirn & Vande Zande, 2015; Kaufman & Baer, 2012; Plucker, 2018). While the ‘in context’ addition to the concept of appropriate, creates more space for visual artists, the constraints of appropriateness remain. For a visual arts and design instructor, this creates tension. How do I encourage students to be creative and push boundaries, yet also teach them to be mindful of the constraints?

            This paper discusses observations in undergraduate design education studio classes wherein students learn to use process and thumbnail sketches to push their concepts beyond predictable and into the radical or novel realm. A modified design thinking process provides the structure that allows students to feel confident about exploring ideas. Ongoing feedback from peers and instructor serves to both push and reign in ideas when revisions are valuable. The process requires diligence and practice. It also requires that students work to break free from either/or thinking and view their work on a continuum.

            Reflecting on relevant methods for encouraging students to accept the value of novel ideas that also acknowledge the viewer is worthwhile. Encouraging students to recognize that creativity is enhanced through process while also engendering an understanding of the nature of creativity is valuable and can lead to the generation of radical ideas that have merit due not only to their novelty but also to their ‘appropriateness.’
Relato visual Espíritu de Pájaro de Fredy Chinkangana
Authors: Cortés Picazo Luis Claudio
Published: June 2022

Durante el año 2020 en pleno contexto de emergencia sanitaria del COVID-19, emerge la necesidad de estrachar lazos entre texto poético e ilustración gráfica, con el objetivo inicial de experimentar nuevas formas de construcción del saber artístico, a través de herramientas manuales y digitales de edición para generar conocimiento desde la expresión visual. Experiencia traducida en una serie de registros visuales, tales como bocetos y dibujos acompañados de relatos poéticos que configuran un mundo imaginario y simbólico poblado de personajes y escenarios alejados de la sensibilidad y experiencia humana.

“Espíritu de pájaro” de Chinkangana (2010) es un relato poético cargado de “pura literatura, pura creación, aquello que tanta falta les hace a los seres humanos: tan cuerdos, tan calculados, tan poco instintivos” (Sierra,2017). Relato que a través del lenguaje oral y lenguaje visual remite a un mundo cada vez más alejado, castigado, maltratado y menoscabo por la acción irracional del ser humano. Y que en pleno confinamiento emerge como grito desgarrador desde una oralidad resignificada con imágenes que cobran vida propia al margen de un presente incapaz de respetar la memoria pasada de pueblos nativos: “Dedico estos cantos a la lucha y resistencia de nuestros pueblos nativos en Colombia y América; a los abuelos y abuelas que han sabido guardar en buen cofre la sabiduría para ir sacándola en los momentos más difíciles de nuestro caminar” (Chinkangana, 2010, p.10).

De este modo, texto e imagen configuran un relato visual conformado por “ocho actos” que representan al poema “Espíritu de pájaro” de Chinkangana (2010) en diferentes fragmentos. Relato que evidencia la necesidad de pertenecer a una Madre Tierra, de la cual nos hemos expulsado para crear espacios que satisfagan un caminar alejado de tradiciones orales. Lenguaje que permite conservar la naturaleza étnica, razones filosóficas y cosmovisión la vida de los pueblos indígenas, cuya visión holística de la naturaleza cumplen un papel fundamental en el ciclo de vida de los pueblos indígenas, para trascender el límite del bienestar físico individual o colectivo que nos pone en contacto con la esencia espiritual de la vida misma (Moreno Rangel y Olmos Zamudio, 2014, pp.30-31).
The Effect of Environmental Enrichment on Sun Bear (Herlarctos malayanus) Behaviour in Captivity
Authors: Abdul-Mawah Siti Sarayati , Y. Norma-Rashid , R. Ramli
Published: June 2022

