bluuebunny/crossref_metadata_2025 huggingface dataset:
FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
Authors: Zheng Yongsen , Chen Ziliang , Qin Jinghui , Lin Liang
Published: March 2024

The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or content that reinforces their existing dominant preferences and beliefs. This results in a lack of exposure to diverse and varied content. Many existing works have predominantly examined filter bubbles in static or relatively-static recommendation settings. However, filter bubbles will be continuously intensified over time due to the feedback loop between the user and the system in the real-world online recommendation. To address these issues, we propose a novel paradigm, Multi-Facet Preference Learning for Pricking Filter Bubbles in Conversational Recommender System (FacetCRS), which aims to burst filter bubbles in the conversational recommender system (CRS) through timely user-item interactions via natural language conversations. By considering diverse user preferences and intentions, FacetCRS automatically model user preference into multi-facets, including entity-, word-, context-, and review-facet, to capture diverse and dynamic user preferences to prick filter bubbles in the CRS. It is an end-to-end CRS framework to adaptively learn representations of various levels of preference facet and diverse types of external knowledge. Extensive experiments on two publicly available benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance in mitigating filter bubbles and enhancing recommendation quality in CRS.
Analytically Tractable Models for Decision Making under Present Bias
Authors: Akagi Yasunori , Marumo Naoki , Kurashima Takeshi
Published: March 2024

Time-inconsistency is a characteristic of human behavior in which people plan for long-term benefits but take actions that differ from the plan due to conflicts with short-term benefits. Such time-inconsistent behavior is believed to be caused by present bias, a tendency to overestimate immediate rewards and underestimate future rewards. It is essential in behavioral economics to investigate the relationship between present bias and time-inconsistency. In this paper, we propose a model for analyzing agent behavior with present bias in tasks to make progress toward a goal over a specific period. Unlike previous models, the state sequence of the agent can be described analytically in our model. Based on this property, we analyze three crucial problems related to agents under present bias: task abandonment, optimal goal setting, and optimal reward scheduling. Extensive analysis reveals how present bias affects the condition under which task abandonment occurs and optimal intervention strategies. Our findings are meaningful for preventing task abandonment and intervening through incentives in the real world.
Proportional Aggregation of Preferences for Sequential Decision Making
Authors: Chandak Nikhil , Goel Shashwat , Peters Dominik
Published: March 2024

We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. Instead of going with the most popular choice in each round, we aim for proportional representation, using axioms inspired by the multi-winner voting literature. The axioms require that every group of α% of the voters, if it agrees in every round (i.e., approves a common alternative), then those voters must approve at least α% of the decisions. A stronger version of the axioms requires that every group of α% of the voters that agrees in a β fraction of rounds must approve β⋅α% of the decisions. We show that three attractive voting rules satisfy axioms of this style. One of them (Sequential Phragmén) makes its decisions online, and the other two satisfy strengthened versions of the axioms but make decisions semi-online (Method of Equal Shares) or fully offline (Proportional Approval Voting). We present empirical results for these rules based on synthetic data and U.S. political elections. We also run experiments using the moral machine dataset about ethical dilemmas. We train preference models on user responses from different countries and let the models cast votes. We find that aggregating these votes using our rules leads to a more equal utility distribution across demographics than making decisions using a single global preference model.
Competition among Pairwise Lottery Contests
Authors: Deng Xiaotie , Gan Hangxin , Li Ningyuan , Li Weian , Qi Qi
Published: March 2024

We investigate a two-stage competitive model involving multiple contests. In this model, each contest designer chooses two participants from a pool of candidate contestants and determines the biases. Contestants strategically distribute their efforts across various contests within their budget. We first show the existence of a pure strategy Nash equilibrium (PNE) for the contestants, and propose a fully polynomial-time approximation scheme to compute an approximate PNE. In the scenario where designers simultaneously decide the participants and biases, the subgame perfect equilibrium (SPE) may not exist. Nonetheless, when designers' decisions are made in two substages, the existence of SPE is established. In the scenario where designers can hold multiple contests, we show that the SPE always exists under mild conditions and can be computed efficiently.
Machine Learning-Powered Combinatorial Clock Auction
Authors: Soumalias Ermis Nikiforos , Weissteiner Jakob , Heiss Jakob , Seuken Sven
Published: March 2024

