Levels Of Representation Research Articles

Gendered Backlash Depends on the Context. Reassessing Negative Campaigning Sanctions Against Female Candidates via Large-Scale Comparative Data

ABSTRACT On paper, women politicians should be punished more than men when they go negative during election campaigns. Yet, empirical evidence in this sense is surprisingly scarce, and findings offer a muddled picture. In this article, we argue that existing research has, so far, neglected to consider a fundamental intervening factor: the role of the context, and specifically the level of descriptive representation of women in politics. Using novel large-scale data for 700+ candidates having competed in 150+ elections in 94 countries worldwide, we test – and confirm – the expectation that women are especially punished for negative campaigning in countries with low descriptive representation of women in politics. This pattern does not hold in more gender-equal contexts. Additional observational evidence from the Comparative Study of Electoral Systems (CSES) shows that this electoral punishment is likely due to voters expressing lower likability of women candidates (compared to men) when the former go negative in countries where women are less established in political leadership positions. We suggest that inconsistent results in previous studies are likely due to the lack of comparative focus.

Casual inference-enabled graph neural networks for generalized fault diagnosis in industrial IoT system

Data-driven fault diagnosis plays a crucial role in diagnosing the operational status within the Industrial Internet of Things (IIoT) systems. Although Graph Neural Networks (GNNs) have recently gained traction in fault diagnosis by adeptly modeling complex dependencies present in high-dimensional sensor measurements, they still grapple with the challenges presented by varying working conditions and pervasive environmental noise, which can significantly hinder their generalization capabilities. Hence, we propose Causal Inference-Enabled Graph Neural Networks (CIE-GNN) for generalized fault diagnosis in large-scale IIoT systems. Specifically, we establish a structural causal model for the GNN-based fault diagnosis model, revealing that the non-causal factors lead to spurious correlations and act as the confounders, thus impairing the generalization performance of fault diagnosis models. To rectify this issue, we design the disentangled transformation module and the causal disentanglement regularization based on mutual information minimization strategy, facilitating the effective decoupling between the fault-causal factors and the non-causal factors in the representation level. Additionally, we propose the random pairing-based backdoor adjustment regularization to mitigate the negative effects of non-causal factors. Extensive experiments and rigorous theoretical analysis validate the generalization capabilities of CIE-GNN across diverse working environments.

Mental Models in Chemistry Concept: A Systematic Review

Mental models in chemistry concepts are defined as student’s comprehension of three levels of chemical representations, including macroscopic, submicroscopic, and symbolic levels. This study aims to identify, review, and evaluate research concerning mental models in chemistry concepts through specific research questions. The research method used is a systematic literature review (SLR) by analyzing relevant articles from Google Scholar, Eric, Scopus, and Crosscref, focusing on publications from the the past decade (2013–2023). A total of 52 articles were obtained from the analysis based on the inclusion and exclusion criteria. The findings indicate that most studies aim to understand students' mental model profiles related to chemistry concepts. High school students are predominantly involved as research samples. The most used data collection tool is diagnostic tests. Mental models are primarily studied in physical chemistry, particularly in chemical equilibrium, adopting various theories, especially the Sendur, Toprak, and Pekmez (2010) model. Factors influencing the formation of students' mental models include internal factors such as prior knowledge, experiences, attitudes, and motivations, and external factors such as the environment, incomplete textbooks, and inappropriate teaching strategies.

The Frontier Nature of Nostalgic Memory

The research is devoted to nostalgia, understood as an existential and a form of historical memory. The timeless relevance of the problem, which is becoming particularly acute in the space of modern mass culture, the culture of the globalized world, is highlighted. The problem is expressed by the contradiction between the orientation of modern culture towards the future and nostalgic intentions in various spheres of social life. This problem includes the question of the role of nostalgia in the life of the subject. The purpose of the study is to determine the ontological foundations of the modern “cultural turn” to the past in social psychology, and to identify the levels, features and models of nostalgic experiences. Four levels of nostalgia representation in culture are highlighted: individual, group, national and universal. The most recognizable features of nostalgia as a form of memory are considered, such as the existential grasp of being in a nostalgic experience, the non‑narrative, symbolic, mythological nature of fixed images of the past, striving for metaphysical authenticity, awareness of the impossibility of returning to the object of longing and simultaneous awaiting of the future, as well as transcending the boundaries of individual being. It is concluded that nostalgia can be both a tool for preserving the existing social order – and changing it, manifesting itself in the form of a static or dynamic model of experiencing longing for the past. The article is intended for researchers of the problems of mass culture, historical memory, and identity of a social subject.

