bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing hybrid models for recognising objects in thermal images at nighttime
Authors: Bandi Maheswari , Rajakumari Reeja Sundaran
Published: April 2024

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.
Stacking classifier method for prediction of human body performance
Authors: Octavianto Noer Rachmat , Wibowo Antoni
Published: April 2024

A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.
Factors driving business intelligence adoption: an extended technology-organization-environment framework
Authors: Subramaniam Radhakrishnan , Palakeel Prashobhan , Arunmozhi Manimuthu , Sridharan Manikandan , Marimuthu Uthayakumar
Published: April 2024

Business intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey.
A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis
Authors: Sai Krishna Thotakura Venkata , Rama Krishna Tummalapalli Siva , Kalime Srinivas , Murali Krishna Chinta Venkata , Neelima Sadineni , PBV Raja Rao
Published: April 2024

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.
Blockchain-based e-voting system in a university
Authors: Marouan Adil , Badrani Morad , Kannouf Nabil , Zannou Abderrahim , Chetouani Abdelaziz
Published: April 2024

The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-of-the-art. exceeds the state-of-the-art in terms of gas cost and execution time.
Deep learning based COVID and Pneumonia detection using chest X-ray
Authors: Kumar Praveen , Rakhimzhanova Mira , Rawat Seema , Orynbek Alibek , Kamra Vikas
Published: April 2024

Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide.
Empowering geological data analysis with specialized software GIS modules
Authors: Baigereyev Dossan , Kasenov Syrym , Temirbekova Laura
Published: April 2024

This research is devoted to the development of a geographic information system (GIS) for the analysis of geological data. It presents two specialized software modules designed to solve complex geological problems related to potential progress to disturbed masses and magnetotelluric sounding. These modules are integrated into the QGIS environment, offering efficient data processing and analysis capabilities, contributing to a deeper understanding of geological structures. The study presents a mathematical model for the problem of magnetotelluric sounding (MTS) and the continuation of potentials towards the perturbed masses, demonstrating numerical results using the developed algorithm. To confirm the accuracy of the model, a comparative analysis was carried out with empirical data for various chemical elements, which showed high accuracy, especially at shallow depths, with an error rate of less than 2%. In addition, the study highlights the importance of powerful GIS for the analysis and interpretation of geological data, including geochemical, geophysical and remote sensing information. The advanced functionality of QGIS simplifies data processing and visualization, which makes it an invaluable tool for geologists and researchers.
Development Paillier's library of fully homomorphic encryption
Authors: Erlanovna Temirbekova Zhanerke , Sakhybay Tynymbayev , Muratovna Abdiakhmetova Zukhra , Gulzat Turken
Published: April 2024

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.
Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach
Authors: Awan Muhammad Zeerak , Arif Muhammad Shoaib , Ul Abideen Mirza Zain , Abodayeh Kamaleldin
Published: April 2024

Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.
Revolutionization of augmented reality in tourism via deep learning
Authors: Essa Yasmin Chuupa , Chaturvedi Saumya , Khurana Shiraz
Published: April 2024

Tourism has become an integral part of social and economic development across the globe. It does not only serve as a recreational activity but also as a source of revenue for the nation. The paper systematically explores the potential enhancements in the tourist experience through cutting-edge technology. Employing deep learning methods, the study specifically concentrates on refining augmented reality encounters for visitors. The proposed approach utilizes deep learning algorithms to optimize and tailor tourists’ augmented reality experiences, addressing current sectoral challenges like customization and engagement shortcomings. The methodology’s selection is predicated on it is capability to elevate user experience, accurately identify objects, offer visual guided tours, integrate historical context, and ultimately propel augmented reality adoption in tourism. Notably, the investigation culminates in a noteworthy average accuracy of 99% when incorporating deep learning to enhance augmented reality in tourism.
Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review
Authors: Sinambela Dewi Pusparani , Rahmatullah Bahbibi , Che Lah Noor Hidayah , Selamat Ahmad Wiraputra
Published: April 2024

Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.
Navigating the cyber forensics landscape a review of recent innovations
Authors: Panigrahi Gyana Ranjana , Barpanda Nalini Kanta , Sethy Prabira Kumar
Published: April 2024