We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most important information from bidders. However, from a practical point of view, the main shortcoming of this prior work is that those designs elicit bidders' preferences via value queries (i.e., “What is your value for the bundle {A, B}?''). In most real-world ICA domains, value queries are considered impractical, since they impose an unrealistically high cognitive burden on bidders, which is why they are not used in practice. In this paper, we address this shortcoming by designing an ML-powered combinatorial clock auction that elicits information from the bidders only via demand queries (i.e., “At prices p, what is your most preferred bundle of items?''). We make two key technical contributions: First, we present a novel method for training an ML model on demand queries. Second, based on those trained ML models, we introduce an efficient method for determining the demand query with the highest clearing potential, for which we also provide a theoretical foundation. We experimentally evaluate our ML-based demand query mechanism in several spectrum auction domains and compare it against the most established real-world ICA: the combinatorial clock auction (CCA). Our mechanism significantly outperforms the CCA in terms of efficiency in all domains, it achieves higher efficiency in a significantly reduced number of rounds, and, using linear prices, it exhibits vastly higher clearing potential. Thus, with this paper we bridge the gap between research and practice and propose the first practical ML-powered ICA.
The Moderating Effect of Instant Runoff Voting
Authors: Tomlinson Kiran , Ugander Johan , Kleinberg Jon
Published: March 2024

Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence focused on case studies and simulations. In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes.
Non-excludable Bilateral Trade between Groups
Authors: Xu Yixuan Even , Zhang Hanrui , Conitzer Vincent
Published: March 2024

Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results.
Decoding AI’s Nudge: A Unified Framework to Predict Human Behavior in AI-Assisted Decision Making
Authors: Li Zhuoyan , Lu Zhuoran , Yin Ming
Published: March 2024

With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process.
Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``nudge'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers
show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently.
TransGOP: Transformer-Based Gaze Object Prediction
Authors: Wang Binglu , Guo Chenxi , Jin Yang , Xia Haisheng , Liu Nian
Published: March 2024

Gaze object prediction aims to predict the location and category of the object that is watched by a human. Previous gaze object prediction works use CNN-based object detectors to predict the object's location. However, we find that Transformer-based object detectors can predict more accurate object location for dense objects in retail scenarios. Moreover, the long-distance modeling capability of the Transformer can help to build relationships between the human head and the gaze object, which is important for the GOP task. To this end, this paper introduces Transformer into the fields of gaze object prediction and proposes an end-to-end Transformer-based gaze object prediction method named TransGOP. Specifically, TransGOP uses an off-the-shelf Transformer-based object detector to detect the location of objects and designs a Transformer-based gaze autoencoder in the gaze regressor to establish long-distance gaze relationships. Moreover, to improve gaze heatmap regression, we propose an object-to-gaze cross-attention mechanism to let the queries of the gaze autoencoder learn the global-memory position knowledge from the object detector. Finally, to make the whole framework end-to-end trained, we propose a Gaze Box loss to jointly optimize the object detector and gaze regressor by enhancing the gaze heatmap energy in the box of the gaze object. Extensive experiments on the GOO-Synth and GOO-Real datasets demonstrate that our TransGOP achieves state-of-the-art performance on all tracks, i.e., object detection, gaze estimation, and gaze object prediction. Our code will be available at https://github.com/chenxi-Guo/TransGOP.git.
MKG-FENN: A Multimodal Knowledge Graph Fused End-to-End Neural Network for Accurate Drug–Drug Interaction Prediction
Authors: Wu Di , Sun Wu , He Yi , Chen Zhong , Luo Xin
Published: March 2024

Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI events. However, DDI events are associated with complex relationships and mechanisms among drugs, targets, enzymes, transporters, molecular structures, etc. Existing approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. Different from them, this paper proposes a Multimodal Knowledge Graph Fused End-to-end Neural Network (MKGFENN) that consists of two main parts: multimodal knowledge graph (MKG) and fused end-to-end neural network (FENN). First, MKG is constructed by comprehensively exploiting DDI events-associated relationships and mechanisms from four knowledge graphs of drugs-chemical entities, drug-substructures, drugs-drugs, and molecular structures. Correspondingly, a four channels graph neural network is designed to extract high-order and semantic features from MKG. Second, FENN designs a multi-layer perceptron to fuse the extracted features by end-to-end learning. With such designs, the feature extractions and fusions of DDI events are guaranteed to be comprehensive and optimal for prediction. Through extensive experiments on real drug datasets, we demonstrate that MKG-FENN exhibits high accuracy and significantly outperforms state-of-the-art models in predicting DDI events. The source code and supplementary file of this article are available on: https://github.com/wudi1989/MKG-FENN.
Deep Homography Estimation for Visual Place Recognition
Authors: Lu Feng , Dong Shuting , Zhang Lijun , Liu Bingxi , Lan Xiangyuan , Jiang Dongmei , Yuan Chun
Published: March 2024

Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to retrieve the candidate images, then verify the spatial consistency of matched local features for re-ranking. However, the latter typically relies on the RANSAC algorithm for fitting homography, which is time-consuming and non-differentiable. This makes existing methods compromise to train the network only in global feature extraction. Here, we propose a transformer-based deep homography estimation (DHE) network that takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching. Extensive experiments on benchmark datasets show that our method can outperform several state-of-the-art methods. And it is more than one order of magnitude faster than the mainstream hierarchical VPR methods using RANSAC. The code is released at https://github.com/Lu-Feng/DHE-VPR.
Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios
Authors: Wang Yuxin , Feng Zunlei , Zhang Haofei , Gao Yang , Lei Jie , Sun Li , Song Mingli
Published: March 2024

Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task. Recently emerged, vision-based navigation has been a promising and feasible alternative to GNSS-based navigation. However, existing vision-based techniques are inadequate in addressing flight deviation caused by environmental disturbances and inaccurate position predictions in practical settings. In this paper, we present a novel angle robustness navigation paradigm to deal with flight deviation in point-to-point navigation tasks. Additionally, we propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module to accurately predict direction angles for high-precision navigation. To evaluate the vision-based navigation methods, we collect a new dataset termed as UAV_AR368. Furthermore, we design the Simulation Flight Testing Instrument (SFTI) using Google Earth to simulate different flight environments, thereby reducing the expenses associated with real flight testing. Experiment results demonstrate that the proposed model outperforms the state-of-the-art by achieving improvements of 26.0% and 45.6% in the success rate of arrival under ideal and disturbed circumstances, respectively.
Towards Epistemic-Doxastic Planning with Observation and Revision
Authors: Engesser Thorsten , Herzig Andreas , Perrotin Elise
Published: March 2024

Epistemic planning is useful in situations where multiple agents have different knowledge and beliefs about the world, such as in robot-human interaction. One aspect that has been largely neglected in the literature is planning with observations in the presence of false beliefs. This is a particularly challenging problem because it requires belief revision. We introduce a simple specification language for reasoning about actions with knowledge and belief. We demonstrate our approach on well-known false-belief tasks such as the Sally-Anne Task and compare it to other action languages. Our logic leads to an epistemic planning formalism that is expressive enough to model second-order false-belief tasks, yet has the same computational complexity as classical planning.
Exact ASP Counting with Compact Encodings
Authors: Kabir Mohimenul , Chakraborty Supratik , Meel Kuldeep S.
Published: March 2024

Answer Set Programming (ASP) has emerged as a promising
paradigm in knowledge representation and automated reason-
ing owing to its ability to model hard combinatorial problems
from diverse domains in a natural way. Building on advances
in propositional SAT solving, the past two decades have wit-
nessed the emergence of well-engineered systems for solv-
ing the answer set satisfiability problem, i.e., finding mod-
els or answer sets for a given answer set program. In re-
cent years, there has been growing interest in problems be-
yond satisfiability, such as model counting, in the context of
ASP. Akin to the early days of propositional model count-
ing, state-of-the-art exact answer set counters do not scale
well beyond small instances. Exact ASP counters struggle
with handling larger input formulas. The primary contribu-
tion of this paper is a new ASP counting framework, called
sharpASP, which counts answer sets avoiding larger input
formulas. This relies on an alternative way of defining answer
sets that allows lifting of key techniques developed in the con-
text of propositional model counting. Our extensive empirical
analysis over 1470 benchmarks demonstrates significant per-
formance gain over current state-of-the-art exact answer set
counters. Specifically, by using sharpASP, we were able to
solve 1062 benchmarks with PAR2 score of 3082 whereas
using prior state-of-the-art, we could only solve 895 bench-
marks with PAR2 score of 4205, all other experimental con-
ditions being the same.
MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs
Authors: Liang Ke , Meng Lingyuan , Zhou Sihang , Tu Wenxuan , Wang Siwei , Liu Yue , Liu Meng , Zhao Long , Dong Xiangjun , Liu Xinwang
Published: March 2024

