Nuanced Information Research Articles

Digital Contact Tracing Implementation Among Leaders and Health Care Workers in a Pediatric Hospital During the COVID-19 Pandemic: Qualitative Interview Study.

Health systems had to rapidly implement infection control strategies to sustain their workforces during the COVID-19 pandemic. Various outbreak response tools, such as digital contact tracing (DCT), have been developed to monitor exposures and symptoms of health care workers (HCWs). Limited research evidence exists on the experiences with these technologies and the impacts of DCT innovations from the perspective of stakeholders in health care environments. This study aims to identify the factors influencing the adoption of DCT, highlight variations in perspectives across 3 key stakeholder groups concerning the impact of DCT, and provide benchmarking evidence for future pandemic preparedness. Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we conducted an exploratory qualitative study to investigate the implementation and impact of DCT at the Children's Hospital of Eastern Ontario between December 2022 and April 2023. We conducted 21 semistructured interviews with key stakeholders, including health care administrators (6/21, 29%), occupational health and safety specialists (8/21, 38%), and HCWs (7/21, 33%). Stakeholders were asked about the factors influencing engagement with the DCT tool, organizational-level uptake, the implementation process, long-term use and sustainability of DCT, and unintended consequences. Verbatim transcripts were subject to thematic analysis using NVivo (QSR International). The implementation of DCT was viable and well received. End users indicated that their engagement with the DCT tool was facilitated by its perceived ease of use and the ability to gain awareness of probable COVID-19 exposures; however, risk assessment consequences and access concerns were reported as barriers (reach). Participants commonly agreed that the DCT technology had a positive influence on the hospital's capacity to meet the demands of COVID-19 (effectiveness). Implementors and occupational specialists referred to negative staffing impacts and the loss of nuanced information as unintended consequences (effectiveness). Safety-focused communication strategies and having a DCT tool that was human-centered were crucial factors driving staff adoption of the technology. Conversely, adoption was challenged by the misaligned delivery of the DCT tool with HCWs' standard practices, alongside the evolving perceived threat of COVID-19. Stakeholders collectively agreed on the viability of DCT and its applicability to infectious disease practices (maintenance). Hospital stakeholders were highly satisfied with DCT technology and it was perceived as feasible, efficient, and having a positive impact on organizational safety. Challenges related to the alignment and delivery of DCT, alongside the evolving perspectives on COVID-19, posed obstacles to continued adoption by HCWs. Our findings contribute to evidence-based practices and present benchmarks that can inform preparedness for future pandemics and infectious disease outbreaks and help other organizations implement similar technologies.

Translation of a Bipolar Fuzzy Set in Hilbert Algebras

This study explores the application of bipolar fuzzy set theory within Hilbert algebras, introducing and examining the concept of bipolar fuzzy (β, α)-translations of a bipolar fuzzy set φ =(φ+, φ−) in two distinct forms: Type I and Type II. Fundamental properties of these bipolar fuzzy translations are investigated in depth, alongside the introduction of bipolar fuzzy extensions andintensities, broadening the utility and flexibility of bipolar fuzzy sets in capturing nuanced bipolar information. Moreover, this work addresses the intricate relationships between the complement of a bipolar fuzzy subalgebra, bipolar fuzzy ideal, and bipolar fuzzy deductive system with respect to their level cuts. The findings significantly contribute to the broader theoretical foundation and potential applications of bipolar fuzzy logic in Hilbert algebras, offering valuable insights for managing complex bipolar information in uncertain environments.

Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention

Abstract Background Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB). Methods We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests. Results The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75). Discussion ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.

Graph Curvature Flow-Based Masked Attention.

