bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent decision-making in healthcare telemonitoring via forward-backward chaining and IoT
Authors: Fahanani Agwin Fahmi , Harbiyanti Novita Titis , Nurvandy Nurvandy , Fitri Fitri , Murtono Ari , Kamajaya Leonardo
Published: February 2024

Healthcare telemonitoring has emerged as a promising approach to remotely monitor patients remotely, enabling timely intervention and personalized care. Internet of things (IoT) device-generated patient data necessitates innovative solutions for intelligent healthcare decision-making, as current methods struggle to provide timely, context-aware, and data-driven recommendations, resulting in suboptimal patient care. This study aims to develop an intelligent decision-making framework for healthcare telemonitoring by leveraging forward-backward chaining and IoT technology. The research focuses on a system using forward-backward chaining algorithms to analyze real-time patient data from IoT devices. It utilizes machine learning models to adapt to changing conditions and refine decision-making, demonstrating its ability to provide real-time context-aware recommendations. Temperature, blood pressure, oxygen level, and heart rate measurement errors are 2.01%, 1.74 to 2.13%, 0.61%, and 1.45%, respectively. The success rate of early disease diagnosis using an expert system is 81%, with an average application interface responsiveness time of 4.978 s. The integration of IoT data with intelligent decision-making algorithms in healthcare telemonitoring has the potential to revolutionize patient care. However, future work should focus on scalability and interoperability for diverse healthcare settings.
Fractional-order PID controller tuned by particle swarm optimization algorithm for a planar CDPR control
Authors: Aboud Hemama , Amouri Ammar , Cherfia Abdelhakim , Bouchelaghem Abdelaziz Mahmoud
Published: February 2024

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs.
An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization
Authors: Krishnamoorthy Latha , Raju Ammasandra Sadashivaiah
Published: February 2024

In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establishes a robust authentication system, countering vulnerabilities in unimodal methods. This approach leverages ECG's robustness against spoofing attacks and lip's fine-grained behavioral cues for comprehensive authentication. Ensemble learning techniques, from majority voting to advanced methods, harness the strengths of individual models, improving accuracy, reliability, and generalization. Moreover, ensemble learning detects anomalies, enhancing security. The study incorporates ECG signal filtering and lip region extraction as preprocessing, uses wavelet transform for ECG features, SIFT for lip image features, and employs greywolf optimization for feature selection. Ultimately, a voting-based ensemble classifier is applied for classification, showcasing the potential of this integrated approach in fortified security measures.
Detecting attacks on e-mail
Authors: Fang Yujia , Mogos Gabriela
Published: February 2024

E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Na¨ıve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored.
The effect of contact pressure on photoplethysmography
Authors: Gong Xin , Yu Chengbo , Xuan Yiguo
Published: February 2024

In the bioinformation photoplethysmography (PPG) measurement, the precision and repeatability could be impacted by the contact pressure between the optical sensor and the measurement site. Taking the finger’s geometrical, mechanical, and optical characteristics into consideration, finite element models and Monte Carlo (MC) simulation methods were used to quantitatively analyze the effects of the deformation of different finger layers under contact pressure on its optical parameters and PPG signals during fingertip spectroscopic detection. Firstly, a 3D axisymmetric finger model was established, pelican optimization was used to find the parameter lamp that caused the simulation to best match the finger pressing behavior, modeled the deformation of each layer, and quantified the changes in their absorption and scattering coefficient. Then, before and after pressure application, photon propagation in the reflectance and transmittance modalities within the diastolic and systolic finger tissues at 660 nm and 940 nm were studied by MC simulation. The result shows that contact pressure significantly altered the thickness of the dermis and subcutaneous tissues, a decrease in tissue thickness caused an increase in optical coefficient, which resulted in a reduction in normalized pulsatile reflectance and a boost in transmittance, and the change was dependent on wavelength.
AES-128 reduced-round permutation by replacing the MixColumns function
Authors: Baladhay Jerico S. , De Los Reyes Edjie M.
Published: February 2024