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs. Besides, the enclosing subgraph extraction in most GraIL-based models restricts the model from extracting enough discriminative information for reasoning. Consequently, the expressive ability of these models is limited. To address the problems, we propose a novel GraIL-based framework, termed MINES, by introducing a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Concretely, the message intercommunication mechanism is designed to capture the omitted hidden mutual information. It introduces bi-directed information interactions between connected entities by inserting an undirected/bi-directed GCN layer between uni-directed RGCN layers. Moreover, inspired by the success of involving more neighbors in other graph-based tasks, we extend the neighborhood area beyond the enclosing subgraph to enhance the information collection for inductive relation reasoning. Extensive experiments prove the promising capacity of the proposed MINES from various aspects, especially for the superiority, effectiveness, and transfer ability.
Non-flat ABA Is an Instance of Bipolar Argumentation
Authors: Ulbricht Markus , Potyka Nico , Rapberger Anna , Toni Francesca
Published: March 2024

Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries.
A common restriction imposed on ABA frameworks (ABAFs) is that they are flat, i.e. each of the defeasible assumptions can only be assumed, but not derived. While it is known that flat ABAFs can be translated into abstract argumentation frameworks (AFs) as proposed by Dung, no translation exists from general, possibly non-flat ABAFs into any kind of abstract argumentation formalism.
In this paper, we close this gap and show that bipolar AFs (BAFs) can instantiate general ABAFs. To this end we develop suitable, novel BAF semantics which borrow from the notion of deductive support. We investigate basic properties of our BAFs, including computational complexity, and prove the desired relation to ABAFs under several semantics.
NUMERICAL THREE-DIMENSIONAL MODEL OF ULTRASONIC COAGULATION OF AEROSOL PARTICLES IN VORTEX ACOUSTIC STREAMING
Authors: Golykh Roman N. , Khmelev Vladimir N. , Shalunov Andrey V.
Published: March 2024

Separation of highly dispersed systems with huge liquid-gas or liquid-solid interfaces is relevant for practical tasks of gas purification from the most highly dispersed and difficult-to-detect dispersed fraction PM2.5, and separation of nanoparticles (including their small agglomerates) in fine chemical technology processes. One of the most effective ways to separate highly dispersed systems with a large interface surface is to combine each of the closed subsurfaces (surfaces of individual dispersed particles) under the influence of hydrodynamic effects
in the gas phase, arising both near the interface surfaces and at a considerable distance from them, due to the
superposition of ultrasonic vibrations. Since the efficiency of ultrasonic coagulation decreases with a large distance
between closed subsurfaces from each other in PM2.5 aerosol and the small size of these surfaces, it is necessary
to create conditions for the emergence of new nonlinear effects that contribute to the local compaction of the
dispersed fraction. In a resonant and significantly inhomogeneous ultrasonic field (with a scale of inhomogeneity on the order of the wavelength), vortex acoustic flows arise, which, due to inertial forces, locally compact the dispersed phase in the form of an increase in the concentration of aerosol particles. A numerical model of ultrasonic coagulation of PM2.5 aerosol particles in three-dimensional (3D) vortex acoustic streaming is proposed in this paper. The model is designed to identify the possibility of increasing the efficiency of ultrasonic coagulation in 3D streaming by virtue of the following mechanisms: (1) local increase in concentration caused by the inertial transfer of particles to the periphery of 3D vortices in the gas phase; (2) increase in the frequency of particle collisions due to 3D turbulent disturbances in ultrasonic fields; and (3) increase in productivity and ensuring
uninterrupted implementation of the process in a flow mode owing to transfer of particles between the streamlines
of the main vortices initiated by ultrasonic vibrations. The listed mechanisms for increasing the efficiency of
coagulation in 3D streaming are taken into consideration by introducing two stream functions, considering
turbulent chaotic disturbances of the flow resulting in dispersion of particle velocities. It was possible to establish based on numerical analysis of the model using the example of PM2.5 that laminar vortex flows begin to influence at sound pressure level from 160-165 dB, and turbulent disturbances make an additional contribution in the range of sound pressure levels from 140-170 dB. At the same time, as a result of 3D turbulent disturbances, the
efficiency of coagulation reaches almost 100% at a sound pressure level 5 dB lower than with laminar flows (sound
pressure amplitude, 3 times lower).
Chemical Composition and Antimicrobial Properties of Morchella crassipes (Ascomycota) from Kashmir Valley (India)
Authors: Talie Mehrajuddin , War John Mohd , Nisa Anees Un , Dar Asif Hamid , Wani Abdul Hamid , Bhat Mohd Yaqub
Published: March 2024