One of the main concerns for animals kept in captivity is expression of stereotypic behaviour which could indicate stress in the animals. To ensure the welfare of animals are adequate for conservation and to promote species specific behaviour that similar to those in the wild, most of zoos are implementing environmental enrichment. The effect of environmental enrichment on behavioural changes was studied in seven captive sun bears kept in an enclosure at Zoo Negara, Kuala Lumpur, Malaysia. Bear behaviours are divided into three categories: Active, passive, and abnormal behavioural. The study involved three period: baseline, enrichment, and post-enrichment period. The activity budget was recorded as a single animal scan. A total of 210 h of baseline data was collected over 36 weeks at varying times of days. Three different environment enrichment tools were used; buoy with dog food, hidden fruit mixed with honey, and gunny sack filled with cinnamon powder. They were installed in the enclosures for 10 consecutive days on a rotational basis. Both enrichment and post-enrichment observations were done for 210 h using the same methods as in baseline sampling. Baseline data showed greater passive and abnormal behaviours than active behaviour. With the introduction of the enrichment tools, the sun bears displayed decreased passive and abnormal behaviours with an increment of active behaviour. For post-enrichment period, there was no significant difference when compared with the baseline between all behavioural categories. Among three enrichment tools, hidden fruit mixed with honey was the most preferred enrichment followed by buoy filled with dog food and gunnysack with cinnamon powder. It is suggested that food-based enrichment should be lasting until evening for the sun bear to engaged longer to the enrichment. Although less preferred, cinnamon powder introduction as sensory enrichment was recommended as well. However, it is crucial to ensure that the cinnamon’s odour lasting until late evening, where the bears are ready to enter their night den. It can be concluded that the application of various enrichment tools may help to alleviate abnormal behaviour in captive bears.
Growth and Anatomical Adaptations in Response to Salinity Stress in Cucurbita moschata Duchesne ‘Butternut’ (Cucurbitaceae)
Authors: Taratima Worasitikulya , Sudjai Sununta , Maneerattanarungroj Pitakpong
Published: June 2022

Pumpkin or squash is an economically important crop that is moderately sensitive to salinity but how its growth is affected by soil salinity is poorly understood. Salinity stress on physiological and anatomical traits of Cucurbita moschata ‘Butternut’ was investigated under hydroponic culture using Hoagland’s solution with various NaCl concentrations (0, 25, 50, 75, 100, and 150 mM) for four weeks. The results showed that pumpkin growth characters decreased after cultured in various NaCl concentrations. Leaf number, leaf width, leaf length, root number, stem height, stem diameter, green intensity in terms of SPAD units, chlorophyll fluorescence (Fv’/Fm’, Fv/Fm), total chlorophyll, chlorophyll a and chlorophyll b contents significantly (p < 0.05) decreased after culture in various NaCl concentrations. Salinity stress impacted fiber layer thickness, vascular bundle size and vessel diameter of treated plants but did not affect cuticle thickness. Physiological and anatomical traits significantly correlated with salinity gradients, except for chlorophyll b and chlorophyll fluorescence, in both light and dark condition. Results provide significant data to improve the understanding of adaptation mechanisms of tolerant pumpkin cultivars under salinity stress condition.
Sensitivity of Proteus vulgaris to Zinc Oxide Nanoparticles
Authors: Djearamane Sinouvassane , Ravintharan Tharani , Liang Sharolynne Xiao Tong , Wong Ling Shing , U.S. Mahadeva Rao , Balasubramanian Senthilkumar
Published: June 2022

Zinc oxide nanoparticle (ZnO NP) has become a popular choice in nanomedicine and in the treatment of infections. The present study investigated the sensitivity of Proteus vulgaris to ZnO NPs. The bacteriostatic and bactericidal effects of P. vulgaris were determined by using turbidity and colony count methods. The oxidative stress induced by the treatment of ZnO NPs was evaluated by investigating the levels of intracellular reactive oxygen species (ROS) along with lipid peroxidation (LP) analysis. The results showed the highest bacterial growth inhibition of 76.21±1.91 and 87.49±3.29% determined using the turbidity and colony count methods, respectively. The highest oxidative stress effects were observed in P. vulgaris exposed to 100 µg/mL of ZnO NPs for 24 h as shown by 510.90±108.53% increase in ROS production and 328.77±44.36% increase in LP level. The Fourier transform infrared spectroscopy (FTIR) spectrum illustrated the possible involvement of functional groups such as amine, alkane, acid and alkene from the bacterial cell wall in allowing the surface attachment of nanoparticles on the bacterial cells. Hence, the present study clearly demonstrated the sensitivity of P. vulgaris to ZnO NPs.
Analysis of Serum Pharmacochemistry of Hederagenin using UPLC-Q-TOF/MS
Authors: Yang Meng , Wang Jing
Published: June 2022

This study investigated the changes of plasma absorbed components in rats after oral administration of hederagenin. Serum pharmacochemistry analysis of hederagenin was carried out to understand the changes of its metabolic components in the body. Biological samples were collected and then the migration components of hederagenin-containing serum were established by UPLC/Q-TOF-MS technique. Possible metabolites were obtained for comprehensive analysis through relevant studies and the regulation of broken bonds in the molecular structure of hederagenin. At about 10.262 min, the molecular ion peak of the hederagenin of M/Z 471[M-H]- was detected in negative ion mode and the prototype product and its associated fragment ions could be detected only at 1, 3, 6, 9, 12, and 24 h after administration. Seventy-one signal peaks of potential metabolites were found in the drug serum. Based on the bond energy characteristics of molecular structure, 47 possible metabolite-related molecular ion peaks through decarboxylation, dehydration, demethylation or methyl shift, deoxygenation, ring opening, and unsaturated biformation were deduced, and signals of 35 metabolite-related molecular ion peaks were identified. Hederagenin can metabolize many products in vivo. Important information about the metabolism of hederagenin, which is useful for fully understanding its mechanism of action, was provided in this study.
Penghasilan Selulosa Bakteria daripada Kordial Minuman Tamat Tempoh dan Potensi Pengunaannya sebagai Gel Selulosa Anti-Pemerangan
Authors: Francis Fabiana , Zaki Zur Ain Aqillah Binti , Mohd Zaini Nurul Aqilah
Published: June 2022