Ensuring the protection of digital data is of utmost importance in our current reliance on network operations. However, security measures such as data encryption often result in decreased performance speed. This paper enhanced the 128-bit version of the advanced encryption standard (AES) by substituting the MixColumns function with a permutation-based approach and decreasing the overall number of rounds. The evaluation results indicate a substantial enhancement in the speed of encryption and decryption, with a 76.76% improvement in encryption time and a 55.46% improvement in decryption time. Furthermore, it is important to mention that the modifications implemented in the standard AES did not compromise its security in relation to the strict avalanche criterion. The avalanche effect of the modified AES is 52.92%, surpassing the minimum requirement of 50%. Finally, the modified AES demonstrated a 31.12% increase in throughput for encryption and a 25.50% increase for decryption when compared to the original AES, using the sample dataset.
Enhancing security mechanisms for robot-fog computing networks
Authors: Sakhi Abdlehak , Mansour Salah-Eddine , Sekkaki Abderrahim
Published: February 2024

The evolution from conventional Internet usage to the internet of things (IoT) is reshaping communication norms significantly. Cloud computing, while prevalent, faces challenges like limited capacity, high latency, and network failures, especially when handling connected objects, leading to the emergence of fog computing as a more suitable approach for IoT. However, establishing secure connections among heterogeneous IoT entities is complex due to resource disparities and the unsuitability of existing security protocols for resource-constrained devices. This article explores fog computing's architecture, drawing comparisons with cloud computing while emphasizing its significance within the realm of IoT. Moreover, it delves into the practical application of fog computing within the context of the robot teacher project. Subsequently, our exploration introduces an advanced mutual authentication protocol, centered around hashed message authentication code (HMAC), aimed at enhancing the security infrastructure between the robot and the fog computing server.
Improving web-oriented information systems efficiency using Redis caching mechanisms
Authors: Privalov Maksim Vladimirovich , Stupina Mariya Valerevna
Published: February 2024

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.
A hybrid data mining for predicting scholarship recipient students by combining K-means and C4.5 methods
Authors: Hendri Halifia , Andrianof Harkamsyah , Robianto Riska , Awal Hasri , Putra Okta Andrica , Wijaya Romi , Gusman Aggy Pramana , Hafizh Muhammad , Pondrinal Muhammad
Published: February 2024

This scholarly investigation delves into the strong desire for academic scholarships within the student body, especially prominent among socioeconomically disadvantaged individuals. The study aims to formulate a hybrid data mining paradigm by synergizing the K-means and C4.5 methodologies. K-means is applied for clusterization, while C4.5 facilitates prediction and decision tree instantiation. The research unfolds in sequential phases, commencing with data input and progressing through meticulous pre-processing, encompassing data selection, cleaning, and transformation. The novelty lies in successfully integrating the K-means and C4.5 methodologies, culminating in the hybrid data mining method. The dataset comprises 200 students seeking scholarships, revealing effective stratification into three clusters—cluster 0, cluster 1, and cluster 2—with 119, 48, and 33 students, respectively. The K-means method proves highly suitable, especially when combined with C4.5, for predicting scholarship recipients. A subset of 81 students from clusters 1 and 2 undergoes predictive modeling using C4.5, resulting in a commendable 85% accuracy, with 17 accurate forecasts and 3 minor inaccuracies. This research significantly enhances scholarship selection efficiency, particularly benefiting socioeconomically disadvantaged students.
An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy
Authors: Bouali Hamid , Benhala Bachir , Guerbaoui Mohammed
Published: February 2024

This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multi-objective artificial bee colony (MOABC), is compared to other state-of-the-art algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems.
Towards an automatic classification of welding defect by convolutional neural network and robot classifier
Authors: Salah Nissabouri , Hamza Ennadafy , Mustapha Jammoukh , Mansouri Khalifa
Published: February 2024

The control process of welding requires manual operations, and this consumes time. Robot classifier can help by automatic detection of welding defect and by taking rapid actions to correct in situ the defect. This paper presents a convolutional neural network (CNN) model developed to classify the welding defect like splash, twisty, overlap, edge and copper adhesion based on machine vision. Using a resistance spot welding (RSW) dataset the CNN model was trained and evaluated to achieve the best performance. The batch size was varied to quantify its effect on the precision of the model. The model can predict the type of welding surface by confidence of 99.86%.
Analysis and design on acceptance of blockchain based e-voting system
Authors: Shukla Ambuj , Mishra Debani Prasad , Pattnaik Anwesh , Salkuti Surender Reddy
Published: February 2024