In the current era, wild macrofungi are being focused for developing and overing novel bioactive compounds for the management of agricultural, horticultural, and other infectious diseases. In that view, current research work was designed to evaluate the biochemical composition and medicinal properties of Morchella crassipes mushroom. The mycochemical screening of aqueous extract exposed the incidence of glycosides, free amino acids and proteins, alkaloids, carbs, flavonoids, terpenoids, phenolic compounds and tannins, except volatile oils, resins, steroids, and anthraquinones. However, hexane extract exhibited the occurrence of glycosides, alkaloids, volatile oils, steroids and terpenoids while as all other phytochemicals were not detected. The gas chromatography mass spectrometry profiling has disclosed the identification of three predominant naturally occurring bioactive volatile monoterpenoids, namely neral, citral, and epoxy-linalool oxide with well-known biological activities. The methanolic extract resulted in strong antifungal efficacy against the tested fungal strains such as Penicillium chrysogenum (20.33 ± 0.57 mm) followed by Pythium ultimum (15.33 ± 0.76 mm) and Aspergillus niger (12.50 ± 0.50 mm) at highest concentrations. Likewise, marked antibacterial effects were reported in case of Staphylococcus aureus (15.16 ± 0.76 mm), followed by Salmonella gallinarum (14.33 ± 0.57 mm) and Escherichia coli (13.66 ± 0.57 mm), respectively. This data may offer baseline information regarding the bioactive metabolites and opening new ways for conducting trails to find natural management strategies to combat multi drug resistant pathogens in horticulture, agriculture, and aquaculture.
Diffinio Ymgysylltiad Rhieni ag AGA: o berthnasoedd i bartneriaethau
Authors: Goodall Janet , Lewis Helen , Clegg Zoe , Ylonen Annamari , Wolfe Chris , Owen Sion , Hughes Carl , Williams Margiad , Roberts Delyth , Ramadan Ibtihal
Published: March 2024

Mae’r papur hwn yn cyflwyno golwg unigryw ar werth canfyddedig ymgysylltiad rhieni â dysgu plant o fewn Addysg Gychwynnol Athrawon (AGA) yng Nghymru, yr ymchwiliad cyntaf o’i fath. Mae’n deillio o brosiect ymchwil dan nawdd Llywodraeth Cymru gan dimau o Brifysgolion Abertawe a Bangor. Mae’r papur yn adrodd barn darparwyr AGA, athrawon dan hyfforddiant, mentoriaid, rhieni a rhanddeiliaid allanol, ynglŷn â’u profiadau yn ystod cyfnod clo’r pandemig. Canfu’r ymchwil, er bod yr holl randdeiliaid yn gwerthfawrogi ymgysylltiad rhieni, roedd diffyg cysondeb ynghylch sut yr oedd hyn yn cael ei ddiffinio a’i weithredu. Mae’r papur yn cynnig datgysylltiad rhwng damcaniaeth fabwysiedig a damcaniaeth ar waith, o ran ymgysylltiad rhieni â dysgu plant, ac yn gorffen gydag argymhellion ar gyfer polisi, ymarfer ac ymchwil bellach.
Development of unsupported IrO2 nano-catalysts for polymer electrolyte membrane water electrolyser applications
Authors: Karels Simoné , Felix Cecil , Pasupathi Sivakumar
Published: March 2024

IrO2 is a current state-of-the-art catalyst for polymer electrolyte membrane water electrolyser (PEMWE) applications due to its high stability during the oxygen evolution reaction (OER). However, its activity needs to be significantly improved to justify the use of such a high-cost material. In this study, the activity of the IrO2 catalyst was improved by optimising and comparing two synthesis methods: the modified Adams fusion method (MAFM) and the molten salt method (MSM). Optimum OER performances of the IrO2 catalysts synthesised with the two synthesis methods were obtained at different temperatures. For the MAFM, a synthesis temperature of 350 °C produced the IrO2 catalyst with an overpotential of 279 mV and the highest OER stability of ~ 82 h at 10 mAcm−2. However, for the MSM, the lowest overpotential of 271 mV was observed for IrO2 synthesised at 350 °C, while the highest stability of ~ 75 h was obtained for the IrO2 synthesised at 500 °C.
AgERA5 representation of seasonal mean and extreme temperatures in the Northern Cape, South Africa
Authors: Kruger Jacobus A. , Roffe Sarah J. , van der Walt Adriaan J.
Published: March 2024