Graph neural networks (GNNs) have revolutionized drug discovery in chemistry and biology, enhancing efficiency and reducing resource demands. However, classical GNNs often struggle to capture long-range dependencies due to challenges like oversmoothing and oversquashing. Graph Transformers address these issues by employing global self-attention mechanisms that allow direct information exchange between any pair of nodes, enabling the modeling of long-range interactions. Despite this, Graph Transformers often face difficulties in capturing the nuanced structural information on graphs. To overcome these challenges, we introduce the CurvFlow-Transformer, a novel graph Transformer model incorporating a curvature flow-based masked attention mechanism. By leveraging a topologically enhanced mask matrix, the attention layer can effectively detect subtle structural differences within graphs, balancing the focus between global mutual information and local structural details of molecules. The CurvFlow-Transformer demonstrates superior performance on the MoleculeNet data set, surpassing several state-of-the-art models across various tasks. Moreover, the model provides unique insights into the relationship between molecular structure and chemical properties by analyzing the attention heat coefficients of individual atoms.

Discovering implicit role of tourists’ perceived distance and emotions in decision-making: combing grounded theory and sentiment analysis

ABSTRACT Analysing the attitudes and emotions behind tourists’ perceptions of distance provides powerful assistance for destination marketers and scholars. However, there is yet to be a universally adopted scale of tourists’ perceived distance. It is hard to effectively extract tourists’ attitudes from the distance scale toward a destination. This paper identifies the critical dimensions of perceived distance voiced by 19 Chinese tourists with grounded analysis, an inductive, comparative, and interactive method that effectively captures nuanced information. Advanced techniques for linguistic analysis provide the opportunity to extract emotional meaning from tourists’ textual data through the Latent Dirichlet Allocation (LDA) algorithm. The results identify a set of appraisal dimensions as antecedents to perceived distance. With cognitive appraisal theory (CAT), different evaluations on these dimensions provide multiple paths to eliciting emotion change. The findings contrast with previous research in the distance decay model, which noted a single distance involving tourism demand. We also find differences based on demographic segments. Social network analysis helps further identify the relationship between dimensions. We conclude by discussing the study's implications for future studies and practice.

Refining and expanding accountability policy for English learners to support Lau implementation: A critical policy analysis

ABSTRACT Title I accountability requirements under the Every Student Succeeds Act (ESSA) represent the present-day instantiation of Lau, which requires schools to provide a program for English learners (ELs) that supports their meaningful engagement and to provide transparent information about EL program quality. This study uses critical policy analysis to explore whether ESSA accomplishes what it intends. Employing a parallel mixed methods design, we first analyze longitudinal data from two states to examine whether their approaches to assessing EL students’ academic performance and progress support valid claims about EL achievement. Then, we analyze focus group and interview data from educators, advocates, and families in states to understand how they assess the quality of a school’s EL program. Taken together, our findings highlight the need for the incorporation of multiple perspectives into the design of state accountability systems and for the development of robust data systems that facilitate the sharing of accurate and nuanced information related to EL student outcomes and opportunities.

CYBERSECURITY PRACTICES AND FRAUD PREVENTION AMONG GHANAIAN TELECOMMUNICATION FIRMS, A MIXED METHOD ANALYSIS

This study explores the effectiveness of cybersecurity practices in preventing fraud within Ghanaian telecommunication firms, adopting a qualitative research approach to gain in-depth insights into the challenges and strategies involved. By focusing on the perspectives of key personnel directly involved in cybersecurity and fraud prevention, the study aims to uncover critical factors influencing the success of these practices. A case study design is employed, examining cybersecurity implementation across five major telecommunication firms in Ghana. The sample consists of 15 participants, including IT managers, cybersecurity officers, and fraud prevention specialists, selected through purposive sampling to ensure relevant and insightful data collection. Semi-structured interviews are used as the primary data collection method, allowing participants to provide detailed and nuanced information on their experiences with cybersecurity measures. Thematic analysis is employed to identify key patterns and themes within the data, highlighting the role of insider threats, regulatory frameworks, and employee behavior in contributing to fraud. The findings indicate that while robust technical measures such as encryption, multi-factor authentication, and intrusion detection systems play a significant role in reducing fraud, challenges in enforcement, compliance, and employee behavior remain critical barriers. The study concludes that a holistic approach, combining technical solutions with organizational culture improvements and stronger regulatory enforcement, is essential for enhancing cybersecurity effectiveness in Ghana’s telecommunication sector. Recommendations are made for improving incident response times, regulatory support, and employee training programs, as well as fostering greater collaboration between firms and regulatory bodies to strengthen fraud prevention efforts.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0709/a.php" alt="Hit counter" /></p>

Non-Copyrightability of Data in Scientific Publications: A Free-for-All or a Global Commons Partnership?