Elections are a critical aspect of democratic governance, providing citizens with the power and right to express their views. A secure voting system with innovative features can improve this process. Blockchain technology is considered a disruptive innovation, and its potential for enhancing the e-voting system is significant. The modern voting system is focusing more on blockchain technology to strengthen and secure the process. Blockchain is a reliable, decentralized database that can offer increased security compared to electronic voting machines (EVMs). This research paper presents a detailed study of the design, smart contracts, evaluation of action, and survey on the acceptance of blockchain-based e-voting systems. It examines the requirements for such a system and provides an understanding of the model. As the acceptance of information technology-based services and products increases, future innovation in the e-voting system may depend on blockchain technology. The survey conducted in this paper explores the differences in opinion based on gender, age, and profession among eligible voters from India regarding the acceptance of blockchain technology-based secure e-voting systems. The analysis of these differences sheds light on the potential for blockchain-based e-voting systems to enhance trust and security in the voting process.
A scoping review of artificial intelligence-based robot therapy for children with disabilities
Authors: Yahya Rusnani , Jailani Rozita , Hanapiah Fazah Akhtar , Zakaria Nur Khalidah
Published: February 2024

The integration of artificial intelligence (AI)-based robot therapy (AIBRT) has become prominent in addressing the needs of children with disabilities, including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), learning disabilities, and speech delays. However, questions arise regarding the effectiveness of different AI techniques in enhancing therapy for children with specific needs. This review explores current literature on AIBRT for children with disabilities, aiming to understand the efficacy and potential of various AI techniques in improving their therapy. This paper presents a comprehensive search of research articles published from 2019 to September 2023. 39 articles focusing on AI-based robot platforms, the employed treatment or therapy methods, assessment procedures during therapy, and the variables or parameters used to measure intervention effectiveness have been discussed in detail. These AI-based robot platforms have been utilized to engage individuals diagnosed with ASD, offering therapeutic interventions and assessments. In conclusion, the integration of AI and robotics in therapy shows promise for enhancing the development and quality of life for children with disabilities. The findings of this review have implications for therapists, practitioners, and researchers interested in incorporating AI applications into therapy practices. This integration can lead to improved therapy outcomes, optimized children’s development, and enhanced quality of life.
Authentication schemes in wireless internet of things sensor networks: a survey and comparison
Authors: Phalaagae Pendukeni , Zungeru Adamu Murtala , Sigweni Boyce , Rajalakshmi Selvaraj
Published: February 2024

The proliferation of wireless sensor networks (WSNs) fuels internet of things (IoT's) rapid global development, connecting diverse devices. IoT transforms devices into intelligent entities delivering exceptional services. This work addresses IoT authentication gaps through a comprehensive survey, analyzing recent works and exploring techniques in various applications. It includes a comparative analysis of authentication schemes, evaluating Bi-Phase authentication scheme (BAS) in WSNs. BAS outperforms sensor protocol for information via negotiation (SPIN), broadcast session key protocol (BROSK), and localized encryption and authentication protocol (LEAP), resulting in lower energy consumption and higher efficiency. With energy efficiency at 60 Kb/J for 25 nodes, BAS focuses on power optimization and lightweight security measures, reducing energy consumption, maximizing efficiency, and extending WSN lifespan. The evaluation, conducted using MATLAB/Simulink, demonstrates BAS's superiority, achieving 10 J, 12 J, 14 J, and 15 J energy consumption for 25 nodes during simulation, showcasing its effectiveness and future potential in advancing IoT authentication.
A hybrid deep learning approach for enhanced network intrusion detection
Authors: Prabu K. , Sudhakar P.
Published: February 2024

The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.
An ensemble deep learning model for automatic classification of cotton leaves diseases
Authors: Kukadiya Hirenkumar , Arora Nidhi , Meva Divyakant , Srivastava Shilpa
Published: February 2024

Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models.
Pothole detection in bituminous road using convolutional neural network with transfer learning
Authors: Tripathi Mukesh Kumar , Baswaraj Donagapure , Deshmukh Shyam , Misal Kapil , Bhosle Nilesh P. , Sangve Sunil Mahadev
Published: February 2024