Over regions with sparse observation networks, including South Africa’s Northern Cape Province, gridded data sets represent valuable supplementary data sources enabling spatially detailed climate investigations. Their performance is, however, influenced by regional characteristics, thus a performance assessment should be a prerequisite for any regional application. Through a pairwise comparison with eight point-based temperature records, we evaluated the AgERA5 data sets representation of mean summer (November–March; Tms) and winter (May–September; Tmw) temperatures and respective seasonal heatwave and coldwave characteristics across the Northern Cape for 1980–2020. Correlations ranging from 0.48 to 0.92 for Tms and from 0.38 to 0.94 for Tmw reflect relatively strong, but varying, temporal correspondence between the AgERA5 data and stations. Low biases, averaging −0.08 (0.17) °C and ranging from -0.79 to 2.10 (-0.40 to 1.47) °C for Tms (Tmw) were evident. Biases for the heatwave (coldwave) magnitudes were low, averaging -0.38 (0.19) °C2, and ranging from -1.55 to 1.47 (-2.05 to 2.91) °C2. Biases for the heatwave (coldwave) frequency were also low, but typically overestimated, averaging 1.19 (0.73) days, and ranging from -1.33 to 5.60 (-1.61 to 3.39) days. Biases for the heatwave (coldwave) number were low and typically overestimated, averaging 0.27 (0.08) events, and ranging from -0.28 to 1.40 (-0.39 to 0.39) events. Despite some stations depicting consistently poor performance, the study results support further application of the AgERA5 product for spatiotemporal analyses of mean and extreme temperatures across the Northern Cape, provided limitations are adequately acknowledged. Further application of the fine-resolution AgERA5 product will greatly inform impact-based studies exploring mean and extreme temperature influences over the Northern Cape Province.
Foreign language effect in everyday moral decision-making
Authors:   , Dumančić Francesca
Published: March 2024

The moral foreign language effect (FLE) is a term that refers to differences in the outcomes of the moral decision-making process depending on whether the process was conducted in the native language or in a foreign language. This study focuses on the FLE in the field of moral decisionmaking and aims to develop new moral dilemmas that describe realistic situations that an average person might encounter in his or her daily life. Next, the goal of the current research was to examine whether the magnitude of moral transgression and L2 dominance affect the magnitude of moral FLE. Another goal was to gain more nuanced insight into the mechanism of moral FLE. To this end, the CNI model of moral decision making was used. Finally, this is the first study in the field to test moral emotions as mediators of the relationship between the language in which moral dilemmas are presented and concern for moral norms and for consequences. We expected that participants in the non-dominant language condition would make more utilitarian decisions than participants in the dominant language condition. We also expected that participants in non-dominant language condition would have less regard for moral norms and consequences. We expected all of these differences to be larger for moderate moral transgresions than for mild or severe ones and to be larger as L2 dominance decreased. Finally, we expected that reading moral dilemmas in a less dominant language will lead to weaker activation of moral emotions, which in turn would lead to lower concern for both moral norms and consequences. In two experiments with participants from two different cultures and in three different language pairs, the newly constructed moral dilemmas proved to be valid. Otherwise, our hypotheses were generally not supported. Although the results were not in line with the expectation, the results suggest that future research should consider language dominance in addition to language proficiency, as non-dominant languages with different degrees of dominance were found to affect concern for norms and concern for consequences in different ways. The results also suggest that it would be useful to develop new moral dilemmas to include even more serious moral transgressions.
Political integration mechanisms in Hungary (2010–2022)
Authors: Gerő Márton , Szabó Andrea
Published: March 2024

Since 2010, Fidesz has won four electoral victories in Hungary with a constitutional majority. In this paper, we argue that the main reason behind this overwhelming electoral success is the specific pattern of political integration that has evolved in recent years. The Hungarian case, as a consequence, may also act as a basis for a theoretical step forward in understanding the role of political integration in de-democratization processes. To understand the role of political integration, we explain how the Habermas-based political integration framework relates to the types of political culture, and then, with the help of representative data collection from 2018 and 2021, we define the integration groups and, using these, examine the party preferences and participation patterns that have developed in today’s Hungary. The Orbán regime successfully generates diffuse mass support: this is embedded in multiple social groups, although none of the integration groups can be considered the sole or primary supporters of the system.
Social structure and integration: Occupational classes and integration mechanisms between 2015 and 2021
Authors: Kovách Imre , Kristóf Luca
Published: March 2024