The extensive relevance of digital forensics in today's data-driven environment has been emphasized in this article. The free software and the commercial software community are debatable, despite users and developers often differing views on important topics like software safety and usability. This article primarily uses pre-defined criteria and a platform-oriented approach to examine promising freeware (Magnet Forensics and Sleuth Kit) vs. profitable (ProDiscover and Oxygen Forensic Suite) mobile forensics tools. Under diverse settings, the tools' capacity to develop and analyze forensically sound digital forensic media sources is validated. After erasing data, each media type was tested again after formatting. The study concludes with a comparison matrix that may aid in determining the best-fit option for the investigation's requirements among the tools. The findings indicate the potential for freeware to supplant numerous proprietary applications, as users can opt for freeware instead of incurring costs associated with proprietary software. Furthermore, this perception can be put into practice.
Traffic accident classification using IndoBERT
Authors: Naufal Muhammad Alwan , Girsang Abba Suganda
Published: April 2024

Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.
Remote practical instruction using web browsers
Authors: Kentarou Nagaki , Satoshi Fujita
Published: April 2024

This paper introduces a novel approach to remote coaching, specifically targeting the body movements of learners participating remotely. The proposed system employs a smartphone camera to capture the learner’s body and represent it as a 3D avatar. The instructor can then offer guidance and instruction by manipulating the 3D avatar’s shape, which is displayed on a web browser. The main challenge faced by the system is to enable the sharing and editing of 3D objects among users. Since the HTML5 drag-and-drop feature is inadequate for transforming virtual objects consisting of multiple interconnected rigid bodies, the system tracks the pivot point of the manipulated rigid body. It assigns attributes such as pivot points and action points to each object, extending beyond their 2D screen coordinates. To implement the system, an interactive web application framework following the model-view-view-model (MVVM) architecture is utilized, incorporating Vue.js, Three.js, and Google Firebase. The prototype system takes advantage of the data binding capability of the framework and successfully operates within the 3D space of a web browser. Experimental results demonstrate that it can effectively share transformation information with an average delay of 300 ms.
Memorias y narraciones: una aproximación a la experiencia biográfica de niñas y niños en Colombia
Authors: Verano Camayo Cristian Andres
Published: April 2024

El presente trabajo tuvo el propósito de adelantar una interpretación comprensiva de las narraciones que construyeron niños escolares a partir de la exploración de su experiencia de vida. En un primer momento, se realiza una revisión teórica alrededor de la triada memoria(s), narración y experiencia. En un segundo momento, se presenta la perspectiva metodológica que orientó este trabajo, la cual partió de un enfoque cualitativo de corte hermenéutico que permitió configurar un escenario para la expresión, donde fue posible articular sentidos y significados entorno a la experiencia pasada de los niños protagonistas. Seguidamente, se muestran los hallazgos delineados a partir de un conjunto de tres tramas: (1) tejidos y reinterpretaciones vitales; (2) valor biográfico y artefactos memoriales; (3) narrativas en pugna e irrupciones metafóricas. Para el análisis de la información se trabajaron categorías conceptuales propuestas por Arfuch (2013), lo que posibilitó identificar en los niños miradas sobre sí mismos y sobre los otros que discurren a través de un lenguaje teñido por emociones y sentimientos, el cual trata de comprender un pasado, aproximándose a él, rememorándolo y trasmitiéndolo.
Unlocking near-whole-brain, layer-specific functional connectivity with 3D VAPER fMRI
Authors: Chai Yuhui , Morgan A. Tyler , Xie Hua , Li Linqing , Huber Laurentius , Bandettini Peter A. , Sutton Bradley P.
Published: April 2024