Scientific publications provide a wealth of peer-reviewed, high-quality data that have been maintained over time, resulting in data persistence. As data repositories with rich provenance information, publications are indispensable sources for the integration and extension of networks of interlinked Findable, Accessible, Interoperable and Reusable (FAIR*1) bio/geodiversity data. In this way, they form pivotal fact- and knowledge-based contributions to applications that address the biodiversity crisis. The mobilization of data preserved in scientific publications is hindered, however, by distinct copyright legislation contexts for publications versus the data that they contain. Moreover, legislations concerning copyright continue to lack harmonization across jurisdictions, their interpretation is difficult, and the applicable legal national scope can be uncertain. We clarify and highlight that data within scientific publications are not copyrightable and thus can be openly and freely reused once legal access has been gained to their enclosing publication*2. To ensure that publications are as accessible as possible, a joint statement supported by the Biodiversity Heritage Library (BHL), the Consortium of European Taxonomic Facilities (CETAF) and the Society for the Preservation of Natural History Collections (SPNHC) (Benichou et al. 2023) recommends that authors and publishers make their works as accessible as possible by using a CC-BY license or preferably waive copyright (CC0) to their publications. Explicitly associating a public domain mark (PDM, e.g., the PDM from Creative Commons) to their published data, provides users with certainty about reusability. Yet, by setting works and bio/geodiversity data into the public domain, they do not become a free-for-all. We stress that data need to be associated with clear provenance information in alignment with scientific best practices and the scientific community's social norms. This includes providing detailed attribution to authors of cited works and reused data. Proposed data governance labels, for example, modeled after the Local Contexts labels developed by the international Indigenous Peoples and Local Communities (IPLC) community, would enable authors to communicate social and ethical contexts and applicable rules to data users for ensuring the sustainability of a shared environmental and data commons. Categories of Local Contexts labels that are of interest and applicable in the sciences are, for example, those that communicate (1) correct citation information and ask for attribution when knowledge and/or data are reused (Traditional Knowledge label (TK) Attribution), (2) an interest in being recognized and acknowledged due to a significant relationship with and responsibility for samples and data (Biocultural label (BC) Provenance), (3) the verification of the data and their context following a community protocol (TK Verified), (4) that non-commercial use (TK Non-Commercial/BC Non-Commercial) or (5) outreach activities (TK Outreach/BC Outreach) are generally permitted, while for other uses direct contact and engagement is required, or (6) an openness to collaboration and partnerships (TK Collaboration/BC Collaboration). There are concerns about the tension between the goal of achieving open data (e.g., Anonymous 2014) to enable and promote open science (e.g., UNESCO 2021) and, at the same time, imposing restrictions on these data in the form of governance labels. Furthermore, while the reference of the publication through which data are published, as well as more specifically bibliographic references cited for specific data within the publication, provide sufficient information for attribution and provenance, much more fine-grained and nuanced contextual information (e.g., in the form of metadata) is needed for assuring responsible reuse. Such context-providing metadata unlock the full potential of the data and enable their reusability. This can be done using machine-actionable markup tags in combination with human-readable labels that inform machines and human users about the semantics of the data as well as their ethical and social dimensions that govern responsible and sustainable reuse. Future work is needed to discover, differentiate and define the quality and scope of the appropriate contexts that are necessary and sufficient for being able to fully and responsibly reuse the data in different situations.