The topic of the paper is the relationship between social stratification based on occupational classes and the mechanisms of social integration, which was analysed on quantitative data collected in 2015, 2018 and 2021. We sought answers to how we can better understand the interaction of social and integration positions, as well as the functioning of complex integration mechanisms.  In the examined six years, a definite homogenization took place in the integration indicators of the occupational groups: this means that indicators measured of occupational group in some integration dimensions took on closer values.  It is also part of our research results that there is an interpretable correlation between the hierarchy of occupational groups and the examined integration mechanisms: political participation, number of weak ties, subjective social exclusion and acceptance of norm violation. The upper strata of the occupational class model (mostly entrepreneurs, managers, professionals and other white-collar workers) have consistently reported higher numbers of weak ties, lower sense of exclusion and higher political participation. We also found consistent, yet opposite results in the lower strata, mostly among the unemployed and those with unskilled and semi-skilled work linked to lower qualifications. The strengthening of homogenization and the impact of the hierarchy of occupational groups are simultaneous integrational characteristics of Hungarian society. The future research question is whether homogenization rearranges the integration effect of the occupational hierarchy.
APPLICATION OF THE GARDEN CITY CONCEPT IN THE HOUSING AREA OF THE NEW CITY OF PARAHYANGAN
Authors: Hashartyadi Himawan
Published: March 2024

The community's need for housing has increased in line with population growth. Kota Baru Parahyangan Housing is a residential area in West Bandung Regency. Kota Baru Parahyangan is a satellite city whose urban development and planning have a structured and interesting concept. The planning concept carries the Garden City concept as an effort towards sustainable and environmentally friendly housing. The purpose of this study was to determine the extent of the application of the Garden City concept in the residential area of Kota Baru Parahyangan. The research method used is descriptive qualitative with data collection methods based on observation and literature review. Based on the analysis of existing data, the Garden City concept has been implemented quite well because it is in accordance with its main principles. Keywords: Garden City; Residential Areas; Parahyangan New Town.
Demographic, Clinical and Language Characteristics of Malaysian Patients with Aphasia: A Retrospective Study
Authors:   Speech Pathology Programme, School of Health Sciences Universiti Sains Malaysia (Health Campus), 16150 Kubang Kerian, Kelantan, Malaysia.
Published: March 2024

Limited research has been conducted in Malaysia on the characteristics of patients with aphasia. To address this gap, a retrospective study was conducted at Hospital Rehabilitasi Cheras (HRC). The study aimed to profile the demographic, clinical, and language characteristics of patients who underwent speech therapy for aphasia. Medical records from 2014 to 2017 were reviewed, focusing on 1,259 adult patients who were referred to the Speech Therapy Unit. Analysis included demographics, clinical aspects, and linguistic features. The Western Battery of Aphasia-Revised (WAB-R), a comprehensive assessment tool, was used to evaluate the language abilities. Among the patients' files analysed, a total of 211 contained WAB-R test results. The demographic profile revealed a higher proportion of male patients compared to females, with Malay patients comprising the majority, followed by Chinese and Indians. Age distribution included 41.7% older adults (aged 55 and above), 37.9% middle-aged (aged 36-55), and 20.4% younger adults (aged 18- 35). Notably, 44.5% had completed high school education. Clinically, right- sided hemiparesis was prevalent as noted with 67.8% of patients, while 14.7% had left-sided hemiparesis. The primary cause of aphasia was stroke, accounting for 71.1% of cases, with ischemic stroke being the dominant subtype (56.9%). Fluent aphasia was presented in 50.2% of the cases, compared to 49.3% of non-fluent aphasia. Anomic aphasia was the most common subtype (35.5%), followed by global aphasia (27.5%) and Broca’s aphasia (17.5%). The language profile provided a detailed description, encompassing linguistic aspects relevant to different aphasia types. These preliminary findings yielded valuable insights and hold significant implications for the clinical management of aphasia patients in Malaysia.
Critical Thinking Level among Nursing Students of Faculty of Medicine UKM
Authors:   Department of Medical Education, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, 56000 Kuala Lumpur, Malaysia
Published: March 2024