The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.
Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach
Authors: Mohamed Ariff Mohamed Imran , Raja Salim Rasyidin Muhammad Zaharin , Azizan Azilawati , Ahmad Samsiah , Arshad Noreen Izza , Abdullah Mohammad Nasir
Published: February 2024

This study delves into the sentiment analysis of courier services in Malaysia through the lens of tweets. With the escalating reliance on courier services for daily parcel deliveries, understanding public sentiment becomes imperative. Through a systematic methodology encompassing data gathering, preparation, alteration, analysis, modeling, and deployment, this study aims to furnish valuable insights and recommendations. A dynamic graphical representation of sentiment scores is presented for intuitive interpretation and informed decision-making. The study's significance lies in empowering Malaysians to make judicious choices regarding courier services, thereby fostering transparency in the industry. The study proposes several avenues for future enhancements, including the integration of autocorrect features, diversifying data sources and languages, incorporating deep learning techniques, and developing a user-friendly interface for enhanced visualization. This study serves as a guiding resource, illuminating sentiments surrounding courier services in Malaysia and paving the way for future advancements in sentiment analysis within this domain.
Multi-person interaction in collaborative virtual conference for Metaverse
Authors: Li Yingying , Ismail Ajune Wanis
Published: February 2024

Virtual conferences have successfully solved the challenges of traditional face-to-face conferences that can’t be carried out smoothly due to issues such as venue, transportation, and the pandemic. More importantly, as virtual conferences continue to develop and mature, they provide a virtual environment for multi-person collaboration, which is impossible to achieve with traditional face-to-face conferences. This technological innovation is expected to bring significant cost savings to various fields such as medical care, education, and industry. This paper proposes a unity-based multi-party collaborative virtual conference model. The model takes automobiles as the theme, which can realize the design and modification of the appearance of the automobiles by multiple people in the same space, and also supports operations such as the disassembly and installation of the automobile engine. This model is designed to deepen our understanding of multi-person collaboration capabilities in virtual conferences, a feature that is expected to significantly improve teamwork efficiency.
SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques
Authors: Moorthy Chellapilla V. K. N. S. N. , Tripathi Mukesh Kumar , Joshi Suvarna , Shinde Ashwini , Zope Tejaswini Kishor , Avachat Vaibhavi Umesh
Published: February 2024

We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.
Polynucleotides and Hyaluronic Acid (PN-HA) Mixture With or Without Deproteinized Bovine Bone Mineral as a Novel Approach for the Treatment of Deep Infra-Bony Defects: A Retrospective Case-Series
Authors: Cairo Francesco , Cavalcanti Raffaele , Barbato Luigi , Nieri Michele , Castelluzzo Walter , di Martino Maria , Pilloni Andrea
Published: February 2024

Polynucleotides and Hyaluronic Acid (PN-HA) mixture showed several effects in modulation of healing process. The aim of this study was to assess the safety and clinical performance of PN-HA alone or in association with Deproteinized Bovine Bone Mineral (DBBM) with papillary preservation flaps (PPF) in the treatment of residual pockets. A total of 43 patients with 55 infra-bony defects were recruited; 30% were smokers. The mean baseline Probing Depth (PD) was 7.7 ±1.9 mm with a corresponding mean recession (Rec) of 1.9± 1.3 mm. The depth of infra-bony defect at the surgical measurement was 5.2±2.1 mm. DBBM was applied at 56% of the defects considered as not-containing based on clinical judgment. Healing was uneventful at all sites. After one year, PD reduction was 4.4±1.8 mm with a Rec increase of 1.0 ±0.8 mm. Detected bone fill at x-ray was 3.5 ± 1.9mm. The multilevel analysis showed that absence of smoking habits was associated with improved PD reduction (P =0.026) and bone gain (P= 0.039). PN-HA mixture is a safe product for periodontal surgery and seems to promote clinical benefit in the treatment of residual pockets associated to infra-bony defects.
Reversible Cerebral Vasoconstriction Syndrome and Female Sex: A Narrative Review
Authors: Nelson Sarah E.
Published: February 2024

