Pattern Discrimination Research Articles

A Consistent Decision Support System for Interpreting of Magnetocardiographic Data as a Tool to Improve the Acceptance of Magnetocardiography in Clinical Practice

BackgroundMagnetocardiography undoubtedly has exceptionally high sensitivity to electrophysiological changes in the myocardium. This is an absolutely non-invasivemethod with no contraindications. However, several barriers exist to the widespread adoption of this technique into clinical routine. One of the most important is the lack of a clear and consistent medical algorithm for interpreting magnetocardiographic data, leading to a clinically significant decision. Areas coveredThe article outlines the main clinical questions clinicians pose using the magnetocardiography method. Methods for assessing the degree of abnormality of the results of a magnetocardiographic study and differential diagnosis based on the analysis of CDV maps are described in detail. Both methods for visual evaluation of sets of these maps and automatic decision rules based on linear discriminant analysis and pattern recognition are characterized. Also, techniques are described for localizing the pathological changes in the myocardium. As an example of using the developed system for interpreting magnetocardiographic data, the results of two multicenter studies in which this system of interpretation of MCG studies was used are presented. СonclusionThe magnetocardiographic examination is reliable for diagnosing chronic coronary heart disease, including in difficult-to-diagnose cases. A consistent system for interpreting of magnetocardiographic data allows medical practitioners to easily master the MCG technology and obtain the correct examination result.

An Affordable Optical Sensor for Safe Touchless Palm Print Biometry Detection

Abstract: Palm print recognition techniques have made extensive and successful use of palm print direction patterns. In order to obtain authentic line responses in the palm print image, the majority of current direction-based techniques uses pre-defined filters, which necessitate extensive prior knowledge and typically overlooks crucial direction information. Furthermore, some noise-influenced line replies will reduce the accuracy of recognition. Another challenge for enhancing recognition performance is figuring out how to extract the discriminative elements that will make the palm print easier to distinguish. We suggest using this work to understand comprehensive and discriminative direction patterns in order to address these issues. Using the palm print picture, we first extract the complete and salient local direction patterns, which include a salient convolution difference feature (SCDF) and a complete local direction feature (CLDF). Two learning models are then suggested in order to obtain the underlying structure for the SCDFs in the training samples and to learn discriminative and sparse directions from CLDF, respectively. Finally, the full and discriminative direction feature for palm print recognition is formed by concatenating the projected CLDF and the projected SCDF. The efficiency of the suggested strategy is amply demonstrated by experimental findings on three noisy datasets and seven palm print databases.

Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.

Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a true predictor of differential care given an absence of real-world studies. We conducted the first real-world pilot study of provider implicit bias by evaluating treatment parity in prostate cancer using unstructured data-the most common way providers document granular details of the patient encounter. Patients ≥18 years with a diagnosis of very-low to favorable intermediate-risk prostate cancer followed by 3 urologic oncologists from 2010 through 2021. The race Implicit Association Test was administered to all providers. Natural language processing screened human annotation using validated regex ontologies evaluated each provider's care on four prostate cancer quality indicators: (1) active surveillance utilization; (2) molecular biomarker discussion; (3) urinary function evaluation; and (4) sexual function evaluation. The chi-squared test and phi coefficient were utilized to respectively measure the statistical significance and the strength of association between race and four quality indicators. 1,094 patients were included. While Providers A and B demonstrated no preference on the race Implicit Association Test, Provider C showed preference for White patients. Provider C recommended active surveillance (p<0.01, φ = 0.175) and considered biomarkers (p = 0.047, φ = 0.127) more often in White men than expected, suggestive of treatment imparity. Provider A considered biomarkers (p<0.01, φ = 0.179) more often in White men than expected. Provider B demonstrated treatment parity in all evaluated quality indicators (p>0.05). In this pilot study, providers' practice patterns were associated with both patient race and implicit racial preferences in prostate cancer. Alerting providers of existing implicit bias may restore parity, however future assessments are needed to validate this concept.