Critical thinking plays an important role in decision-making and implementing daily tasks which includes searching, obtaining, evaluating, analysing, synthesising and conceptualising information. The aim of this study was to identify and compare the level of critical thinking among nursing students in Faculty of Medicine UKM based on systematic analysis, thinking in and out of the box domains. This was a cross-sectional study that utilised a self-administered Google Form online questionnaire on the level of critical thinking levels and domains. There were 115 nursing students in the Faculty of Medicine, UKM comprising of Year 1 (34.9%), Year 2 (26.1%), Year 3 (16.5%) and Year 4 (23.5%). The result showed that overall level of critical thinking was at intermediate level (mean=3.815, s.d=0.431) with systematic analysis domain was at intermediate level (mean=4.027, s.d=0.522), thinking in the box domain at low level (mean=2.547, s.d=0.688) and thinking out of the box domain was at intermediate level (mean=4.181, s.d=0.600). Further analysis showed that there was significant difference between students Year 2 with Year 3 and Year 3 with Year 4 in thinking out of the box domain (p=0.006). It was shown that critical thinking skills among nursing students were intermediate level.
The Reliability of 44-Item Big Five Personality Inventory (Malay Version) to Measure Readiness to Breastfeed
Authors:   Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Ya’acob Latiff, Bandar Tun Razak, Cheras, Kuala Lumpur, Malaysia
Published: March 2024

The 44-item Big Five Personality Inventory (BFI-44) helps in simplify a wide range of complex personality features, including how personality affects breastfeeding. The aim of this study was to examine the reliability of the Malay version of the BFI-44. A cross-sectional study was conducted using the convenience sampling of female respondents. A total of 96 female public university students participated in this study. Cronbach’s alpha, intraclass correlation coefficient, limit of agreement, and standard error of measurement (SEM) were used in determining the reliability of the questionnaire. Cronbach’s alpha value for Extraversion subscale obtained the highest internal consistency (0.723) and the lowest internal consistency was obtained in the subscale of Openness to Experience (0.543). The intraclass correlation coefficient (ICC) value for all dimensions exceeded 0.70 (ICC= 0.792-0.883). The Bland-Altman plots showed high reliability in all dimensions except Neuroticism. For SEM, Extraversion scored the lowest (0.773) while Neuroticism scored the highest (1.199). Overall, the BFI-44 in Malay version had moderate to good internal consistency and igh test-retest reliability. These findings may give positive implications for the implementation of the questionnaire in future evaluation of Malaysians’ personality traits
Why Don’t Heterosexual New Alternative Masculinities Choose Enchained Women for Either Sporadic or Long-Term Relationships?
Authors: Canal-Barbany Josep Maria , Cañaveras Paula , Zubiri-Esnaola Harkaitz , Gómez Aitor
Published: March 2024

Research on the reproduction of disdainful and toxic models of heterosexual relationships shows their negative effects on people’s health status. Contrarily, the preventive socialization process illustrates the development of alternative relationships, which are directly related to egalitarian, passionate and non-violent values and have been demonstrated to produce positive effects on health. The present study focuses on the socialization process, analyzing how men choose their affective and sexual relationships and examining the influence of the new alternative masculinities (NAM) approach on these choices. The defining characteristics of these men are self-confidence, solidarity, and strength to face violence and any discrimination. The investigation has used a qualitative methodology with a communicative orientation. Firstly, seven semi-structured interviews with men who follow the NAM’s approach have been conducted. Secondly, a content analysis has been conducted focused on discourses and messages shaped on internet forums and songs about how men explain their choices and interests in their relationships. The results reveal the existence of men who prefer women that get excited with egalitarian men and not with chauvinist and violent men. These kinds of choices show an improvement in heterosexual men’s affective and sexual relations as well as in their health status.
Agency in Silence: The Case of Unaccompanied Eritrean Refugee Minors in the Netherlands
Authors: Kusmallah Nebil , Ghorashi Halleh
Published: March 2024

Following the so‐called refugee crisis, unaccompanied refugee minors (URMs) from Eritrea were portrayed negatively in Europe. Although such portrayals are often amplified by media and policy discourses, the main reasons for this negative view were a lack of understanding of URMs’ subjectivities, the institutional silencing process they face in their everyday lives, and the ways they show agency in such precarity. This article addresses institutional silencing practices that Eritrean URMs encounter and the various ways they engage with them. Using data gathered during 2016–2018 from Eritrean URMs in the Netherlands, we explore how participants navigate the exclusionary processes they encounter in relation to institutions, such as refugee reception centres, refugee protection organizations, immigration authorities, and schools. Inspired by Sherry Ortner’s and Saba Mahmood’s work, we show the importance of less dominant forms of agency (delayed or docile forms) in how URMs engage with the power of institutional silencing practices. We then show the (often unseen) agency of these young people as the desire of the “less powerful” or “less resourceful” to “play their own serious games even as more powerful parties seek to devalue and even destroy them” (Ortner, 2006, p. 147).