Culturally Tailored Messages and Trial Registry Enrollment

Marginalized populations have lower levels of clinical trial representation than other populations. Tailoring recruitment materials and providing incentives may improve representation. To determine whether culturally tailored video improves parents' decision to enroll (DTE) Black children in a research registry. A randomized clinical trial, using mixed methods and consecutive sampling, was conducted in an urban primary care pediatric clinic. A post hoc study assessed the association of a monetary incentive with DTE. Ninety-three parents were recruited before their child's scheduled clinic visit. Participants completed a survey during which they were randomly assigned to review a paragraph (control group), generic video, or tailored video and received a gift card. Video watchers completed questions about its effectiveness. All participants were invited to join the registry. In the post hoc study, consenting participants followed the control group protocol and the coordinator promised them an ink pen for participating. The study was conducted between November 15, 2022, and August 29, 2023. A survey regarding the importance of being included in research studies was administered after participants watched a culturally tailored video. The primary outcome was DTE. Covariates were message effectiveness, clinical trial knowledge, and trust. Intention-to-treat analysis was performed. A total of 125 individuals were included. All groups had similar mean (SD) parent age (overall, 32.6 [7.35] years), gender (110 [88.0%] women), and race (116 [92.8%] Black or African American). There was no significant difference in the odds of DTE between the appeal video and control (odds ratio [OR], 1.19; 95% CI, 0.38-3.72) or between the generic video and control (OR, 1.91; 95% CI, 0.54-6.71). However, DTE was higher for all 3 gift card groups vs the ink pen incentive group (eg, control vs ink pen incentive: OR, 5.01; 95% CI, 1.68-14.95). After adjusting for trust, monetary incentives were associated with higher odds of DTE (adjusted OR, 5.92; 95% CI, 2.44-14.39). In this randomized clinical trial, targeted messaging was not associated with a higher decision to enroll in a pediatric clinical trial registry. There may be a role for combining holistic messaging with monetary incentives to improve enrollment. ClinicalTrials.gov Identifier: NCT06138145.

Revisiting tokenism: impact of gender diversity on corporate social performance of Indian companies

PurposeThis study aims to examine the relationship between corporate governance, in particular board diversity, and corporate social performance (CSP) amongst the top 500 listed companies in India.Design/methodology/approachData from the top 500 listed Indian companies constituting the BSE500 index for a five-year period from 1 April 2014 to 31 March 2019 are used to test the impact of the proportion of women directors, proportion of women independent directors, female CEOs and other corporate governance variables on CSP.FindingsThe results of panel regression analysis suggest that firms characterised by high proportions of women directors, independent women directors or a female CEO spend more on corporate social responsibility (CSR) activities. Furthermore, CEO duality, board size and board independence are positively related to social performance. These findings are of relevance to the policymakers and board of directors who are engaged in meeting corporate governance requirements.Practical implicationsThis is one of the initial studies to document the impact of executive-level female representation on CSP following India's 2013 regulations on mandatory women representation on boards and CSR expenditure. The study reveals that greater gender diversity on corporate boards significantly boosts CSP, offering strategic advantages in governance and CSR. The study offers practical benefits for various stakeholders including corporate regulators, policymakers and corporate managers.Originality/valueThe paper contributes to the corporate governance and CSR literature by showing that good governance practices and high women representation on boards promote social performance. Our study is one of the preliminary efforts to document the level and impact of female representation at the executive level on CSP after the regulation of minimum women representation on corporate boards and mandatory CSR expenditure requirement introduced in India in 2013.