Reversible cerebral vasoconstriction syndrome (RCVS) refers to segmental, multifocal constriction of intracranial arteries along with acute headache and resolves within weeks. It occurs more commonly in women, and 1 well-known manifestation of RCVS is postpartum angiopathy. Furthermore, the female sex is included in scoring systems designed to assist with diagnosing RCVS. Nonetheless, the literature is mixed regarding the true role of female and pregnancy-related factors in the pathophysiology of RCVS, and it is similarly unclear whether management of this disorder differs by sex. Given the association of RCVS with female sex and the importance of highlighting, recognizing, and managing stroke etiologies in women, herein, the author reviews what is currently known and unknown about the topic of RCVS in women.
Systematic cross-sectional age-associations in global fMRI signal topography
Authors: Nomi Jason S. , Bzdok Danilo , Li Jingwei , Bolt Taylor , Chang Catie , Kornfeld Salome , Goodman Zachary T. , Yeo B.T. Thomas , Spreng R. Nathan , Uddin Lucina Q.
Published: February 2024

The global signal (GS) in resting-state functional MRI (fMRI), known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here, we show distinct linear and curvilinear relationships between GS topography and age in a cross-sectional sample of individuals (6-85 years old) representing a significant portion of the lifespan. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across age. The thalamus has stronger contributions to the GS in older-age individuals compared with younger-aged individuals, while the putamen has stronger contributions in younger individuals compared with older individuals. The subcortical nucleus basalis of Meynert shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where contributions of the GS are stronger at early and old age, and weaker in middle age. This differentiation between subcortical and cortical brain activity across age supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on age across the lifespan of human development. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.
Quantifying and Maximizing the Information Flux in Recurrent Neural Networks
Authors: Metzner Claus , Yamakou Marius E. , Voelkl Dennis , Schilling Achim , Krauss Patrick
Published: February 2024

Free-running recurrent neural networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information I[x→(t),x→(t+1)] between subsequent system states x→. Although previous studies have shown that I depends on the statistics of the network’s connection weights, it is unclear how to maximize I systematically and how to quantify the flux in large systems where computing the mutual information becomes intractable. Here, we address these questions using Boltzmann machines as model systems. We find that in networks with moderately strong connections, the mutual information I is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron pairs, a quantity that can be efficiently computed even in large systems. Furthermore, evolutionary maximization of I[x→(t),x→(t+1)] reveals a general design principle for the weight matrices enabling the systematic construction of systems with a high spontaneous information flux. Finally, we simultaneously maximize information flux and the mean period length of cyclic attractors in the state-space of these dynamical networks. Our results are potentially useful for the construction of RNNs that serve as short-time memories or pattern generators.
Anatomy of the top 1% most highly cited publications: An empirical comparison of two approaches
Authors: Perianes-Rodriguez Antonio , Gomez-Nuñez Antonio J. , Olmeda-Gomez Carlos
Published: February 2024

Scientific excellence is an ongoing object of quantitative analysis of science and technology literature. The most commonly adopted of the various criteria for such quantification is to define highly cited papers as the ones lying in the first percentile (top 1%) of citation counts. Wagner and colleagues have recently proposed a new method in which citation counts are determined as a whole, irrespective of discipline. This study analyzes the practical implications of the new approach compared to the traditional procedure in which papers are ranked bearing in mind the scientific field involved, with particular attention to the consequences for rankings by country and discipline. The results show that the new methodology favors countries with a predominance of polytechnical scientific production, technological development, and innovation and lowers the rank of countries where the social sciences, humanities, and basic research account for the lion’s share of output. An analysis of worldwide production confirms the growth of scientific output in technical and technological disciplines.
Lost in the Middle: How Language Models Use Long Contexts
Authors: Liu Nelson F. , Lin Kevin , Hewitt John , Paranjape Ashwin , Bevilacqua Michele , Petroni Fabio , Liang Percy
Published: February 2024

While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.
Environmental Justice and Lessons Learned from COVID-19 Outcomes—Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data
Authors: Zhen Zhiwei , Lee Huikyo , Segovia-Dominguez Ignacio , Huang Meichen , Chen Yuzhou , Garay Michael , Crichton Daniel , Gel Yulia R.
Published: February 2024

Virtually all aspects of our societal functioning—from food security to energy supply to healthcare—depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records that measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature, and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the states of Texas and Pennsylvania. The results indicate, in general, that the poorer air quality tends to be associated with higher rates for clinical severity and, in the case of Texas, that this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socioeconomically disadvantaged communities. Furthermore, given that one of NASA’s recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA’s satellite observations with deep learning (DL) tools for social good.