Categorical claims about voluntary renewable energy actions are not supported by categorical evidence

Abstract Voluntary renewable energy procurement by retail electricity customers could play a critical role in achieving clean energy objectives. Research could optimize that role by providing accurate and nuanced information about how different voluntary actions most effectively drive renewable energy deployment. Recent literature has made categorical assertions that broad classes of voluntary actions are weakly or not impactful. This Perspective argues that these categorical assertions are not supported by equally categorical evidence. A closer examination shows the evidence is less conclusive and less generalizable than stakeholders may presume. Voluntary renewable energy buyers would benefit from more nuanced analysis of the impacts of different energy procurement actions across distinct contexts.

Empowering patients: leveraging the untapped potential of smartphone recordings in dermatology consultations.

During consultations, patients can be overwhelmed with new diagnoses, complex treatment plans, particularly when bad news is broken (such as a new skin cancer diagnosis). Patients may be unable to recall nuanced medical information, particularly if elderly, attending on their own, with lower literacy, with impaired sensory abilities and where English may not be their native language. Given the ubiquity of smartphones, recording conversations during consultations—with the doctor’s consent—has become an accessible tool for patients. Allowing patients to record their consultations can empower them, enhance recall, and reduce decisional regret, particularly in emotionally charged settings such as skin cancer clinics where cognitive processing may be impaired.

A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.

Background To assess the feasibility of a machine learning (ML) approach using radiomics features of perfusion defects on rest myocardial perfusion imaging (MPI) to detect the presence of hibernating myocardium. Methodology Data of patients who underwent 99mTc-sestamibi MPI and 18F-FDG PET/CT for myocardial viability assessment were retrieved. Rest MPI data were processed on ECToolbox, and polar maps were saved using theNFile PMap tool. The reference standard for defining hibernating myocardium was the presence of mismatched perfusion-metabolism defect with impaired myocardial contractility at rest. Perfusion defects on the polar maps were delineated with regions of interest (ROIs) after spatial resampling and intensity discretization. Replicable random sampling allocated 80% (257) of the perfusion defects of the patients from January 2017 to September 2022 to the training set and the remaining 20% (64) to the validation set. An independent dataset of perfusion defects from 29 consecutive patients from October 2022 to January 2023 was used as the testing set for model evaluation. One hundred ten first and second-order texture features were extracted for each ROI. After feature normalization and imputation, 14 best-ranked features were selected using a multistep feature selection process including the Logistic Regression and Fast Correlation-Based Filter. Thirteen supervised ML algorithms were trained with stratified five-fold cross-validation on the training set and validated on the validation set. The ML algorithms with a Log Loss of <0.688 and <0.672 in the cross-validation and validation steps were evaluated on the testing set. Performance matrices of the algorithms assessed included area under the curve (AUC), classification accuracy (CA), F1 score, precision, recall, and specificity. To provide transparency and interpretability, SHapley Additive exPlanations (SHAP) values were assessed and depicted as beeswarm plots. Results Two hundred thirty-nine patients (214 males; mean age 56 ± 11 years) were enrolled in the study. There were 371 perfusion defects (321 in the training and validation sets; 50 in the testing set). Based on the reference standard, 168 perfusion defects had hibernating myocardium (139 in the training and validation sets; 29 in the testing set). On cross-validation, six ML algorithms with Log Loss <0.688 had AUC >0.800. On validation, 10 ML algorithms had a Log Loss value <0.672, among which six had AUC >0.800. On model evaluation of the selected models on the unseen testing set, nine ML models had AUC >0.800 with Gradient Boosting Random Forest (xgboost) [GB RF (xgboost)] achieving the highest AUC of 0.860 and could detect the presence of hibernating myocardium in 21/29 (72.4%) perfusion defects with a precision of 87.5% (21/24), specificity 85.7% (18/21), CA 78.0% (39/50) and F1 Score 0.792. Four models depicted a clear pattern of model interpretability based on the beeswarm SHAP plots. These were GB RF (xgboost), GB (scikit-learn), GB (xgboost), and Random Forest. Conclusion Our study demonstrates the potential of ML in detecting hibernating myocardium using radiomics features extracted from perfusion defects on rest MPI images. This proof-of-concept underscores the notion that radiomics features capture nuanced information beyond what is perceptible to the human eye, offering promising avenues for improved myocardial viability assessment.