Neuroscientific investigations at the cortical layer level not only enrich our knowledge of cortical micro-circuitry in vivo, but also help bridge the gap between macroscopic (e.g., conventional fMRI, behavior) and microscopic (e.g., extracellular recordings) measures of brain function. While laminar fMRI studies have extensively explored the evoked cortical response in multiple subsystems, the investigation of the laminar component of functional networks throughout the entire brain has been hindered due to constraints in high-resolution layer-fMRI imaging methodologies. Our study addresses this gap by introducing an innovative layer-specific 3D VAPER (integrated VASO and Perfusion contrast) technique in humans at 7 T, for achieving fMRI at high resolution (800 µm isotropic), high specificity (not biased toward unspecific vein signals as BOLD), high sensitivity (robust measurement at submillimeter resolution), high spatial accuracy (analysis in native fMRI space), near-whole-brain coverage (cerebellum not included), and eventually extending layer fMRI to more flexible connectivity-based experiment designs. To demonstrate its effectiveness, we collected 0.8-mm isotropic fMRI data during both resting-state and movie-watching scenarios, established a layer-specific functional connectivity analysis pipeline from individual to group levels, and explored the role of different cortical layers in maintaining functional networks. Our results revealed distinct layer-specific connectivity patterns within the default mode, somatomotor, and visual networks, as well as at the global hubness level. The cutting-edge technique and insights derived from our exploration into near-whole-brain layer-specific connectivity provide unparalleled understanding of the organization principles and underlying mechanisms governing communication between different brain regions.
Reduced cortical complexity in ventromedial prefrontal cortex is associated with a greater preference for risky and immediate rewards
Authors: Bergström Fredrik , Lerman Caryn , Kable Joseph W.
Published: April 2024

In our everyday lives, we are often faced with situations in which we make choices that involve risky or delayed rewards. However, the extent to which we are willing to accept larger risky (over smaller certain) or larger delayed (over smaller immediate) rewards varies across individuals. Here, we investigated the relationship between cortical complexity in the medial prefrontal cortex and individual differences in risky and intertemporal preferences. We found that reduced cortical complexity in left ventromedial prefrontal cortex (vmPFC) was associated with a greater preference for risky and immediate rewards. In addition to these common structural associations in left vmPFC, we also found associations between lower cortical complexity and a greater preference for immediate rewards that extended into the left dorsomedial prefrontal cortex and right vmPFC. Furthermore, these structural associations occurred in a context where a preference for risky rewards was correlated with a preference for delayed rewards across individuals. These results suggest that risk and intertemporal preferences are distinct but related, and likely influenced by multiple neurocognitive processes, with cortical complexity in vmPFC reflecting one shared aspect possibly related to impulsiveness in terms of risky and impatient economic choice. Future work should elucidate the complex relationships between brain structure and behavioral preferences.
Reward Reinforcement Creates Enduring Facilitation of Goal-directed Behavior
Authors: Ballard Ian C. , Waskom Michael , Nix Kerry C. , D’Esposito Mark
Published: April 2024

Stimulus–response habits benefit behavior by automatizing the selection of rewarding actions. However, this automaticity can come at the cost of reduced flexibility to adapt behavior when circumstances change. The goal-directed system is thought to counteract the habit system by providing the flexibility to pursue context-appropriate behaviors. The dichotomy between habitual action selection and flexible goal-directed behavior has recently been challenged by findings showing that rewards bias both action and goal selection. Here, we test whether reward reinforcement can give rise to habitual goal selection much as it gives rise to habitual action selection. We designed a rewarded, context-based perceptual discrimination task in which performance on one rule was reinforced. Using drift-diffusion models and psychometric analyses, we found that reward facilitates the initiation and execution of rules. Strikingly, we found that these biases persisted in a test phase in which rewards were no longer available. Although this facilitation is consistent with the habitual goal selection hypothesis, we did not find evidence that reward reinforcement reduced cognitive flexibility to implement alternative rules. Together, the findings suggest that reward creates a lasting impact on the selection and execution of goals but may not lead to the inflexibility characteristic of habits. Our findings demonstrate the role of the reward learning system in influencing how the goal-directed system selects and implements goals.
Neural Tracking of Perceived Parent, but Not Peer, Norms Is Associated with Longitudinal Changes in Adolescent Attitudes about Externalizing Behaviors
Authors: Do Kathy T. , Prinstein Mitchell J. , Lindquist Kristen A. , Telzer Eva H.
Published: April 2024