Assessment of age-related features of the condition of heavy-duty truck drivers based on spectral analysis of EEG-ECG and stress tests during mass occupational examinations

The authors studied the physiological characteristics of drivers of heavy-duty coal mine vehicles based on spectral analysis of EEG-ECG and stress tests, which have prognostic value in professional activity. The study aims to assess the functional condition of heavy-duty truck drivers of different age groups based on a comprehensive spectral analysis of EEG-ECG under occupational examinations. During the survey, the authors have studied the spectral parameters of the EEG‒ECG in 300 male drivers aged 25 to 66 years. Specialists have performed EEG registration when placing electrodes in the 10–20 system. The EEG analysis was performed with a preliminary visual analysis followed by the removal of artifacts. The scientists assessed the level of representation of the main alpha rhythm and its spatial distribution. The ECG registration was in the first lead. The duration of EEG-ECG registration is about 15 minutes in a typical sequence of stress tests: baseline, rhythmic photostimulation, hyperventilation with recovery periods. The spectral analysis of the EEG‒ECG was performed automatically on a Neurotravel device (Italy). The authors found lower alpha and delta activity in the initial EEG spectra in older drivers aged 45–66 years, which contributed to the cumulative effect of stress load. Light stress stimulation significantly reduced alpha and delta activity and increased beta EEG activity in both groups, while in the older group the high-frequency range significantly exceeded the low-frequency range. Simultaneously with the changes in the EEG, stress photostimulation increased the energy of the ECG spectra in all frequency ranges. The authors established the appearance of additional stress indicators during light stimulation in older drivers based on the correlation of EEG‒ECG spectra (alpha/delta) and the absence of such changes in the group of young drivers aged 25–35 years. Moreover, the EEG–ECG correlation coefficients and their critical values increased in groups in increments of 6 years — from 35 to 65 years. Elderly drivers, based on neuro-cardiodynamic indicators, were in a state of chronic stress and the resulting risks to health and professional activity, and the identified physiological mechanisms of stress may be at the heart of ways to prevent it. Limitations. The study is limited to the assessment of EEG and ECG indicators in 300 heavy truck drivers. Ethics. The study was performed using non-invasive methods and approved by the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, Protocol No. 5, § 1 dated 12/26/2018, in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting medical research with human participation as a subject" as amended in 2013 and the "Rules of Good Clinical Practice" approved by by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. All the examined patients signed a voluntary informed consent to participate in the study.

ICPE 2022 Special Issue (Part 4) Editorial

The International Conference on Physics Education 2022 was themed "Physics Education: Preparing for the Future." This special issue, contains six articles elaborating on presentations from the conference. What they share is the introduction of new methods and didactic approaches in the field of physics education, along with corresponding studies. In "The Use of Analogies by Future Physics Teachers in Conducting Supervised Internship Activities," F.W. Parma, M.H.A. Vieira, and R. Nardi aimed to investigate how future physics teachers employed analogies during a supervised internship. Shintsuruta, T. Iwayama, and H. Okubo provide an exciting insight into physics education research (PER) in the field of electricity. In Japan, the shape dependence of electrical resistance is effectively taught in senior high schools. The authors present design and practice lessons that illustrate and help students understand the concept. Escape rooms are playing an increasingly important role as a leisure activity for teenagers (and adults). T. Sukelová and K. Velmovská address the motivational aspect of escape rooms and integrate it into the learning of physics. Their article impressively demonstrates that a forward-looking physics education must explore new paths to be both engaging and effective! "A Study of Grade Eleven Students’ Representations of Electricity Through Model-Based Inquiry" by W. Boonmak and J. Huntula arises from the conditions prevailing during COVID-19. The pandemic forced schools to close and transition to online learning. The authors from Thailand researched whether students achieve a higher level of representation after learning via an online learning platform through the Model-Based Inquiry (MBI) method and how they can be supported. In the context of physics, this study once again pertains to the topic area of electricity. Physics education takes place all over the world. Thus, "The Use of Research Findings by Undergraduate Physics Education Programs in Angola" enriches the previously presented studies with insights from Africa. The authors, J. Oliveira and R. Nardi, present the findings of a pilot study that aimed to determine whether scholarly literature—such as journal articles, monographs, dissertations, and theses—is considered in the education of physics teachers in Angola. Finally, E. Blanquet and D. Hennequin from France present "Identifying First-Year Science Students’ Difficulties: Results From The Online French Physical Society Questionnaire," a large-scale study involving 5,891 first-year science students from various French tertiary education tracks (universities, preparatory classes for engineering schools, technological university institutes, etc.) conducted over nine years. The goal was to document the understanding of physics concepts and the mastery of basic mathematical tools after completion of the baccalauréat (French high school exit examination). The articles vividly demonstrate how new paths are being pursued internationally to modernize, enhance, and future-proof the teaching and learning of physics. They also highlight the necessity of physics education research to make informed statements about the effectiveness of these new approaches. I would like to thank all the authors and their teams for their inspiring insights into new paths and scientific investigations. May these contributions encourage many educators worldwide to embark on the path toward a future-oriented physics education!