AGR – 2 The Multi-Dimensional Role of Neuropsychology in Diagnostically Complex Epilepsy Patients: Refining the Surgical Plan and Post-Operative Expectations

Abstract Objective We present a case highlighting the importance of neuropsychological involvement at all phases of a pre-surgical epilepsy work-up, including clinical interview, neuropsychological evaluation, Wada testing, extra-operative electrical stimulation mapping (ESM), and psychoeducation. Method A 24-year-old, right-handed, Caucasian female presented with non-lesional, intractable left-hemisphere localization-related epilepsy characterized by frequent experiences of déjà vu and autonomic sensations. Results Neuropsychological evaluation revealed weaknesses in processing speed and verbal memory implicating left hippocampal involvement. Wada testing suggested minimal contralateral memory support, indicating poor candidacy for resective surgery. The patient underwent prolonged sEEG monitoring to refine a plan for device implantation (i.e., responsive neurostimulation; RNS). No typical events were captured during extended study. Episodic somatosensory changes were captured, which had no electrographic correlate and were presumed to be non-epileptic. ESM evoked the patient’s classic semiology, elucidating nuanced information about her seizure onset zone and network. Barriers to extended monitoring and consistent, reliable engagement during ESM included depression, anxiety, and trauma; considerable neuropsychologist-led emotional support was required. Typical seizure semiology was elicited during stimulation of entorhinal cortices, posterior and anterior hippocampi, amygdala, cingulate and insula. RNS placement included a depth electrode targeting the medial structures (via a posterior approach) with a sub-temporal strip. Conclusions Involvement of neocortical and mesial temporal structures, coupled with a clear neuropsychiatric component, were believed to be poly-etiological factors to the patient’s presentation. Neuropsychological input at all phases of pre-surgical assessment was instrumental in systematically identifying and addressing her complex needs. Psychotherapeutic intervention was suggested to be equally as important to clinical, long-term patient outcome.

Phylogeny and bioprospecting: The diversity of medicinal plants used in cancer management

Societal Impact StatementAs the second‐leading cause of mortality worldwide, cancer is a major focus of drug discovery research. Traditional plant knowledge can guide the search for undiscovered compounds, but the efficacy of this approach for cancer, a highly complex disease affecting diverse tissues, is unknown. We investigated the patterns underlying plant selection for cancer treatment globally, finding certain lineages are repeatedly targeted. While this indicates therapeutic value, their relatedness with plants used for unrelated ethnobotanical uses suggests that plants are probably selected to treat cancer‐associated symptoms, rather than addressing tumour growth. Careful re‐examination and scoring of ethnobotanical reports may make the prediction of lineages for drug discovery more informative.Summary Cancer is a highly diverse disease and as the second‐leading cause of death worldwide is a focus of drug discovery research. Natural products have been shown to be a useful source of novel molecules for treating cancer. It is likely there are many plants with undiscovered molecules of therapeutic value, however identifying new leads from the vast diversity of plants is very challenging. Traditional knowledge might inform bioprospecting by predicting lineages of plants rich in therapeutically useful molecules. We characterise the phylogenetic diversity of plants used in traditional cancer management using a comprehensive genus‐level phylogeny of angiosperms, and a list of 597 genera used globally to treat different cancers. We phylogenetically predict which lineages may have elevated potential for drug discovery and assess the quality of the prediction. We demonstrate the independent and repeated targeting of specific lineages of plants by different peoples in different parts of the world. However, the lineages we report here as rich in plants used in traditional cancer management coincide with those for other ethnobotanical applications and contain few plants with proven anti‐cancer activity. That the same lineages are used to treat different cancers is suggestive of independent discovery of therapeutic value. However, it is likely that the traditional knowledge explored here is shaped by the selection of plants conferring milder effects for treating wider symptoms, such as tiredness or nausea, rather than for halting tumour growth. Accurate prediction of useful plant lineages for cancer management requires more nuanced information than is commonly provided in ethnobotanical records.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