Stigmatization and social discrimination of pulmonary tuberculosis patients in Kotamobagu City, North Sulawesi: A qualitative study

Tuberculosis (TB) remains a significant public health challenge, particularly in developing countries such as Indonesia. In Kotamobagu city, North Sulawesi, despite being recognized as one of Indonesia's most tolerant cities, patients with TB face persistent stigmatization and discrimination. This study aimed to examine the social construction of discrimination against TB patients in Kotamobagu city, focusing on how these perceptions develop and persist. This qualitative study employed a constructivist paradigm and utilized Berger and Luckman's social construction theory. Data were collected through in-depth interviews and direct observations of 16 informants, including patients with TB, healthcare workers, family members, neighbors, and colleagues. The analysis was conducted via NVIVO 12 Plus software following a systematic thematic approach. The study revealed complex patterns of stigmatization and discrimination across familial, community, and workplace settings. Network analysis revealed interconnected stigmatization factors, with physical isolation emerging as a primary preventive reaction within families. Social distancing behaviors have been documented in both familial and workplace environments. This research identified multiple pathways through which stigmatization affects patients' lives, including social isolation, psychological impact, and treatment adherence. Fear of transmission has emerged as the primary driver of discriminatory behaviors, leading to various forms of social exclusion and workplace ostracism. TB-related stigma and discrimination in Kotamobagu manifest through complex social mechanisms that significantly affect patients' psychological well-being and treatment outcomes. These findings emphasize the need for comprehensive interventions that address both the structural and interpersonal aspects of stigma, particularly in workplace and family settings.

Criminalization of traditional practices as a facet of cultural genocide: the case of enbedade amongst the Amhara community in Ethiopia

ABSTRACT The Amhara people of Ethiopia have faced a series of systemic assaults on their cultural, social, and political structures over the past decades, reflecting a broader pattern of ethnic and political discrimination. During the Tigray People’s Liberation Front (TPLF) regime (1991–2018), the Amhara community experienced severe marginalization and systematic suppression of their cultural practices. The criminalization of ènbedãdé epitomized the deliberate effort to undermine Amhara’s identity and influence. Since 2018, the situation has further deteriorated under the Oromumma regime, with accusations of ethnic cleansing, forced displacement, and targeted violence against the Amhara population. This regime has also employed deceitful mediation tactics, manipulating respected elders and religious leaders to capture and imprison opposition figures, thereby eroding traditional conflict resolution mechanisms. Concurrently, the Ethiopian government’s foreign policy has isolated the Amhara, exacerbating their vulnerability through hostile regional relations and historical distortions. The systematic attacks on Amhara culture and social cohesion, emphasize the concerted efforts to dismantle their historical and cultural identity. The ongoing marginalization, both internally and externally, has led to profound disintegration within the Amhara community, highlighting the need for international recognition and intervention to address these injustices.

GENDER BIAS IN ARTIFICIAL INTELLIGENCE: A CRITICAL PERSPECTIVE AND LEGAL ANALYSIS

Artificial Intelligence (AI) is transforming key industries like employment, healthcare, and criminal justice, but it also introduces significant ethical and legal challenges, particularly regarding gender bias. AI systems, often trained on biased historical data, can perpetuate and even amplify existing gender inequalities. This essay examines the legal implications of gender bias in AI, focusing on challenges to anti-discrimination laws, transparency issues, and the need for regulatory oversight. Gender bias in AI arises when systems are trained on datasets that reflect societal inequalities, leading to discriminatory outcomes. This bias is not a technical flaw, but rather a consequence of using data and algorithms that mirror patterns of discrimination. The lack of diversity among AI developers, who are predominantly male, exacerbates this issue by failing to account for the perspectives and needs of women and marginalized groups. In legal contexts, the use of AI in hiring, criminal justice, and risk assessment raises ethical concerns. AI-driven systems risk reinforcing historical gender biases, which can undermine fairness in decision-making processes. Unchecked, these biases could worsen disparities in critical areas such as recruitment and justice administration, threatening legal protections. A major legal challenge is how AI interacts with existing anti-discrimination laws. To address these challenges, transparency in AI decision-making is essential. Regulatory frameworks must evolve to require regular audits of AI systems and enforce accountability for biased outcomes. Ethical guidelines are insufficient; mandatory legal oversight is needed to ensure AI promotes fairness and inclusivity.

EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training.

The superior performance of modern computer vision backbones (e.g., vision Transformers learned on ImageNet-1 K/22 K) usually comes with a costly training procedure. This study contributes to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these two aspects and design curriculum learning schedules by proposing tailored searching algorithms. Moreover, we present useful techniques for deploying our approach efficiently in challenging practical scenarios, such as large-scale parallel training, and limited input/output or data pre-processing speed. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. As an off-the-shelf approach, it reduces the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer) by [Formula: see text] on ImageNet-1 K/22 K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE).

Caste and Sale Market: A Study of Economic Discrimination in Business Enterprises of Andhra Pradesh

This article attempts to analyse the issue of caste discrimination, with a special focus on the experience of business enterprises in the Guntur district of Andhra Pradesh. It tries to examine the discrimination faced by scheduled caste business owners (SCBOs) of the non-farm sector, especially of business enterprises in the sale market, based on a field study conducted in Guntur district. The study is based on a sample of 225 respondents, comprising 150 Scheduled Caste (SC) and 75 non-SC/Scheduled Tribe (ST) business owners. A structured questionnaire was followed for the interview. The objective of this article is to understand the nature and pattern of discrimination existing among business owners. In the present day, even though SCBOs are allowed in business enterprises, they still face economic discrimination in markets. The present study has found that there is an existence of discrimination in the sale market as about 61.7% of respondents reported that their caste background affects their sales and access to the common market. The immediate impact of discrimination is evident from the existing disparities in profits between SCBOs and other business owners (OBOs). If identity negatively affects profit expectations, the chance of survival in small business will be very low for the underprivileged group. This is why, this article seeks to examine whether caste background affects the profitability of businesses significantly or not?

Similar freezing spectra of particles in plant canopies and in the air at a high-altitude site

Abstract. Plant canopies are an important source of biological particles aerosolized into the atmosphere. Certain aerosolized microorganisms are able to freeze slightly supercooled cloud droplets and therefore affect mixed-phase cloud development. Still, spatiotemporal variability of such biological ice-nucleating particles (INPs) is currently poorly understood. Here, we study this variability between late summer and leaf shedding on the scale of individual leaves collected about fortnightly from four temperate broadleaf tree species (Fagus sylvatica, Juglans regia, Prunus avium and Tilia platyphyllos) on a hillside (Gempen, 650 m a.s.l. (metres above sea level)) and in a vertical canopy profile of one Fagus sylvatica (Hölstein, 550 m a.s.l.) in north-western Switzerland. The cumulative concentration of INPs active at ≥-10 °C (INPs−10) did not vary significantly between the investigated tree species but, as inferred from leaf mass per area and leaf carbon isotopic ratios, seemed to be lower on sun leaves as compared with shade leaves. Between August and mid-November, the median INP concentration increased from 4 to 38 INP−10 cm−2 of leaf area and was positively correlated with mean relative humidity throughout 24 h prior to sampling (Spearman's r=0.52, p&lt;0.0001, n=64). In 53 of the total 64 samples collected at the Gempen site, differential INP spectra between −3 and −10 °C exhibited clearly discriminable patterns: in 53 % of the spectra, the number of additionally activated INPs increased persistently with each 1 °C decrease in temperature; the remaining spectra displayed significant peaks in differential INP concentration above −9 °C, most frequently in the temperature interval between −8 and −9 °C (21 %) and between −7 and −8 °C (17 %). Interestingly, the three most frequent patterns in differential INP spectra on leaves in Gempen were also prevalent in similar fractions in air samples with clearly discriminable patterns at the high-altitude Jungfraujoch site (3580 m a.s.l., Switzerland) collected during summer in the previous year. These findings corroborate the idea that a large fraction of the airborne biological INP population above the Alps during summer originates from plant surfaces. Which parameter or set of parameters could affect biological INP populations on both scales – upwind airsheds of high-altitude sites as well as individual leaves – is an intriguing question for further exploration. A first guess is that leaf wetness duration plays a role.

TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation

Multivariate time series classification (MTSC), which identifies categories of multiple sensor signals recorded in continuous time, is widely used in various fields such as transportation, finance, and medical treatment. The focused challenge remains learning the dependencies between subsequences to capture discriminative patterns while providing convincing explanations. In this paper, we propose a temporal-variable parallel deep learning framework to mine global and local features to achieve a win-win situation in performance and interpretability. Specifically, for harmonizing the inattention blindness of global features, we introduce a graph attention mechanism with global awareness (GAT-g), where the learning of edge representations incorporates both inter-node relationships and the node-to-graph context. Furthermore, for evaluating the feature combinations utility, we exploit game interactions for the first time, which quantifies the utility of feature combination through Shapley values to illustrate the dynamically coordinating representation ability of the model for diverse time series features. In addition, the interpretation module leverages temporal and variable subspace attention distributions to provide instantiated explanations with additive computational complexity, enhancing the comprehension of prediction results. Experimental evaluation on the University of East Anglia (UEA) archive of 30 multivariate time series datasets shows that the proposed method outperforms 12 state-of-the-art methods on 11 datasets.

Leaf cultivar identification via prototype-enhanced learning

Leaf cultivar identification, as a typical task of ultra-fine-grained visual classification (UFGVC), is facing a huge challenge due to the high similarity among different varieties. In practice, an instance may be related to multiple varieties to varying degrees, especially in the UFGVC datasets. However, deep learning methods trained on one-hot labels fail to reflect patterns shared across categories and thus perform poorly on this task. As an analogy to natural language processing (NLP), by capturing the co-relation between labels, label embedding can select the most informative words and neglect irrelevant ones when predicting different labels. Based on this intuition, we propose a novel method named Prototype-enhanced Learning (PEL), which is predicated on the assumption that label embedding encoded with the inter-class relationships would force the image classification model to focus on discriminative patterns. In addition, a new prototype update module is put forward to learn inter-class relations by capturing label semantic overlap and iteratively update prototypes to generate continuously enhanced soft targets. Prototype-enhanced soft labels not only contain original one-hot label information, but also introduce rich inter-category semantic association information, thus providing more effective supervision for deep model training. Extensive experimental results on 7 public datasets show that our method can significantly improve the performance on the task of ultra-fine-grained visual classification. The code is available at https://github.com/YIYIZH/PEL.

Characterisation of IBD heterogeneity using serum proteomics: A multicentre study.

Recent genetic and transcriptomic data highlight the need for improved molecular characterisation of inflammatory bowel disease (IBD). Proteomics may advance the delineation of IBD phenotypes since it accounts for post-transcriptional modifications. We aimed to assess the IBD spectrum based on inflammatory serum proteins and identify discriminative patterns of underlying biological subtypes across multiple European cohorts. Using proximity extension methodology, we measured 86 inflammation-related serum proteins in 1551 IBD patients and 312 healthy controls (HC). We screened for proteins exhibiting significantly different levels among IBD subtypes and between IBD and HC. Classification models for differentiating between Crohn's disease (CD) and ulcerative colitis (UC) were employed to explore the IBD spectrum based on estimated probability scores. Levels of multiple proteins, such as IL-17A, MMP-10, and FGF-19, differed (fold-change>1.2; FDR<0.05) between ileal vs colonic IBD. Using multivariable models, a protein signature reflecting the IBD spectrum was identified, positioning colonic CD between UC and ileal CD, which were at opposite ends of the spectrum. Based on area under the curve (AUC) estimates, classification models more accurately differentiated UC from ileal CD (median AUCs>0.73) than colonic CD (median AUCs<0.62). Models differentiating colonic CD from ileal CD demonstrated intermediate performance (median AUCs 0.67-0.69). Our findings in serum proteins support the presence of a continuous IBD spectrum rather than a clear separation of CD and UC. Within the spectrum, disease location may reflect a more similar disease than CD vs UC, as colonic CD resembled UC more closely than ileal CD.