Adolescents' perceptions of parent and peer norms about externalizing behaviors influence the extent to which they adopt similar attitudes, yet little is known about how the trajectories of perceived parent and peer norms are related to trajectories of personal attitudes across adolescence. Neural development of midline regions implicated in self–other processing may underlie developmental changes in parent and peer influence. Here, we examined whether neural processing of perceived parent and peer norms in midline regions during self-evaluations would be associated with trajectories of personal attitudes about externalizing behaviors. Trajectories of adolescents' perceived parent and peer norms were examined longitudinally with functional neuroimaging (n = 165; ages 11–16 years across three waves; 86 girls, 79 boys; 29.7% White, 21.8% Black, 35.8% Latinx, 12.7% other/multiracial). Behavioral results showed perceived parent norms were less permissive than adolescents' own attitudes about externalizing behaviors, whereas perceived peer norms were more permissive than adolescents' own attitudes, effects that increased from early to middle adolescence. Although younger adolescents reported less permissive attitudes when they spontaneously tracked perceived parent norms in the ventromedial and medial pFCs during self-evaluations, this effect weakened as they aged. No brain–behavior effects were found when tracking perceived peer norms. These findings elucidate how perceived parent and peer norms change in parallel with personal attitudes about externalizing behaviors from early to middle adolescence and underscore the importance of spontaneous neural tracking of perceived parent norms during self-evaluations for buffering permissive personal attitudes, particularly in early adolescence.
Multi-hierarchy Network Configuration Can Predict Brain States and Performance
Authors: Wang Bin , Yuan Yuting , Yang Lan , Huang Yin , Zhang Xi , Zhang Xingyu , Yan Wenjie , Li Ying , Li Dandan , Xiang Jie , Yang Jiajia , Liu Miaomiao
Published: April 2024

The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
Early Electrophysiological Correlates of Perceptual Consciousness Are Affected by Both Exogenous and Endogenous Attention
Authors: Doradzińska Łucja , Bola Michał
Published: April 2024

It has been proposed that visual awareness negativity (VAN), which is an early ERP component, constitutes a neural correlate of visual consciousness that is independent of perceptual and cognitive mechanisms. In the present study, we investigated whether VAN is indeed a specific marker of phenomenal awareness or rather reflects the involvement of attention. To this end, we reanalyzed data collected in a previously published EEG experiment in which awareness of visual stimuli and two aspects that define attentional involvement, namely, the inherent saliency and task relevance of a stimulus, were manipulated orthogonally. During the experimental procedure, participants (n = 41) were presented with images of faces that were backward-masked or unmasked, fearful or neutral, and defined as task-relevant targets or task-irrelevant distractors. Single-trial ERP analysis revealed that VAN was highly dependent on attentional manipulations in the early time window (140–200 msec), up to the point that the effect of awareness was not observed for attentionally irrelevant stimuli (i.e., neutral faces presented as distractors). In the late time window (200–350 msec), VAN was present in all attentional conditions, but its amplitude was significantly higher in response to fearful faces and task-relevant face images than in response to neutral ones and task-irrelevant ones, respectively. In conclusion, we demonstrate that the amplitude of VAN is highly dependent on both exogenous (stimulus saliency) and endogenous attention (task requirements). Our results challenge the view that VAN constitutes an attention-independent correlate of phenomenal awareness.
Research and Creative Activity in the Design Field: Intersections of Science, Art, and Engineering
Authors: Gryshchenko Ivan , Yezhova Olga , Pashkevich Kalina , Biryukova Yulia
Published: April 2024

This case study assesses the development of research competence among graduate design students through a Ukrainian–Chinese English-language remote learning program. The quasi-experimental study included one group of 19 students who enrolled in the course, titled “Research Methodology and Modern Technology of Design Activity.” The extent to which the course improved their confidence in using the English language and their self-assessed readiness to engage in professional activities at the intersection of science, design, and engineering were used as the outcome measures. After 3 weeks of the program, all students reported improved readiness to participate in the scientific community through the English language, demonstrating their ability to perform research and creative activity in the field of design.
History of Error: Jacob Taubes’s Apocalyptic Interpretation of Martin Heidegger’s Vom Wesen der Wahrheit
Authors: Styfhals Willem
Published: April 2024