Significance Statement

By leveraging the strengths of modern deep learning models, particularly, graph neural networks to describe complex spatiotemporal dependencies and by introducing new NASA satellite records, this study aims to investigate the problem of potential environmental injustice associated with COVID-19 clinical severity and caused by disproportional impacts of poor air quality on disadvantaged socioeconomic populations.
Cestodiasis in 2 Puerto Rican crested anoles
Authors: Wu Timothy K. , Kaneko Stacy , Lucio-Forster Araceli , Spagnoli Sean , Schultz-Powell Latoya , Liotta Janice , Bowman Dwight
Published: February 2024

Two adult male Puerto Rican crested anoles ( Anolis cristatellus cristatellus) housed in a research facility were presented with debilitation and were euthanized. On autopsy, anole 1 had a large cystic white structure in the left pelvic limb, which protruded through the ruptured epidermis, and a large, poorly demarcated swelling in the right caudal abdomen. Anole 2 had masses in the mid-dorsum, caudal dorsum, left pelvic limb, and tail. These masses contained variably sized cestode larvae, which ruptured into the coelomic cavity. Evaluation of the larvae revealed a thickened and wrinkled anterior end, with a cleft-like invagination, consistent with either a plerocercoid sparganum or a tetrathyridium. Histologically, several cestode larvae were contained in the body wall of both anoles. These were up to 650 μm in diameter, with a thin tegument and a spongy parenchyma. The spongy parenchyma contained numerous, up to 30 μm diameter, sharply demarcated, basophilic-to-black structures (calcareous corpuscles). There was pneumonia and hepatitis in anole 2, suggestive of potential secondary infection subsequent to immunosuppression. Molecular amplification of the cytochrome C oxidase subunit 1 revealed 100% homology for the COX1 gene of the diphyllobothriid tapeworm Spirometra erinaceieuropaei, also known as Spirometra mansoni.
Comparative evaluation of assay performance for SARS-CoV-2 detection in animal oral samples, lung homogenates, and phosphate-buffered saline using the TaqPath COVID-19 Combo kit
Authors: Miller Megan R. , Tkachenko Andriy , Guag Jake , Alexander Stacey , Webb Brett T. , Stenger Brianna L. S.
Published: February 2024

A One Health approach has been key to monitoring the COVID-19 pandemic, as human and veterinary medical professionals jointly met the demands for an extraordinary testing effort for SARS-CoV-2. Veterinary diagnostic laboratories continue to monitor SARS-CoV-2 infection in animals, furthering the understanding of zoonotic transmission dynamics between humans and animals. A RT-PCR assay is a primary animal screening tool established within validation and verification guidelines provided by the American Association of Veterinary Laboratory Diagnosticians (AAVLD), World Organisation for Animal Health (WOAH), and the U.S. Food and Drug Administration (FDA). However, differences in sample matrices, RNA extraction methods, instrument platforms, gene targets, and cutoff values may affect test outcomes. Therefore, targeted validation for a new sample matrix used in any PCR assay is critical. We evaluated a COVID-19 assay for the detection of SARS-CoV-2 in feline and canine lung homogenates and oral swab samples. We used the commercial Applied Biosystems MagMAX Viral/Pathogen II (MVP II) nucleic acid isolation kit and TaqPath COVID-19 Combo kit, which are validated for a variety of human samples, including nasopharyngeal and oropharyngeal swab samples. Our masked test showed a high detection rate and no false-positive or false-negative results, supporting sample extension to include feline oral swab samples. Our study is a prime example of One Health, illustrating how a COVID-19 assay designed for human testing can be adapted and used to detect SARS-CoV-2 in oral swab samples from cats and likely dogs, but not lung homogenates.
On the incremental equations in surface elasticity
Authors: Yu Xiang , Fu Yibin
Published: February 2024

We derive the incremental equations for a hyperelastic solid that incorporate surface tension effect by assuming that the surface energy is a general function of the surface deformation gradient. The incremental equations take the same simple form as their purely mechanical counterparts and are valid for any geometry. In particular, for isotropic materials, the extra surface elastic moduli are expressed in terms of the surface energy function and the two surface principal stretches. The effectiveness of the resulting incremental theory is illustrated by applying it to study the Plateau–Rayleigh and Wilkes instabilities in a solid cylinder.
“All you Gain is Pain and Sorrow”: Facilitators and Barriers to the Prevention of Female Genital Mutilation in High-income Countries
Authors: Younas Fatima , Gutman Leslie Morrison
Published: February 2024