Convolutional neural networks uncover the dynamics of human visual memory representations over time.

The ability to accurately retrieve visual details of past events is a fundamental cognitive function relevant for daily life. While a visual stimulus contains an abundance of information, only some of it is later encoded into long-term memory representations. However, an ongoing challenge has been to isolate memory representations that integrate various visual features and uncover their dynamics over time. To address this question, we leveraged a novel combination of empirical and computational frameworks based on the hierarchal structure of convolutional neural networks and their correspondence to human visual processing. This enabled to reveal the contribution of different levels of visual representations to memory strength and their dynamics over time. Visual memory strength was measured with distractors selected based on their shared similarity to the target memory along low or high layers of the convolutional neural network hierarchy. The results show that visual working memory relies similarly on low and high-level visual representations. However, already after a few minutes and on to the next day, visual memory relies more strongly on high-level visual representations. These findings suggest that visual representations transform from a distributed to a stronger high-level conceptual representation, providing novel insights into the dynamics of visual memory over time.

Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

CE-DCVSI: Multimodal relational extraction based on collaborative enhancement of dual-channel visual semantic information

Visual information implied by the images in multimodal relation extraction (MRE) usually contains details that are difficult to describe in text sentences. Integrating textual and visual information is the mainstream method to enhance the understanding and extraction of relations between entities. However, existing MRE methods neglect the semantic gap caused by data heterogeneity. Besides, some approaches map the relations between target objects in image scene graphs to text, but massive invalid visual relations introduce noise. To alleviate the above problems, we propose a novel multimodal relation extraction method based on cooperative enhancement of dual-channel visual semantic information (CE-DCVSI). Specifically, to mitigate the semantic gap between modalities, we realize fine-grained semantic alignment between entities and target objects through multimodal heterogeneous graphs, aligning feature representations of different modalities into the same semantic space using the heterogeneous graph Transformer, thus promoting the consistency and accuracy of feature representations. To eliminate the effect of useless visual relations, we perform multi-scale feature fusion between different levels of visual information and textual representations to increase the complementarity between features, improving the comprehensiveness and robustness of the multimodal representation. Finally, we utilize the information bottleneck principle to filter out invalid information from the multimodal representation to mitigate the negative impact of irrelevant noise. The experiments demonstrate that the method achieves 86.08% of the F1 score on the publicly available MRE dataset, which outperforms other baseline methods.

Attention enhanced machine instinctive vision with human-inspired saliency detection