Selulosa adalah polimer karbohidrat yang mudah diperoleh daripada sumber tumbuhan dan dihasilkan melalui proses fermentasi pelbagai jenis bakteria seperti Komagataeibacter xylinus. Walau bagaimanapun, selulosa bakteria mempunyai ketulenan dan kapasiti pegangan air yang lebih tinggi berbanding selulosa daripada sumber tumbuhan dan oleh itu, ia mempunyai potensi untuk digunakan sebagai gel selulosa aktif dalam industri makanan seperti gel anti-pemerangan. Minuman kordial yang telah tamat tempohnya merupakan media fermentasi yang baik bagi penghasilan selulosa bakteria kerana mengandungi kandungan gula yang tinggi dan sekaligus mengurangkan masalah pengurusan sisa minuman. Oleh itu, objektif kajian ini adalah untuk menghasilkan selulosa bakteria sebagai gel anti-pemerangan daripada minuman kordial yang yang telah tamat tempohnya dalam model jus epal segar. Tahap pertama kajian adalah membandingkan penghasilan selulosa bakteria daripada minuman kordial yang sudah tamat tempohnya tanpa dan dengan penambahan ekstrak yis. Hasil kajian menunjukkan minuman kordial dengan penambahan yis menghasilkan berat selulosa basah tertinggi iaitu 140 g/L, penurunan nilai pH sebanyak 1.72 dan penurunan kepekatan gula dan protein sebanyak 12.44 mg/mL dan 0.93 mg/mL selepas 14 hari fermentasi. Gel selulosa yang dihasilkan kemudiannya digunakan sebagai gel anti-pemerangan (penambahan 0.1% asid askorbik) pada peringkat kedua kajian ini. Hasil kajian menunjukkan bahawa indeks pemerangan dalam jus epal yang mengandungi gel selulosa anti-pemerangan telah berjaya mencegah pemerangan dalam jus epal segar (15.01) berbanding dengan indeks pemerangan dalam jus epal kawalan (25.58). Kesimpulannya, selulosa bakteria dapat dihasilkan daripada minuman kordial yang telah tamat tempohnya dan berpotensi sebagai gel anti-pemerangan bagi mengurangkan pemerangan enzim dalam jus epal segar.
Bioassay-guided Isolation of Triterpene Compounds from Dillenia suffruticosa and Their Cytotoxic Activities Against Cancer Cells
Authors: Nurdin Armania , Saiful Yazan Latifah , Ismail Intan Safinar
Published: June 2022

Previous studies showed the ability of Dillenia suffruticosa to inhibit the growth of cancer cells by means of cell cycle arrest and apoptosis, thus validating the traditional use of the plant in treating cancer. Therefore, the present study was designed to isolate and elucidate the bioactive compounds responsible for the anticancer properties of D. suffruticosa extract. Bioassay-guided fractionation of the most potent fractions of DCM and EtOAc extract of D. suffruticosa was performed via column chromatography followed by purification using preparative HPLC. The structures of the isolated compounds were elucidated by NMR spectroscopy. Biological experiment by MTT assay and a series of column chromatography resulted in the isolation of three triterpene compounds. However, only the structure of compound (3) was confirmed as 1-isopropenyl-4α, 4β, 8, 10, 14-pentamethyl-icosahydro-cyclopenta[a]chrysene-3, 17-diol. Another two compounds were labelled as (1) and (2), which currently are unidentified due to unsuccessfulness in the full analysis of the spectroscopic data that enable the assignment of all protons, carbons, and confirmation of the structure. Compound (2) isolated from DCM extract of D. suffruticosa was most cytotoxic towards the selected cancer cells compared to the other two compounds and selected chemotherapeutic drugs, tamoxifen, and cisplatin. The ability of the isolated compounds to inhibit the growth of cancer cells indicates that these compounds are the bioactive constituents in D. suffruticosa that is mainly responsible for the cytotoxic activities. For this reason, these isolated compounds could be used as a means for the standardisation of herbal product from D. suffruticosa.