Through a close reading of the opening pages of Occidental Eschatology, this paper analyzes how Jacob Taubes relied on Martin Heidegger’s philosophy to understand the nature of eschatology. Taubes implemented Heidegger’s notions of truth, error, and history from his seminal essay “On the Essence of Truth,” (mis)interpreting the essay by ascribing an eschatological meaning to it. This surprisingly allowed him to find in Heidegger a model to come to terms with the Jewish experience of history. In order to fully understand this untimely use of Heidegger’s work, the paper turns its attention to the correspondence of Jacob Taubes with his first wife, Susan Taubes. The conclusion asks to what extent Jacob Taubes’s uncritical use of Heidegger’s thinking plays into the latter’s notorious antisemitism.
Hermeneutics before Ontology: How Later Levinas Better Understands Heidegger
Authors: Lapidot Elad
Published: April 2024

This paper examines Emmanuel Levinas’s philosophical development from Totality and Infinity to Otherwise than Being as a self-critique and revised understanding of Martin Heidegger. It focuses on later Levinas’s analysis of language in terms of the difference between Saying and Said. For Levinas, the Said represents the betrayal of ethical Saying into ontological essence. This echoes Heidegger’s notion of the forgetfulness of Being in beings. However, Levinas critiques Heidegger’s own philosophy as remaining within the Said. The paper explores three strategies of “unsaying” that Levinas discerns in Western philosophy to disrupt ontological Said: ontology itself, skepticism, and philosophical conversation. It argues that Levinas ultimately seeks an alternative “prophetic” mode of language beyond philosophy that enacts difference, which the paper relates to Talmud. The paper claims that the later Levinas no longer simply opposes Greek to Jewish thought, but rather seeks to deploy a specifically Jewish form of language to interrupt Western metaphysics from within.
Elective Affinity: the Geist of Israel in Heidegger’s Free Use of the German National
Authors: Fagenblat Michael
Published: April 2024

This article examines the way Heidegger’s account of the unique spiritual mission of the German people is haunted by certain conceptions of the election of Israel. I argue that Heidegger’s political ontology is informed by three conceptions of the mission of Israel: biblical salvation history, kabbalistic panentheism, and Germany literary Hebraism. To link these disparate historical phenomena to Heidegger’s account of the mission of being German, I develop a methodological approach for understanding Heidegger’s “free use of the national” that accounts for the way it binds different sources into a new figure that is haunted by the spirit of Israel.
Greek and Latin Texts on the Harp and Similar Instruments in Byzantine Times
Authors: Najock Dietmar
Published: April 2024

In Byzantine sources, both literary and iconographic, the harp occurs surprisingly seldom. Harpa as a term for a musical instrument is first encountered in Venantius Fortunatus. It is usually translated as ‘harp’, but at that time it most likely meant a lyre. Since the Carolingian Renaissance – when in the West often the harp, in Byzantium the psaltery gradually took the place of the lyre – the old name apparently passed to the new instrument, as similarly in the cases of cithara and ψαλτήριον. The name harpa was apparently adopted into Greek only at the end of the 14th century under Italian influence. Πλινθίον probably referred to the rectangular psaltery in Byzantium. Ἀχιλλιακόν may have meant a rare instrument in Byzantium, probably due to a misunderstanding of our Fortunatus passage.
A Passion for Virtue? Appeals to Morality and Emotions in European Parliament Climate Change Debates
Authors: Salgado Rosa Sanchez , Bos Linda
Published: April 2024

Although it is well acknowledged that moral foundations have an emotional component, little is known about the relation between moral foundations and emotions in current political and public debates. The analysis of this relation is crucial to better understand causal pathways from affect and emotion to climate change action and behavior. Employing Emotion Discourse Analysis, this study analyzes appeals to moral foundations in European Parliament (EP) plenary debates on the topic of climate change, and their relation to emotions between 1994 and 2022. We show that the relation between moral foundations and emotions depends on the narratives put forward by policymakers from different political groups. After linking narratives, moral foundations and emotions, we hypothesize how the identified combinations affect political action. We show that narratives promoting action-oriented and effective appeals to emotions, which are key in advancing environmental protection, are not the most prominent in EP debates.
Not So Vats: How Early Is Too Early in the Operative Management of Patients with Traumatic Hemothorax?
Authors: Uma Chinweotuto V. , Risinger William B. , Nath Suhail , Pera Samuel J. , Smith Jason W.
Published: April 2024

Background

Video-assisted thoracoscopic surgery (VATS) is a practical resource in the management of traumatic hemothorax. However, it carries inherent risks and should be mobilized cost-effectively. In this study, we investigated the ideal VATS timing using cost analysis.