Salient object detection (SOD) enables machines to recognize and accurately segment visually prominent regions in images. Despite recent advancements, existing approaches often lack progressive fusion of low and high-level features, effective multi-scale feature handling, and precise boundary detection. Moreover, the robustness of these models under varied lighting conditions remains a concern. To overcome these challenges, we present Attention Enhanced Machine Instinctive Vision framework for SOD. The proposed framework leverages the strategy of Multi-stage Feature Refinement with Optimal Attentions-Driven Framework (MFRNet). The multi-level features are extracted from six stages of the EfficientNet-B7 backbone. This provides effective feature fusions of low and high-level details across various scales at the later stage of the framework. We introduce the Spatial-optimized Feature Attention (SOFA) module, which refines spatial features from three initial-stage feature maps. The extracted multi-scale features from the backbone are passed from the convolution feature transformation and spatial attention mechanisms to refine the low-level information. The SOFA module concatenates and upsamples these refined features, producing a comprehensive spatial representation of various levels. Moreover, the proposed Context-Aware Channel Refinement (CACR) module integrates dilated convolutions with optimized dilation rates followed by channel attention to capture multi-scale contextual information from the mature three layers. Furthermore, our progressive feature fusion strategy combines high-level semantic information and low-level spatial details through multiple residual connections, ensuring robust feature representation and effective gradient backpropagation. To enhance robustness, we train our network with augmented data featuring low and high brightness adjustments, improving its ability to handle diverse lighting conditions. Extensive experiments on four benchmark datasets — ECSSD, HKU-IS, DUTS, and PASCAL-S — validate the proposed framework’s effectiveness, demonstrating superior performance compared to existing SOTA methods in the domain. Code, qualitative results, and trained weights will be available at the link: https://github.com/habib1402/MFRNet-SOD.

OTM-HC: Enhanced Skeleton-Based Action Representation via One-to-Many Hierarchical Contrastive Learning

Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variations in timing and movement within action sequences. Our proposed One-to-Many Hierarchical Contrastive Learning (OTM-HC) framework maps the input into multi-layered feature vectors, creating a hierarchical contrast representation that captures various granularities within a human skeleton sequence temporal and spatial domains. Using sequence-to-sequence (Seq2Seq) transformer encoders and downsampling modules, OTM-HC can distinguish between multiple levels of action representations, such as instance, domain, clip, and part levels. Each level contributes significantly to a comprehensive understanding of action representations. The OTM-HC model design is adaptable, ensuring smooth integration with advanced Seq2Seq encoders. We tested the OTM-HC framework across four datasets, demonstrating improved performance over state-of-the-art models. Specifically, OTM-HC achieved improvements of 0.9% and 0.6% on NTU60, 0.4% and 0.7% on NTU120, and 0.7% and 0.3% on PKU-MMD I and II, respectively, surpassing previous leading approaches across these datasets. These results showcase the robustness and adaptability of our model for various skeleton-based action recognition tasks.

Neural processing of Mandarin and Cantonese lexical tone alternations: implications for the nature of phonological representations

This study provides neurocognitive evidence to shed further insight into the architecture of phonological representations. We tap into these representations by focusing on the neural processing of phonological alternations. Traditional generative accounts postulate abstract representations which are transformed by phonological processes into different alternants. Recent psycholinguistically-based frameworks posit that different alternants are listed in parallel in the abstract representation. Exemplar-based theories posit that alternation is a manifestation of frequency effects among concrete phonetic forms stored in memory. However, it is unclear the extent to which phonological representations of different levels of abstractedness are neurocognitive realities, beyond formal entities for linguistic analysis. This study tests the hypothesis that phonological representations are neurocognitive primitives that modulate speech processing and lexical access. We examined the processing of two surface-similar, but distributionally distinct lexical tone alternation patterns in Mandarin and Cantonese. With a cross-modal priming paradigm, differential neurophysiological components (LPC vs. N400) associated with the processing of alternation violations were identified cross-linguistically. Results support our hypothesis, suggesting that cross-linguistically distinct abstract phonological representations differentially modulate phonotactic detection, lexical access, and phonological restructuring processes. Results also lend theoretical insight into the multiform nature of the phonological representation, which encompasses both abstract and concrete levels of representations.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

The Indonesian Film Industry: Representation Of Toxic Masculinity In The Film Ngeri-Ngeri Sedap

This study explores the representation of toxic masculinity in the film ”Ngeri-Ngeri Sedap”, as exemplified by the character Mr. Domu, the patriarch of a Batak family. His behavior mirrors the patriarchal structure embedded within his family dynamics. The research employs a qualitative approach, utilizing John Fiske’s semiotic analysis, which is structured into three levels: the Level of Reality (focusing on behavior and expression), the Level of Representation (analyzing camera shots and lighting), and the Level of Ideology (uncovering underlying ideological messages). Data was gathered through observation, interviews, and documentation. Through semiotic analysis, the study reveals both verbal and non-verbal signs, such as the characters' behaviors, expressions, and dialogues within the film. Toxic masculinity in Mr. Domu manifests in various ways, including his stubbornness, insistence on appearing strong, authoritarian attitude, refusal to be contradicted, and obsession with achieving high social status to earn respect. He also enforces emotional suppression, such as prohibiting expressions of sadness or crying, especially in front of his family. These behaviors are indicative of the harmful aspects of masculinity that are often misinterpreted, misused, and imposed upon others. Moreover, the film highlights an ideology of individualism, which tends to disregard the needs and desires of others.