Background:

Female genital mutilation (FGM) is a harmful practice that has long-lasting negative impacts on the physical and psychological health of victims. Deemed a global concern, this practice persists in high-income countries (HIC) among certain migrant communities. Given the deleterious effects of the practice, we conducted an updated systematic review of the facilitators and barriers associated with the prevention of FGM in HIC.

Method:

A systematic review of published qualitative studies of FGM in HIC was conducted from 2012 to 2022. The search resulted in 276 studies. Of these, the majority were from low- and middle-income countries (LMIC) and excluded. A total of 14 studies were deemed fit for inclusion and none were excluded during quality appraisal. Relevant data were extracted from the studies and thematically analyzed to identify prevalent themes.

Results:

A total of 12 themes were identified and the majority reflected barriers to the prevention of FGM including beliefs about female virtue, beliefs about social sanctions, and the preservation of culture, among others. Facilitators to the prevention of FGM were fewer and included memory and trauma from experiencing FGM, knowledge and awareness of the female anatomy, and legislative protection from FGM due to migration. A few themes, such as religious beliefs, acted as both facilitators and barriers.

Conclusion:

Findings highlight the importance of shared cultural and social threads among FGM practicing communities in HIC. Interventions can use these findings to guide the development of sociocultural strategies centered on community-level prevention and reduction of FGM in HIC.
Distinctiveness, not dual coding, explains the picture-superiority effect
Authors: Higdon Kate F , Neath Ian , Surprenant Aimée M , Ensor Tyler M
Published: February 2024

The picture-superiority effect is the finding that memory for pictures exceeds memory for words on many tasks. According to dual-coding theory, the pictures’ mnemonic advantage stems from their greater likelihood to be labelled relative to words being imaged. In contrast, distinctiveness accounts hold that the greater variability of pictures compared to words leads to their mnemonic advantage. Ensor, Surprenant, et al. tested these accounts in old/new and forced-choice recognition by increasing the physical distinctiveness of words and decreasing the physical distinctiveness of pictures. Half of the words were presented in standard black font, and half were presented in varying font styles, font sizes, font colours, and capitalisation patterns. Half of the pictures were presented in black and white and half in colour. Consistent with the physical-distinctiveness account but contrary to the dual-coding account, the picture-superiority effect was eliminated when comparing the black-and-white pictures to distinctive words. In the present study, we extend Ensor, Surprenant, et al.’s results to associative recognition and free recall. Results were consistent with physical distinctiveness. We argue that dual-coding theory is no longer a viable explanation of the picture-superiority effect.
Surgical outcomes and complication rates of arthroscopic-assisted fixation versus open fixation for coronoid fractures: A systematic review and meta-analysis
Authors: Kholinne Erica , Liu Hua , Singjie Leonard Christianto , Anastasia Maria , Kwak Jae-Man , Jeon In-Ho
Published: February 2024

Background

Coronoid fractures often occur with complex elbow dislocations, accounting for 15% of elbow fractures. The effectiveness of open reduction internal fixation (ORIF) versus arthroscopic-assisted reduction internal fixation (ARIF) surgeries for coronoid fractures remains uncertain. This study aimed to compare the outcomes of these two surgical approaches in treating varus posteromedial injuries.

Methods

This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study protocol was registered with PROSPERO. The primary outcomes assessed in this study were the functional outcomes measured by the Mayo Elbow Performance Score (MEPS) and the complications associated with each surgical approach.

Results

Analysis of the data from 759 patients included in this study revealed that the mean postoperative MEPS score was higher in the ARIF group compared to the ORIF group (97.5 Vs. 90.7), with the mean improvement in MEPS was 16.6 points (19 for ORIF and 14.3 for ARIF). The complication rate in the ORIF group was 24.6%, while the ARIF group reported a complication rate of 6%.

Conclusion

This study suggests that ARIF surgery can yield comparable and promising outcomes to ORIF surgery for coronoid fractures.

Level of evidence

IV