Pattern recognition-based aptasensor array for discrimination of matrix effect

Nucleic acid aptamers have attracted considerable attention in recent years, particularly in the fields of disease diagnosis and treatment and biosensing. However, complex matrix effects have severely hindered their progress towards practical application. In this study, the novel strategy of a pattern recognition-based aptasensor array was introduced for target recognition based on the predictable selectivity capability of aptamers by circumventing the challenge of the interference of the matrix effect. As one proof of concept, we chose lactoferrin as the target protein, three selected aptamers with differential affinity as its recognition probes, eleven matrix samples of milk powder as the pattern discrimination model, and AuNPs color as the response signals. In the AuNPs colorimetric arrays, the lactoferrin-added milk powder matrix induced different degrees of protection on the surface of AuNPs particles due to different degrees of binding of lactoferrin to the three aptamer sequences and therefore exhibited differentiated red-to-blue color change along with salt-induced aggregation behaviors. The difference in color responses exhibited with and without lactoferrin addition was considered as the fingerprint pattern of the lactoferrin in each milk powder matrix, followed by the supervised machine learning analysis of linear discriminant analysis (LDA) for better orthogonality to complete the identification and prediction of the unknown samples. By this strategy, eleven kinds of milk powder at the added concentration of lactoferrin (50 nmol/L) presented a distinct response pattern and have been identified and classified with accuracies of 100 % via LDA patterns (three aptamers × eleven milk powder matrices × five replicates) with three canonical factors (91.2 %, 8.58 %, and 0.22 %). Furthermore, the accuracy of 22 unknown samples was 100 %, indicating the achievement in differentiating between different milk powder matrices and the identification of each matrix. The proposed aptasensor array complements the traditional “lock-and-key” single aptasensor and opens a new direction for developing sensitive sensing array systems circumventing complex matrix effects.

LSTM-CRP: Algorithm-Hardware Co-Design and Implementation of Cache Replacement Policy Using Long Short-Term Memory

As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discrimination of complex access patterns. Therefore, this paper presents the LSTM-CRP algorithm as well as its efficient hardware implementation, which employs the long short-term memory (LSTM) for access pattern identification at run-time to guide cache replacement algorithm. LSTM-CRP first converts the address into a novel key according to the frequency of the access address and a virtual capacity of the cache, which has the advantages of low information redundancy and high timeliness. Using the key as the inputs of four offline-trained LSTM network-based predictors, LSTM-CRP can accurately classify different access patterns and identify current cache characteristics in a timely manner via an online set dueling mechanism on sampling caches. For efficient implementation, heterogeneous lightweight LSTM networks are dedicatedly constructed in LSTM-CRP to lower hardware overhead and inference delay. The experimental results show that LSTM-CRP was able to averagely improve the cache hit rate by 20.10%, 15.35%, 12.11% and 8.49% compared with LRU, RRIP, Hawkeye and Glider, respectively. Implemented on Xilinx XCVU9P FPGA at the cost of 15,973 LUTs and 1610 FF registers, LSTM-CRP was running at a 200 MHz frequency with 2.74 W power consumption.

Discrimination of Left Atrial Strain Patterns in Hypertensive Heart Disease and Hypertrophic Cardiomyopathy: a Cardiac Magnetic Resonance Feature.