Beyond Jacobson and Truax: Estimation of Clinical Significance Trajectories in the Coping Power Intervention Using Measurement Error-Corrected Multilevel Modeling

Coping Power (CP) is a preventive intervention that focuses on reducing child externalizing problems. Although it is typically delivered in a group format (GCP), individually delivered CP (ICP) has produced greater mean reductions in externalizing problems. However, standard analysis of randomized trials loses individual-level information regarding which youth improve, fail to improve, or get worse, whereas clinically significant change (CSC) metrics capture information on individual change. The present study is a secondary analysis of an ICP/GCP trial (N = 360) that examines differences in CSC-based individual-level inferences on externalizing. A novel method for assessing CSC under measurement error-corrected multilevel modeling was used, overcoming three limitations of traditional CSC methods: (a) restriction to two time points, (b) use of total scores, and (c) assumption of constant reliability across time and participants. Because of concerns about Type II errors with all CSC methods, an individual-level effect size metric for CSC was also developed. Based on individualized Cohen’s d estimates, individual-level improvements in externalizing from 4th through 11th grades of d ≥ 0.5 were significantly greater in ICP (73%) versus GCP (45%). Further, GCP saw significantly higher percentages of youth with worsening of externalizing, underscoring concerns about diminished effects for GCP. Half of the sample had improvement that was not statistically significant but exceeded d ≥ 0.5, highlighting the susceptibility to Type II errors of CSC’s results based on statistical significance. An examination of ICP/GCP differences under advanced CSC analysis gives more nuanced information than conventional randomized controlled trial analysis and greater precision in estimating individual-level outcomes than standard CSC methodologies.

DISCURSIVE CONSTRUCTION OF NEWS REPORTS ON PEOPLE WITH MENTAL ILLNESS IN MALAYSIAN ONLINE NEWSPAPERS

Background and Purpose: As one of the most common trustworthy sources, online newspapers have been regarded as the primary source of knowledge among the public to obtain reliable and unbiased information. However, in the context of media coverage of people with mental illness, they are often portrayed negatively and regarded as unpredictable and dangerous, resulting in increased stigma and misunderstanding among the public at large. This study examines the discursive strategies used in Malaysian news articles in portraying mental illness and the linguistic devices used to successfully implement these strategies. Methodology: This study investigated 43 news articles published in popular Malaysian online newspapers to examine how issues related to mental health-related illnesses are discursively constructed and the linguistic elements that were employed to represent the complexities of the cases from a discourse analysis perspective. Findings: The results show that the local media use nomination, predication and argumentation strategies to legitimise people with mental illness. They are portrayed negatively and through various linguistic elements, primarily as perpetrators and offenders. Contributions: The study provides media professionals with nuanced information on responsible reporting of mental health issues and uses the media as a platform to destigmatise mental illness among the general public. Keywords: Discourse analysis, online newspapers, mental illness, media practice, stigma. Cite as: Mohd Ali, S. M., Wan Fakhruddin, W. F. W., Wan Adnan, W. N. A., &amp; Azizan, E. A. (2024). Discursive construction of news reports on people with mental illness in Malaysian online newspapers. Journal of Nusantara Studies, 9(2), 371-401. http://dx.doi.org/10.24200/jonus.vol9iss2pp371-401

A dual-critic DQN architecture for lifter assignment in multi-floor semiconductor FAB

This paper proposes a deep reinforcement learning-based lifter assignment algorithm utilising Deep Q-network (DQN) to minimise the total inter-floor delivery time in semiconductor manufacturing. Given the complexities and randomness inherent in manufacturing environments, predicting delivery times poses a significant challenge for companies operating in such domains. To address this challenge and improve the accuracy of delay prediction, we partition the end-to-end delivery process of individual lot systematically, focusing on predicting segment delays rather than the overall end-to-end delay. We introduce a unique dual critic architecture designed to handle these segmented steps. This innovative approach enhances accuracy by capturing nuanced information at each step, which is stored as trajectories. Simulation results substantiate the effectiveness of the proposed architecture, comparing favorably against existing algorithms. We conduct comparative analyses with benchmark algorithms, revealing that the proposed algorithm outperforms other algorithms.