Methods

617 cases of unilateral traumatic hemothorax from 2012 to 2022 were identified in our trauma database. We extracted encounter cost, length of stay (LOS), and operative cost information. Using Kruskal-Walli’s test, we compared the cost and LOS for patients who underwent VATS or continued nonoperative management in the first 7 days of admission. Additionally, we computed the daily proportion of patients initially managed nonoperatively but ultimately underwent VATS. P-values <.05 were considered significant.

Results

The median encounter cost of cases managed operatively before hospital day 4 (HD4) was higher than those managed nonoperatively. This difference was $63k on HD2 ( P-value .07) and was statistically significant for HD3 (difference of $65k, P-value .02). The median LOS with operational management on HD2 and 3 was 7 and 6 respectively vs median LOS of 2 and 3 with nonoperative management on those days ( P-value <.001, .01 respectively). The proportion of patients who failed nonoperative management did not change from baseline until HD4 (23% (95% CI 19.7, 26.3) vs 33.9% (95% CI 28.3, 39.6), P-value <.001).

Discussion

Early mobilization of VATS before hospital day 4 increases the overall hospital cost without offering any length of stay benefit. Continuing nonoperative management longer than 4 days is associated with a high failure rate and a costlier operation.
“Freedom is not free”: Visual activism and dispersed resistance in Hong Kong’s anti-extradition bill protests
Authors: Suglo Ignatius GD
Published: April 2024

Graffiti is widely used in social movements globally, yet media and communication research disproportionately focus on the role of social and new media technologies in protest movements. In this paper I ask why university students – a tech-savvy generation – resorted to graffiti and why campus graffiti were not widely circulated on social media during the Hong Kong anti-Extradition Law Amendment Bill (ELAB) protests. I argue that graffiti enables dispersed resistance and is one way to mobilize, voice dissent, and preserve memory in an increasingly surveilled and evolving repressive media environment. I pursue this argument by analyzing graffiti photographed on university campuses during the anti-ELAB protests. Situating graffiti within protest culture in Hong Kong, I conclude that graffiti are not always circulated on digital/social media to reach a broader audience. In times of crises, not reaching a wider audience is a manifestation of dispersed resistance in a hybrid media environment.
The relationships of platelet-derived growth factor, microvascular proliferation, and tumor cell proliferation in canine high-grade oligodendrogliomas: Immunohistochemistry of 45 tumors and an AFOB-01 xenograft mouse model
Authors: Yoshida Kio , Chambers James K. , Uchida Kazuyuki
Published: April 2024

High-grade oligodendroglioma (HGOG) is the most common type of glioma in dogs and expresses platelet-derived growth factor receptor-α (PDGFR-α). Microvascular proliferation is often observed in HGOG. Therefore, the present study investigated the functional relationships between PDGFR-α, microvascular proliferation, and tumor cell proliferation in canine HGOG. The expression of PDGFR-α and PDGF-subunit A (PDGF-A) in tumor cells, as well as endothelial cells and pericytes of tumor-associated microvascular proliferations, in 45 canine HGOGs were examined immunohistochemically. Microvascular proliferation was observed in 24/45 cases (53%). PDGFR-α expression in tumor cells and microvascular proliferations was observed in 45/45 (100%) and 2/24 cases (8%), respectively. Furthermore, PDGF-A expression in tumor cells and microvascular proliferations was detected in 13/45 (29%) and 24/24 cases (100%), respectively. In vitro, stimulation of the canine HGOG cell line AOFB-01 with PDGF-A showed that the doubling time of AOFB-01 cells was significantly shorter with PDGF-A than without PDGF-A. Crenolanib (a PDGFR inhibitor) inhibited AOFB-01 cell proliferation. In vivo, the AOFB-01 xenograft mouse model was treated with crenolanib. Tumor xenografts were smaller in crenolanib-treated mice than in untreated control mice. PDGFR-α expression in tumor cells and PDGF-A expression in microvascular proliferations and tumor cells suggest autocrine and paracrine effects of PDGF-A in canine HGOG. The results of in vitro assays indicate that canine HGOG expresses functional PDGFR-α, which responds to PDGF-A. Therefore, PDGF-A produced by microvascular proliferations and tumor cells may promote the proliferation of PDGFR-α-expressing tumor cells in canine HGOG. PDGFR-α signaling has potential as a therapeutic target.
Clinical Assessment of Magnetic Resonance Spectroscopy and Diffusion-Weighted Imaging in Diffuse Glioma: Insights Into Histological Grading and IDH Classification
Authors: Zhang Han-Wen , Zhang Hong-Bo , Liu Xiao-Lei , Deng Hua-Zhen , Zhang Yu-Zhe , Tang Xu-Mei , Lin Fan , Huang Biao
Published: April 2024

Purpose:

To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase ( IDH) classification in adult diffuse gliomas.

Methods:

A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann–Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan–Meier test.

Results:

The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade ( P < .01). Subsequent application of Dunn’s test showed significant differences in ADC mean among each histological grade ( P < .01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas ( P < .01). The Mann–Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification ( P < .01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI’s ADC mean parameter shows significant distinctions in both histological grade and IDH classification.
Exploring the Impact of Rehabilitation and Custody Orientations on Workplace Experiences of Juvenile Probation and Detention Officers
Authors: Rhineberger Gayle , Mack Kristin Y.
Published: April 2024

Prior research has suggested that juvenile justice staff’s support for rehabilitation is important for the success of their juvenile clients. The purpose of this article was to assess how rehabilitation and punishment orientations impacted burnout, secondary trauma, job stress, and job satisfaction among juvenile probation and detention officers. Results indicated that having more of a rehabilitation orientation did not demonstrate direct effects on the workplace outcomes included in this study. However, staff who indicated more of a punishment orientation were less likely to report emotional exhaustion and personal accomplishment, as well as less job stress, but were more likely to report feelings of depersonalization.
Assessing Clinical Profile and Short-term Outcomes of Neonates Retrieved by Neonatal Transport Service and Their Co-relation with Neonatal Severity Illness Scores: An Observational Study
Authors: Reddy Neetha M. , Athreya Rajath
Published: April 2024

Introduction

Newborn transport is a key component of neonatal perinatal care, and its delay is attributed highly to neonatal mortality.

Aim

Our study aimed to assess the clinical profile and short-term outcomes (<48 hours of admission) and to apply the severity illness scores to know the impact on outcomes of retrieved neonates. We used Transport Risk Index of Physiological Stability (TRIPS II) score to correlate with short-term morbidities and Score of Neonatal Physiology Perinatal Extension (SNAPPE II) to correlate with overall outcomes of retrieved neonates.

Materials and Methods

It was an observational study done on 160 out-born neonatal retrievals over 13 months by a dedicated transport team at a level III NICU in Bengaluru, India. A validated TRIPS II score was recorded pre-transport, at Admission and at 12 and 24 hours of admission, and the SNAPPE II score was recorded at admission. An ROC curve was used to predict the utility of these scores.

Results

Among 141 eligible neonates, the common indication for transport was respiratory distress syndrome 57 (40.4%). Extreme pre-terms (<28 weeks; p = .001) and ELBW (<1000 g; p = .001) are associated with an increased mortality rate. The area under the curve (AUC) for TRIPS II in predicting pulmonary bleed (AUC of ROC 0.866, 0.833, 0.823, 0.836), IVH (0.776, 0.79, 0.78, 0.77), worsening of respiratory settings (0.786, 0.814, 0.78, 0.79), and increase in ionotropic support (0.882, 0.904, 0.904, 0.902) at pre-transport, at admission, at 12 hours, and at 24 hours, respectively, was good. The AUC for SNAPPE II was 0.895, which is good in predicting mortality.

Conclusion

A dedicated neonatal transport service can reduce the mortality rate significantly to 7.8%. TRIPS II and SNAPPE II scores were good in predicting the morbidities and mortality in retrieved neonates.