To assess left atrial (LA) strain parameters using cardiovascular magnetic resonance imaging feature tracking (cardiac MRI-FT) for differentiating hypertensive heart disease (HHD) from hypertrophic cardiomyopathy (HCM), which are two left ventricular hypertrophic diseases that could present with similar morphologies in early stage but differ in clinical symptoms and treatment strategies. 45 patients with HHD, 85 patients with HCM (non-obstructive hypertrophiccardiomyopathy [HNCM, n = 45] and obstructive hypertrophic cardiomyopathy [HOCM, n = 40]) and 30 healthy controls (HC) were retrospectively included. LA volumes, strain, and strain rate were determined by manually contouring on the two- and four-chamber views of the CMR-FT module using CVI 42 software. LA volume parameters including LA maximum, precontraction, and minimum volume index, and total, passive, and active emptying fractions were obtained using the biplane methods. The LA strain parameters, including total strain (εs), passive strain (εe), active strain (εa), peak positive strain rate (SRs), early peak negative strain rate (SRe), and late peak negative strain rate (SRa), were obtained from the LA strain curve. The LA strain and LA strain rate were impaired in both HHD group and HCM group, and they were the most severely impaired in the HOCM group. εs (AUC = 0.691, P = 0.006; the best cutoff value, 25.1%), εa (AUC = 0.654, P = 0.027; the best cutoff value, 10.5%), SRs (AUC = 0.710, P = 0.003; the best cutoff value, 0.81 1/s) and SRa (AUC = 0.667, P = 0.016; the best cutoff value, -1.30 1/s) showed significant differences in the identification between HHD and HNCM. All LA strain parameters were different in the identification between HHD and HOCM (all P < 0.05).LA strain parameters can be helpful for differentiating HHD from HCM, providing valuable insights for diagnosis.

Miscarriage: cases of unfair incrimination reveal weaknesses in the criminalization of abortion

The article examines the historical, religious and legal context of abortion, highlighting its criminalization in Ecuador and its impact on women's health and rights. Subsequently, it addresses the injustice of criminalization derived from a situation of spontaneous abortion and proposes to evaluate the theoretical elements that should be considered to avoid the issuance of sentences to women who experience this condition. To this end, the methodology employed focused on the analysis of representative cases. The cases analyzed include Sara, Marta, Paola and Maribel, who suffered unjust detentions and cruel treatment in the judicial system due to medical complications or situations of gender violence. The findings reveal a pattern of discrimination and stigmatization against low-income women and marginalized communities and conclude that the criminalization of abortion increases risks to women's health and lives, perpetuating gender-based violence and inequality. This ultimately highlights the urgent need to reform abortion laws and policies in Ecuador to protect human rights and promote gender equality.

CRAFTING A FOUCAULDIAN GENEALOGY METHOD FOR ANALYZING CURRICULUM-AS-DISCOURSE IN SOUTH AFRICA

Introduction/Frame: (Contextualization/Justification) Viewing curriculum as discourse can enable educators to identify how historical markers of discrimination are repeated. Historical markers are archaeologically embedded in the various dimensions of a curriculum and find expression in preferred ways of thinking, speaking, doing, and being. Such historical hypernorms are often maintained by practical rationalities, or technologies of power that reify their taken-for-granted legitimacy as ‘truth regimes’. Research Question/Objectives: This phase of the study aimed to critically explore and disclose how the (socio/politico-historically constructed) rules of knowledge-formation in a contemporary occupational therapy curriculum-as-discourse, are reproduced and maintained as truths at a university that historically supported apartheid in South Africa. Methodology: A Foucauldian Discourse Analysis (FDA) approach was used to systematically craft a genealogy analysis method from foundational Foucauldian theory. Adjacent, a lens of the ‘dimensions of the curriculum’ was employed: i.e., the formal, the informal, the hidden, and the negated curriculum. Various data sources were categorized accordingly, e.g., overarching policy documents; social events; timetables and prescribed texts; physical teaching spaces; espoused values, and underlying assumptions modelled during assessment in teaching and learning spaces. Results: Following the four rules of knowledge-formation that ensued from an archaeology analysis, the contemporary reification of the historical markers was problematised through various technologies of power and self, as well modes of reasoning. Examples are pervasive white demographics of both students and faculty reifying cognitive monopoly and mono-cultural epistemologies; the rationalization of highly ritualized (over)assessment of students suppressing adult-to-adult transactionalism in andragogy practices; the reproduction of mono-cultural epistemologies in clinical training operating in the hidden curriculum. Final considerations: Consciously dismantling the rationalising strategies for the continuation of discriminatory patterns of inclusion and exclusion in a curriculum, can open a space for critical dialogue, disruption, and reconfiguration of a curriculum-as-discourse towards social justice, and epistemic freedom in (higher) education.

Geometric Morphometric and Pattern Discrimination of Handwritten Numeral Characters Based on Local Ethnicities and Native Linguistic Disparities in Malaysia for Forensic Applications

Handwriting can be influenced by factors like culture and native language disparities; however, the specific research to discriminate handwritten numeral characters for forensic questioned document investigation involving the diverse ethnicities (Malay, Chinese and Indian) in Malaysian population remains unreported. Despite its application in forensic anthropology, utilization of the landmark based analysis using the Geometric Morphometric (GMM) technique for examining handwritten numeral character specimens appears sparse. Therefore, this present research that attempted to discriminate ethnicities background of 390 consenting adult participants in Malaysia based on the handwritten numeral characters (0-9) using GMM technique, merits forensic and scientific considerations. Results revealed that while there were universal ones, ethnic-specific handwritten numeral characters that may possibly be influenced by culture and native language (Malay, Chinese and Tamil languages) also prevailed. Significant differences (p &lt; 0.0001) in ethnic-specific characters for the Malay, Chinese and Indian participants who attended the different national primary school settings in Malaysia were also observed. In conclusion, the discrimination of handwritten numeral characters for forensic examination of questioned documents among Malay, Chinese and Indian authors for the development of forensic intelligence is empirically supported.

The Crisis of the 1930s and American Antisemitism

Abstract This article explores the economic and psychological impact of the Great Depression on the American population, emphasizing the rise of antisemitism during the 1930s. We begin with a historical overview of the economic downturn following the Wall Street Crash of 1929, with an emphasis on the ensuing unemployment and social despair. We argue that the representation of the Great Depression diverged from reality, with public discourse constructing an ‘imagined crisis’ that amplified fears and uncertainties. This crisis rhetoric led to the scapegoating of the Jewish community, which was unjustly blamed for the nation’s hardships. The article provides an in-depth analysis of how Henry Ford’s dissemination of “The Protocols of the Elders of Zion” and Father Charles E. Coughlin’s radio broadcasts contributed to spreading antisemitic sentiments by exploiting economic anxieties and promoting a narrative of Jewish conspiracy. Furthermore, the article discusses the historical context of American antisemitism, identifying it as a complex phenomenon influenced by various factors, including religious, economic, and political antisemitism. Despite efforts to combat antisemitic rhetoric, figures like Ford and Coughlin played a significant role in shaping public perceptions and directing blame towards the Jewish community during times of crisis. We conclude that the crisis of the 1930s, marked by unprecedented economic hardship and social unrest, was exacerbated by ideological discourses that targeted Jews as scapegoats, reflecting a disturbing pattern of prejudice and discrimination that has echoed into contemporary times.

Discriminatory discourses against Morocco and Moroccan immigrants in the Spanish context. A corpus-assisted critical discourse analysis of ‘Jovenlandia/jovenlandés’ (Youthland/Youthlandian) on X

Media discourse has provided a breeding ground for the advent of new patterns of discrimination with the power of influencing the public narrative. This goal of this study is to analyze how the recently-created terms ‘Jovenlandia’ (Youthland) and ‘jovenlandés’ (Youthlandian) are employed mockingly on X in response to the use of generic ‘joven’ (youth, youngster) in news articles. With the use of these terms, X users imply a supposed cancel culture in mass media that withholds data of Muslim immigrant criminals. The theoretical approach adopted combines corpus tools with Critical Discourse Analysis to examine 3856 posts on X during the 2023 Spanish general election campaign (from May 29th to July 23rd 2023). The analysis is based on Wodak’s discourse-historical approach and examines referential and predication strategies to unveil the linguistic expressions to represent immigrants. The results show that the words serve to construct a racist discursive representation of Moroccan migrant identities on the platform X in Spain. Immigrants are portrayed as violent, criminals, and rapists, whereas Morocco is depicted as a land of criminals with homophobic and sexist traditions. The discursive strategies against migrants were commonly grounded in the Muslims’ perceived failure to assimilate into the Spanish culture. In this sense, pork meat is used as a symbolic cultural criterion for evaluating authentic Spanish adherence. Spaniards are self-portrayed as victims of the left-wing government decisions and X users appeal to the extreme-right wing party Vox to solve the problem.