Home on the range: A multi-isotope investigation of ungulate resource partitioning at Ashfall Fossil Beds, Nebraska, USA

During the Miocene (ca. 23.0–5.3 Ma), North America's Great Plains were a broad mosaic of savanna-woodlands inhabited by a diverse ungulate fauna. We investigated resource partitioning among ungulates at Ashfall Fossil Beds State Historical Park (henceforth “Ashfall”) in north-central Nebraska to establish how taxa shared the landscape. This mid-Miocene (11.86 ± 0.13 Ma) site preserves a watering-hole filled with volcanic ash that entombed hundreds of skeletons of medium- and large-bodied ungulates. We analyzed carbon (δ13C), oxygen (δ18O), and strontium (87Sr/86Sr) isotopes in enamel from seven species: the rhinoceros Teleoceras major (n = 13); three horses (Cormohipparion occidentale, Pliohippus pernix, and Pseudhipparion gratum; n = 3 each); two camels (Procamelus grandis and Protolabis heterodontus; n = 1 each); and one small ruminant artiodactyl (Longirostromeryx wellsi; n = 3). Carbon isotope data indicate that all seven ungulates foraged in open habitats dominated by C3 plants (enamel δ13CVPDB range from −9.6‰ to −6.9‰) with little to no evidence of C4 plant consumption. The overall range in δ18OVPDB values is ca. 6‰ (−7.6‰ to −1.3‰), while the range for 87Sr/86Sr is narrow (0.70864–0.70898). Horses have significantly higher δ18O and 87Sr/86Sr than T. major, which we suggest indicates horses foraged in relatively dry habitats, while T. major inhabited wet habitats with different strontium sources. Both camels isotopically resemble horses, and Longirostromeryx wellsi has intermediate 87Sr/86Sr and wide-ranging δ18O values. Plotting Teleoceras and co-occurring ungulates in a model that tests for semi-aquatic behavior using δ18O suggests that T. major from Ashfall was semi-aquatic; ecology and behavior may have varied among Teleoceras species living in different places at different times. This study exemplifies how 87Sr/86Sr data complement δ13C and δ18O analyses when reconstructing ancient environments; they can provide nuanced ecological information even in landscapes with homogeneous bedrock.

Mitigating generative AI inaccuracies in soil biology

Generative AI (GenAI) is becoming a valuable tool for enhancing efficiency and can be used to help foster critical thinking skills among students and possibly assist in hypothesis generation. GenAI can excel at improving original content and to make it more accessible to broader audiences. While GenAI can create stunning images, inaccuracies persist, even with well-designed prompt engineering. Likewise, challenges persist in GenAI processing nuanced information accurately, highlighting the need for foundational knowledge and critical thinking when creating prompts and interpreting GenAI responses.

Making AI Work for Government: Critical Success Factor Analysis Using R-SWARA

This study quantifies what makes Artificial Intelligence (AI) work for government, the critical success factors (CSFs) for successful AI implementation within the Directorate General of Taxes (DGT). Analyzing factors such as technology, organization, process, and environment, the research highlights the importance of organizational readiness, strategic vision, and leadership support to drive successful AI integration within DGT. The dimension of the organization became the most critical factor, followed by technology, process, and environment. The findings offer actionable insights for DGT's decision-making processes, aiding in strategic resource allocation and tailored AI strategy refinement. Furthermore, this research is a valuable reference for other public sector organizations that aim to enhance operational efficiency through the adoption of AI. This study empowers decision makers within the DGT and the wider public sector by providing nuanced information on the critical factors that influence the successful implementation of AI, fostering improved operational efficiency and governance practices.