Analisis Semiotika Feminisme Radikal dalam Film Poor Things

The issue of feminism has been a topic of intense and in-depth discussion in various circles, involving a variety of perspectives in academic, social, and cultural studies. Social dynamics that occur such as social class injustice based on gender, oppression of women, sexism, patriarchal ideology that defines women as objects of sexuality and so on make this issue raised in any media, one of which is film. In this study, researchers are interested in analyzing the film Poor Things directed by Yorgos Lanthimos from a script by Tony McNamara which highlights women having freedom over their bodies, the setting of this film was taken in the Victorian era where at that time women were considered a second class that functioned to satisfy male sexual urges. This research aims to explain the meaning of the semiotic code of radical feminism in Poor Things directed by Yorgos Lanthimos from a script by Tony McNamara which highlights women having freedom over their bodies. The method used in this research is a qualitative approach with John Fiske's semiotic analysis based on codes that are divided into three levels, namely the level of reality, representation and ideology. The results show the values of radical feminism at the level of reality through the codes of appearance, makeup, costumes, speech, environment and behavior. At the representation level, the values of feminism are shown through the codes of camera, character, action, conflict and dialog. At the ideological level, radical feminism found in the movie Poor Things is included in patriarchal ideology. In the movie Poor Things, the level of ideology displayed is patriarchy where women are considered as a second class that functions to satisfy men's sexual urges.

A novel enhancing method for terahertz imaging of integrated circuits flaw detection

This research paper addresses the current gap in understanding the intricate damage types within packaged integrated circuits (ICs). We introduce a novel and multiscale latent low-rank representation (M-LatLRR) methodology, meticulously tailored to enhance the quality of terahertz (THz) IC images and reveal latent features. By integrating THz imaging with our proposed M-LatLRR method, we aim to facilitate the precise identification of damage types within inside the packaged ICs. Firstly, the multiscale Gaussian functions are used to remove the blur and the components are obtained via averaging. Secondly, the image matrices are utilized to extract base matrices and detail matrices. With LatLRR, the multiscale detail matrices are extracted at several representation levels. The final enhanced image is reconstructed by average strategy for dealing with the detail and pre-enhancement parts. The M-LatLRR framework is universal and can be effectively applied to extract multi-level features of packaged IC images. In a comparative analysis, our method demonstrates superior capabilities in determining the failure types of fractured wire bonds, cracks, and delamination of the dielectric layer, outperforming alternative methodologies.

“Did you watch the Disney parade?” pedagogical translanguaging for evoking students’ perezhivanie to integrate representations in linguistically diverse science classroom

Learning content subjects like science requires culturally and linguistically diverse students to learn abstract representations. Previous studies focused on how content teachers can use translanguaging to promote emotional engagement of students with homogenous cultural and linguistic composition. As students in culturally and linguistically diverse classrooms brought different emotive experiences to classrooms, it was important for teachers to leverage semiotic resources to evoke shared memory that reconcile backgrounds of different students. This bridged the gap between students’ lived experiences and learning of abstract content representations. In the current case study involving a seventh-­grade mixed medium of instruction (MMI) science classroom, we examined how a science teacher capitalised on culturally and linguistically diverse students’ shared emotive experiences to translate across different representational levels in science. Drawing on the socio-­cultural concept of perezhivanie, our findings showed that the science teacher leveraged the class’s collective experience of watching the Disney parade and linked it to water molecules moving around dissociated salts in blackboard drawings. We argue for the importance of reconciling lived experiences among students with diverse cultural and linguistic background, in order to facilitate their equitable access to content subjects.

Towards better visualizations of urban sound environments: insights from interviews

Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility.