bluuebunny/crossref_metadata_2025 huggingface dataset:
An improved post-hurricane building damaged detection method based on transfer learning
Authors: Wang Guangxing , Shin Seong-Yoon , Jo Gwanghyun
Published: February 2024

After a natural disaster, it is very important for the government to conduct a damaged assessment as soon as possible. Fast and accurate disaster assessment helps the government disaster relief departments allocate resources and respond quickly and effectively to minimize the losses caused by the disaster. Usually, the method of measuring disaster losses is to rely on manual field exploration and measurement, and then calculate and label the damaged buildings or land, or rely on unmanned collections to remotely collect pictures of the disaster-stricken area, and compare the original pictures to carry out the disaster annotation and calculation. These methods are time-consuming, labor-intensive, and inefficient. This paper proposes a post-hurricane building damage detection method based on transfer learning, which uses deep learning image classification algorithms to achieve post-disaster satellite image damage detection and classification, thereby improving disaster assessment efficiency and preparing for disaster relief and post-disaster reconstruction. The proposed method adopts the theory of transfer learning, establishes a disaster image detection model based on the convolutional neural network model, and uses the 2017 Hurricane Harvey data as the experimental data set. Experiments have proved that our proposed model accuracy of disaster detection reaches 97%, which is 1% higher than other models.
Cartoon single-image super-resolution approach based on generative adversarial network
Authors: Wang Guangxing , Shin Seong-Yoon , Kim Jong-Chan
Published: February 2024

In recent years, the study of a single image super-resolution (SISR) is crucial to improving image resolution and using hardware technology to improve image resolution. SISR is widely used in satellite remote sensing, video surveillance, and medical image processing because it mainly relies on deep learning algorithms to realize the conversion from low-resolution (LR) images to high-resolution images. It has the advantages of low cost, simple operation, and high efficiency. This paper proposes an image super-resolution method based on a generative adversarial network named text localization generative adversarial nets (TLGAN) model. The method is improved based on super-resolution generative adversarial networks (SRGAN), and the batch normalization layer is removed, which significantly reduces the computational burden of the model. In TLGAN model, we used the transfer learning method to pre-trained the model on the large dataset ImageNet, and then apply the pre-trained model to the cartoon image data set animes to achieve image super-resolution. Experimental results report that the proposed method has the advantages of fast running speed and excellent visual perception of super-resolution images compared with bicubic interpolation and SRGAN method.
Stress and anxiety detection: deep learning and higher order statistic approach
Authors: Joshi Vaishali M. , Kulkarni Deepthi D. , Uke Nilesh J.
Published: February 2024

Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress.
Interface design features and evaluation of batik 4.0 mobile application
Authors: Suparmanto Nova , Sri Asih Anna Maria , Sudiarso Andi , Santosa Paulus Insap
Published: February 2024

The use of information and communication technology could increase the quantity and quality of small medium enterprises (SME) production, including batik industry. This study focuses on the development of batik 4.0, a custom batik mobile-based interface that makes it easier for customer which can be used to quickly produce high-quality digital batik designs. The findings of this study simplify the ordering process for potential clients who want to acquire custom batik designs. Ease of transactions, namely down payments (DP) where users can make advance payments, so that users are relieved in terms of payment transactions. In designing mobile devices, applications, and user interfaces (UI), it is important to consider the user experience (UX). This paper focuses on UX design rooted in the user-centered design (UCD) approach, placing emphasis on understanding user requirements and prioritizing empathy for users. This ensures the recognition of user needs and the creation of a high-fidelity prototype. Then it was validated by the UI experts to identify problems and user difficulties in interacting with the UI. The experts responded positively towards the application and suggest for prototype improvement. Lastly, UX testing; based on the user experience questionnaire (UEQ)-S benchmark results, the batik 4.0 mobile is included in the “Excellent” category.
Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification
Authors: Barid Aziz Jihadian , Hadiyanto Hadiyanto , Wibowo Adi
Published: February 2024

Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.
Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework
Authors: Singh Sandeep Pratap , Tiwari Shamik
Published: February 2024

In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improved accuracy and reduced false acceptance and rejection rates in the parallel modal architecture. Meanwhile, HPO-CNN focuses on optimizing network designs and parameters in the sequential modal architecture. The hybrid model's 90% confidence interval ensures accurate and statistically significant performance evaluation, enhancing overall system accuracy, precision, recall, F1 score, and specificity. Through rigorous analysis and comparison, the hybrid model surpasses existing approaches across critical criteria, providing an advanced solution for secure and accurate biometric authentication.
One level deep convolutional neural network for facial key points detection
Authors: Benaiss Abdelaali , El Ayachi Rachid , Biniz Mohamed , Oujaoura Mustapha
Published: February 2024

Facial landmark detection has a lot of applications in face recognition, face alignment, facial expression recognition, video surveillance and security systems. In the existing literature, there are multiple methods utilizing convolutional neural networks (CNNs) that address this problem in various ways. In many cases, the models use a tree-like structure of CNNs to achieve better results. This paper proposes a combination of three parallel deep convolutional neural networks (DCNNs) to estimate the accurate localization of each keypoint. The first one focuses on the whole face to outperform five points, including the eyes, nose, and mouth corners. The second one focuses on the eyes-nose parts to outperform three points, specifically the eyes and nose. The last one focuses on the nose-mouth parts to outperform three points, namely the nose and mouth corners. Further, we combine all outputs of the three DCNNs and take the average value of each detected key point as the final output. In the first step, we improvthe the parameter efficiency and accuracy of each DCNNs through a set of experiments using the labeled face parts in-the-wild database (LFPW) and the helen facial feature dataset (Helen). Then, we demonstrate that our approach yields more accurate estimations of facial key points than two state-of-the-art methods and commercial software in terms of accuracy.
Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification
Authors: Setty Anitha Nagaraja , Mathad Rajesh Thalwagal , Shenthar Krishnatejaswi , Likhith Likhith
Published: February 2024

Deep learning provides many convenient methods to help medical practitioners take informed decisions about diverse ailments. The goal of this project is to measure the effectiveness of filters and contrast enhancement techniques qualitatively and quantitatively in classifying lung scan images. Transfer deep learning was used to obtain the necessary results, with DenseNet 121 being the base model. Salt and pepper filter was used to introduce noise, and 3×3 mean and 5×5 mean with contrast limited adaptive histogram equalization (CLAHE) was used to minimize the effect of noise. All layers excluding the rearmost were frozen, and new dense and dropout layers were added to identify features of computerized tomography (CT) scan images of lungs. The resultant models were of comparable accuracy, where the model with no filter gave the accurate results for the given data, and the one using the 5×5 mean filter gave better adaptability in classification of unseen data. The misclassification between normal and pneumonia affected lungs is relatively higher, because of the lack of distinct features between them.
Bengali sign language translator with location tracking system
Authors: Rahman Tanim , Chowdhury Tanjia , Sultana Jeenat
Published: February 2024

Designing an embedded system to convert sign language to sound forms to communicate with the outside world can be a challenging yet rewarding project, especially for mute people. To convey a speaker's thought through sign language, hand shapes, hand orientation and movement, and facial expressions must be combined concurrently. This research is intended to design a system that translates sign language into sound forms to establish communication with the outside world for people who are deaf, those who can hear but cannot physically speak, or have trouble with spoken languages due to some other disabilities. They can thus receive prompt assistance and stay out of uncomfortable circumstances. Additionally, this system incorporates a tracking system that uses a global system for mobile communications (GSM)/ global positioning system (GPS) module to locate a person using a tracking device and send the location to previously saved emergency contact numbers so that someone nearby can quickly locate and assist the person. Typically, each nation has its own native sign language. This project will create a few essential and typical sentences and phrases in Bengali.
Hybrid model for brain tumor detection using convolution neural networks
Authors: Kuntiyellannagari Bhagyalaxmi , Dwarakanath Bhoopalan , Reddy Panuganti VijayaPal
Published: February 2024

The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR). The visual geometry group16 (VGG16) which was pre-trained model is used for the extraction of features. To reduce the complexity, we eliminated the last eight layers of VGG16. From this transformed model, the features are extracted in the form of a vector array. These features fed into different ML classifiers like support vector machine (SVM), and Naïve Bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as Recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
Interoperability of Botswana’s healthcare systems using semantic prescription ontologies
Authors: Okon Eunice Chinatu , Sigwele Tshiamo , Malatsi Galani , Mokgetse Tshepiso , Hlomani Hlomani
Published: February 2024

The developing country of Botswana’s health information system faces interoperability challenges mainly due to the lack of shared patient medical data and histories between private and public healthcare providers, which leads to increased medical errors, increased healthcare costs, and potentially fatal outcomes. This research proposes an intelligent electronic prescription ontology (IEPO) framework to share Botswana’s patient electronic health records (EHRs) between private and public healthcare systems for a standardized and semantically rich data exchange. IEPO was evaluated for interoperability using the recall metric for completeness to capture the degree of all relevant information for exchange and the precision metric for accuracy performance to gauge the degree of error minimization during interoperability. The harmonic means of precision and recall called the F1- score, offered the overall interoperability performance. IEPO outperformed related works by 75% in recall, 54% in precision, and 76% in F1-score, demonstrating improved interoperability performance. Furthermore, IEPO was evaluated for correctness and expressiveness through competency questions via queries, results confirming correct and expressive responses.
Software component selection methods and techniques: a systematic review
Authors: Nabot Ahmad
Published: February 2024

Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.
Artificial intelligence-based Karawo motif formation using genetic algorithm
Authors: Latief Mukhlisulfatih , Syahrul Syahrul , Mappalotteng Abdul Muis
Published: February 2024

This research explores the application of artificial intelligence in generating Karawo motifs, a traditional Indonesian pattern. The research involves collecting a dataset of existing Karawo motifs and utilizing genetic algorithms to evolve and create novel pattern variations. The generated motifs are evaluated based on their adherence to traditional design principles and aesthetic appeal. The formation of Karawo motifs begins with randomly selecting image data from a database. Then, the selection of transformation treatments is performed by optimizing the fitness function within the genetic algorithm. The applied types of transformations include geometric transformations, Boolean transformations, and arithmetic transformations. The outlined genetic algorithm steps include determining the fitness function, performing its evaluation, selecting fitness values, applying crossover, implementing mutation, managing survivor selection, and terminating iterations. The results indicate that the developed system is capable of creating diverse and appealing Karawo motif patterns, showcasing the potential of combining traditional artistry with artificial intelligence. This study has the potential to expand the possibilities of Karawo motif design and contribute to the preservation and promotion of Indonesian cultural heritage.
Towards a consulting model for a good urban generation of social housing districts in terms of equipment
Authors: Alaoui Lamyae , Elhadi Sakina , Lakhouili Abdellah , Merzak Abdelaziz
Published: February 2024

Social housing in Morocco is housing built with state assistance. It is subject to precise rules of construction, management and allocation and governed by specifications defining requirements in a manual manner. This work which is done manually can produce errors either at the level of calculations or at the level of equipment proposal. The objective of this article is to computerize this area as there is a lack of tools and platforms. To solve this problem, we have created an advisory platform for good urban planning for residents which will help engineers. Based on mathematical formulas. This tool will allow architects and urban planners to design autonomous populations in terms of equipment.
A review on learning analytics in mobile learning and assessment
Authors: Sun Teik Heng , Lakulu Muhammad Modi , Mohd Noor Noor Anida Zaria
Published: February 2024

Employers are facing difficulties in selecting the most suitable candidates for employment and the transition from education to work is challenging for young graduates. Therefore, it is important to have indicators that could show the suitability of a potential candidate for his/her chosen job. A person who possesses knowledge but lacks confidence may struggle to perform assigned tasks, while an overly confident person with limited knowledge is likely to make errors in their job. Although there is existing research on learning analytics related to assessments, the research on learning analytics specifically focused on the confidence-knowledge relationship based on assessment data is still lacking. This article aims to examine the application of analytics in providing insights based on assessment data that can be utilized by potential employers. To achieve this, a systematic review was carried out, analyzing a total of 141 articles. The findings contribute to a better understanding of the use of assessment analytics in identifying the knowledge-confidence quadrants of students.
Mitigating ransomware attacks through cyber threat intelligence and machine learning
Authors: Kante Mamady , Sharma Vivek , Gupta Keshav
Published: February 2024

In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.
Image classification-based transfer learning framework for image detection of IoT devices
Authors: Saputra Yuris Mulya , Chandra Utomo Hanung Addi , Alfian Ganjar
Published: February 2024

For artificial intelligence (AI) applications, centralized learning on a cloud server and local learning on an internet-of-things device may suffer from data privacy leakage due to data sharing and inaccurate prediction due to limited computing resources. Transfer learning has been proposed as one potential solution to the world's big data problems. Transfer learning eliminates the need for each internet-of-things device to share local data with the cloud server during the training process. Instead, it can go through the training process on its own, using a cloud server's pre-trained model with high accuracy. As a result, despite its limited computing resources, the internet of things device can still predict with high accuracy. This paper proposes a transfer learning model for improving image detection accuracy on IoT devices with restricted computation. To obtain accurate image classification, a deep learning approach based on convolutional neural networks is used. The proposed method with freeze and unfreeze approaches achieves a higher validation accuracy (up to 43.6%) and a lower validation loss (up to 6.5 times) than the non-transfer learning method, according to simulation results using three relevant internet-of-things datasets.
Multiplexed Functional Assessments of
MYH7
Variants in Human Cardiomyocytes
Authors: Friedman Clayton E. , Fayer Shawn , Pendyala Sriram , Chien Wei-Ming , Loiben Alexander , Tran Linda , Chao Leslie S. , McKinstry Ashley , Ahmed Dania , Farris Stephen D. , Stempien-Otero April , Jonlin Erica C. , Murry Charles E. , Starita Lea M. , Fowler Douglas M. , Yang Kai-Chun
Published: February 2024

BACKGROUND:

Pathogenic autosomal-dominant missense variants in
MYH7
(
myosin heavy chain 7
), which encodes the sarcomeric protein (β-MHC [beta myosin heavy chain]) expressed in cardiac and skeletal myocytes, are a leading cause of hypertrophic cardiomyopathy and are clinically actionable. However, ≈75% of
MYH7
missense variants are of unknown significance. While human-induced pluripotent stem cells (hiPSCs) can be differentiated into cardiomyocytes to enable the interrogation of
MYH7
variant effect in a disease-relevant context, deep mutational scanning has not been executed using diploid hiPSC derivates due to low hiPSC gene-editing efficiency. Moreover, multiplexable phenotypes enabling deep mutational scanning of
MYH7
variant hiPSC-derived cardiomyocytes are unknown.

METHODS:

To overcome these obstacles, we used CRISPRa On-Target Editing Retrieval enrichment to generate an hiPSC library containing 113
MYH7
codon variants suitable for deep mutational scanning. We first established that β-MHC protein loss occurs in a hypertrophic cardiomyopathy human heart with a pathogenic
MYH7
variant. We then differentiated the
MYH7
missense variant hiPSC library to cardiomyocytes for multiplexed assessment of β-MHC variant abundance by massively parallel sequencing and hiPSC-derived cardiomyocyte survival.

RESULTS:

Both the multiplexed assessment of β-MHC abundance and hiPSC-derived cardiomyocyte survival accurately segregated all known pathogenic variants from synonymous variants. Functional data were generated for 4 variants of unknown significance and 58 additional
MYH7
missense variants not yet detected in patients.

CONCLUSIONS:

This study leveraged hiPSC differentiation into disease-relevant cardiomyocytes to enable multiplexed assessments of
MYH7
missense variants for the first time. Phenotyping strategies used here enable the application of deep mutational scanning to clinically actionable genes, which should reduce the burden of variants of unknown significance on patients and clinicians.
FARS2 Deficiency Causes Cardiomyopathy by Disrupting Mitochondrial Homeostasis and the Mitochondrial Quality Control System
Authors: Li Bowen , Liu Fangfang , Chen Xihui , Chen Tangdong , Zhang Juan , Liu Yifeng , Yao Yan , Hu Weihong , Zhang Mengjie , Wang Bo , Liu Liwen , Chen Kun , Wu Yuanming
Published: February 2024

BACKGROUND:

Hypertrophic cardiomyopathy (HCM) is a common heritable heart disease. Although HCM has been reported to be associated with many variants of genes involved in sarcomeric protein biomechanics, pathogenic genes have not been identified in patients with partial HCM. FARS2 (the mitochondrial phenylalanyl-tRNA synthetase), a type of mitochondrial aminoacyl-tRNA synthetase, plays a role in the mitochondrial translation machinery. Several variants of
FARS2
have been suggested to cause neurological disorders; however, FARS2-associated diseases involving other organs have not been reported. We identified
FARS2
as a potential novel pathogenic gene in cardiomyopathy and investigated its effects on mitochondrial homeostasis and the cardiomyopathy phenotype.

METHODS:

FARS2
variants in patients with HCM were identified using whole-exome sequencing, Sanger sequencing, molecular docking analyses, and cell model investigation.
Fars2
conditional mutant (p.R415L) or knockout mice,
fars2
-knockdown zebrafish, and
Fars2
-knockdown neonatal rat ventricular myocytes were engineered to construct FARS2 deficiency models both in vivo and in vitro. The effects of FARS2 and its role in mitochondrial homeostasis were subsequently evaluated using RNA sequencing and mitochondrial functional analyses. Myocardial tissues from patients were used for further verification.

RESULTS:

We identified 7 unreported
FARS2
variants in patients with HCM. Heart-specific
Fars2
-deficient mice presented cardiac hypertrophy, left ventricular dilation, progressive heart failure accompanied by myocardial and mitochondrial dysfunction, and a short life span. Heterozygous cardiac-specific
Fars2

R415L

mice displayed a tendency to cardiac hypertrophy at age 4 weeks, accompanied by myocardial dysfunction. In addition,
fars2
-knockdown zebrafish presented pericardial edema and heart failure. FARS2 deficiency impaired mitochondrial homeostasis by directly blocking the aminoacylation of mt-tRNA
Phe
and inhibiting the synthesis of mitochondrial proteins, ultimately contributing to an imbalanced mitochondrial quality control system by accelerating mitochondrial hyperfragmentation and disrupting mitochondrion-related autophagy. Interfering with the mitochondrial quality control system using adeno-associated virus 9 or specific inhibitors mitigated the cardiac and mitochondrial dysfunction triggered by FARS2 deficiency by restoring mitochondrial homeostasis.

CONCLUSIONS:

Our findings unveil the previously unrecognized role of
FARS2
in heart and mitochondrial homeostasis. This study may provide new insights into the molecular diagnosis and prevention of heritable cardiomyopathy as well as therapeutic options for FARS2-associated cardiomyopathy.
Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI
Authors: Liao Ying , Coelho Santiago , Chen Jenny , Ades-Aron Benjamin , Pang Michelle , Stepanov Valentin , Osorio Ricardo , Shepherd Timothy , Lui Yvonne W. , Novikov Dmitry S. , Fieremans Els
Published: February 2024

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here, we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation, and integrity of axons. We demonstrate that using a machine learning-based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia, and multiple sclerosis (total N = 821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging, and pathology.
Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries
Authors: Fang Zhiwei , Wang Sifan , Perdikaris Paris
Published: February 2024

Recently, deep learning surrogates and neural operators have shown promise in solving partial differential equations  (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a novel physics-informed neural operator method to solve parameterized boundary value problems without labeled data. By reformulating the PDEs into boundary integral equations (BIEs), we can train the operator network solely on the boundary of the domain. This approach reduces the number of required sample points from O(Nd) to O(Nd-1), where d is the domain’s dimension, leading to a significant acceleration of the training process. Additionally, our method can handle unbounded problems, which are unattainable for existing physics-informed neural networks (PINNs) and neural operators. Our numerical experiments show the effectiveness of parameterized complex geometries and unbounded problems.
Conflicting Results and Statistical Malleability: Embracing Pluralism of Empirical Results
Authors: Maziarz Mariusz
Published: February 2024

Conflicting results undermine making inferences from the empirical literature. So far, the replication crisis is mainly seen as resulting from honest errors and questionable research practices such as p-hacking or the base-rate fallacy. I discuss the malleability (researcher degrees of freedom) of quantitative research and argue that conflicting results can emerge from two studies using different but plausible designs (e.g., eligibility criteria, operationalization of concepts, outcome measures) and statistical methods. I also explore how the choices regarding study design and statistical techniques bias results in a way that makes them more or less relevant for a given policy or clinical question.
Examining the quality of the corresponding authorship field in Web of Science and Scopus
Authors: Chinchilla-Rodríguez Zaida , Costas Rodrigo , Robinson-García Nicolás , Larivière Vincent
Published: February 2024

Authorship is associated with scientific capital and prestige, and corresponding authorship is used in evaluation as a proxy for scientific status. However, there are no empirical analyses on the validity of the corresponding authorship metadata in bibliometric databases. This paper looks at differences in the corresponding authorship metadata in Web of Science (WoS) and Scopus to investigate how the relationship between author position and corresponding authors varies by discipline and country and analyzes changes in the position of corresponding authors over time. We find that both WoS and Scopus have accuracy issues when it comes to assigning corresponding authorship. Although the number of documents with a reprint author has increased over time in both databases, WoS indexed more of those papers than Scopus, and there are significant differences between the two databases in terms of who the corresponding author is. Although metadata is not complete in WoS, corresponding authors are normally first authors with a declining trend over time, favoring middle and last authors, especially in the Medical, Natural Sciences, and Engineering fields. These results reinforce the importance of considering how databases operationalize and index concepts such as corresponding authors, this being particularly important when they are used in research assessment.
Highly Ranked Scholars and the influence of countries and regions in research fields, disciplines, and specialties
Authors: Faghri Amir , Bergman Theodore L.
Published: February 2024

The scholarly influence of a country or region can be inferred from its number of exceptional scientists in specific research areas. Using the ScholarGPS database, we provide the first analysis of the number and geographical distribution of Highly Ranked ScholarsTM from a universe of over 30 million scholars in more than 200 countries. The associated influence of nations is reported for 14 broad fields of scholarly pursuit, two disciplines (Chemistry and Computer Science), and three specialties (Artificial Intelligence, Polymer, and Stem Cell). By comparing numbers of Highly Ranked Scholars for the last 5 years to lifetime information, we quantify the growth and decay of the influence of multiple countries and regions and identify those that are emerging in their influence. For the research areas considered here, the United States has the largest recent reduction in influence, whereas China, India, and Iran have increased their influence notably.
Cochlear Implantation in Charcot-Marie-Tooth Patients: Speech Perception and Quality of Life
Authors: Farber Nicole I. , Chin Oliver Y. , Mills Dawna M. , Diaz Rodney C. , Brodie Hilary A. , Sagiv Doron
Published: February 2024

Objectives:

There is a limited understanding of the impact of cochlear implantation (CI) in patients with Charcot-Marie-Tooth disease (CMT), given the scarcity of reported cases. We aim to evaluate the audiological outcomes and quality of life (QoL) after CI in CMT.

Methods:

Multi-institutional, university-affiliated, tertiary-referral centers, retrospective chart review. Our cohort includes 5 patients with CMT. Patients’ charts were reviewed for demographic characteristics, operation notes, and pre- and post-implantation audiology evaluation. Patients completed the Cochlear Implant Quality of Life-10 (CIQOL-10) Global questionnaire.

Results:

Pre-implantation, the mean pure tone average was 84.1 ± 7.2 dB, and the mean word recognition score was 2.4% in the implanted ear. AzBio sentence test was performed in quiet, revealing a mean of 4 ± 1.4% in the implanted ear. Post-implantation, PTA results were all within the mild hearing loss range (mean 33.0 ± 5.9 dB). Post-CI, AZ-Bio test results were 5%, 65%, and 74% (for 3 patients), and HINT scores were 55% and 58% (for 2 patients). The mean score of the CIQOL-10 questionnaire was 42.7 ± 10.47 (range 1-100). Patients were most satisfied with their ability to listen to the television or radio, have conversations in a quiet environment, and feel comfortable being themselves.

Conclusion:

To the best of our knowledge, this is the most extensive series of CI in CMT-associated sensorineural hearing loss and auditory neuropathy. Our cohort suggests that CI is a safe and reliable method for hearing rehabilitation that can achieve good speech performance and improve QoL in CMT patients.
Adherence, Persistence, Switching and Costs of Injectable and Oral Therapies for Multiple Sclerosis. Real Life Analysis Over 6 Years of Treatment
Authors: Santoleri Fiorenzo , Lasala Ruggero , Berardini Eleononora , Vernacchio Flavia , Leo Donato , Costantini Alberto
Published: February 2024

Background: Adherence and persistence to treatment with disease-modifying therapies (DMTs) is a predictor of the efficacy of treatment. Aims: The objectives of the study were the analysis of adherence, persistence, switches, and costs of the drugs used in MS. Methods: This is a retrospective non-interventional pharmacological observational study of 610 patients diagnosed with Relapsing-Remitting Multiple Sclerosis (RRMS) under therapy between January 2007 and September 2022. Results: Adherence values were greater than 0.75 for all the drugs in considered for the study. The mean persistence value was 2.5 years on the analysis performed on the first-line treatment. Conclusion: In a therapy in which adherence is predominant, but not exclusive to therapy efficacy, persistence to the drug is synonymous with drug efficacy.
‘Normalizing Informality’ in Local–Transnational Spaces: Contraband, Conflict and Street Trade in Eastern Ethiopia
Authors: Esayas Dube Engida , Brown Alison
Published: February 2024

Informality arguably remains the main source of employment and income for most participants in the cities of the Global South. It is rampant in cities along national borders and transport routes. This paper discusses the ‘normality of informality’, exploring the nexus between contraband, conflict and informal cross-border trade in a local–transnational space, drawing from Dire Dawa in eastern Ethiopia. Data were collected through key informants and in-depth interviews in 2015/2016 and 2019. This study also benefitted from secondary sources. Since its establishment in 1902, the city has become a major hub of commerce, industry, transportation, and cross-border trade. Combining the literature on the informal economy and cross-border trade and drawing on evidence from eastern Ethiopia, this study explores how trade is deeply embedded in local–transnational interactions through partnerships between state and non-state actors. This study argues that informal cross-border trade is a societal mode of provisioning with extensive livelihood opportunities that survive even through periods of conflict and repression. Thus, reducing the vulnerability of participants in informal cross-border trade could be a more effective policy target than eradicating them.
Domestic Violence, Sex, Strangulation and the ‘Blurry’ Question of Consent
Authors: Douglas Heather , Sharman Leah , Fitzgerald Robin
Published: February 2024

A stand-alone strangulation offence was introduced in Queensland, Australia in 2016. One of the elements of the Queensland strangulation offence is that the victim did not consent to the strangulation. This paper reviews the harms and dangers associated with strangulation before overviewing the debates about the use of strangulation during sex. Drawing on focus group discussions conducted with domestic violence support workers and men's behaviour change workers, we discuss four overlapping themes identified in the discussions. These were perceptions that: strangulation during sex is normalised; consent is not informed; it happens in the context of coercive control; and the requirement of consent opens a loophole in the strangulation offence. Considering the issues raised, and the clear risks and harms, we suggest that consideration should be given to whether it is ever possible to consent to strangulation and we consider possible reforms such as following the two-tiered approaches to consent used in the England and Wales law and elsewhere in Australia. We also conclude that law reforms such as these are partial solutions and there is significant need for more community education about the risks and harms of strangulation.
Consequences of Marketing Asset Accountability—A Natural Experiment
Authors: Guenther Peter , Guenther Miriam , Lukas Bryan A. , Homburg Christian
Published: February 2024

Marketing scholars have extensively studied marketing's effect on firm value and have developed metrics and dashboards to help establish marketing accountability. However, empirical evidence of marketing accountability's specific outcomes is scarce and mainly derived from surveys. It also lacks consideration of outcomes beyond the marketing function's standing in the firm, thus overlooking possible downsides and outcomes with regard to external stakeholders such as investors. Using a natural experiment—Australia's change from a nonrestrictive to a restrictive accounting regime—this study investigates how accountability for the financial value of marketing assets (marketing asset accountability) affects a firm's marketing management focus on short-term vis-à-vis long-term marketing efficiency, its cost of capital, and the degree to which its stock price reflects actual future performance (i.e., stock price informativeness). The results show that marketing asset accountability improves long-term marketing efficiency, reduces cost of equity, and improves stock price informativeness, but does not consistently affect short-term marketing efficiency and cost of debt. Moreover, although marketing-intensive firms are commonly assumed to benefit most from marketing asset accountability, this is not the case. These results have implications for researchers, managers, and public policy decision makers.
Experimental study on new structure teeth in a high-temperature environment
Authors: Song Dongdong , Yang Yingxin , Ren Zhangping , Wang Jian , He Xianzhi , Huang Ya , Lv Siduan , Yang Kaizhi , Peng Song , Chen Hanyu
Published: February 2024

In high-temperature drilling, especially in high-temperature geothermal drilling, cone bits often experience common and severe tooth loss. This issue significantly reduces the cone bit's service life and has a detrimental impact on drilling efficiency. The quality of the fixed teeth plays a crucial role in the performance of the cone bit. In high-temperature environments, conventional methods fail to meet the requirements for securing the cone bit's teeth. Therefore, to address the tooth loss problem in high-temperature drilling, a new tapered tooth structure is proposed. Laboratory experiments were conducted to secure teeth with varying tapers at both normal and high temperatures. The results revealed that the maximum fastening force increased progressively with the degree of taper, reaching its peak at C50. Compared to conventional cylindrical teeth, the maximum fastening force increased by approximately 88.6%–271.1% at different temperatures. The tapered structure demonstrated superior tooth-fixing strength. The maximum fastening force is the smallest at 300 °C, approximately 23.7%–61.2% lower than at normal temperature. Under the same interference conditions, the maximum fastening force increased with greater taper. With interference values of 0.075, 0.095, and 0.115, the maximum fastening force increased by 48.9%–175.1%, 14%–141.6%, and 53%–271.1%, respectively, when compared to cylindrical teeth with C300, C200, C100, and C50 tapers. The tapered structure exhibited superior tooth-fixing strength and significantly enhanced tooth retention strength at high temperatures.
Citizenship and discomfort: Wearing (clothing) as an embodied act of citizenship
Authors: Halász Katalin
Published: February 2024

This article contributes to research on citizenship and belonging in the post-Brexit white East European migration to the UK. It explores wearing a garment as an act of citizenship and an embodied methodology. It is formed of two interrelated parts: the first presents the argument that wearing a particular garment at a specific spatio-temporal juncture can be considered an act of citizenship. The second part proposes wearing as an affective method in researching citizenship that has the potential to explore the sensory and emotional dimensions of (non)belonging. White embodiments and discomfort are two threads that connect the main arguments. The article builds on autoethnographic notes made after preparing for a job interview as a white East European woman wearing a Victorian male costume while travelling from East to South London in the wake of the General Election on 12 December 2019.
An ‘unfathomable hatred of Islam’: Ethno/graphing the trial for the Québec City mosque massacre
Authors: Fernandez Fabrice , Marois Sophie , Gariépy Stéphanie , Arnal Sarah
Published: February 2024

On the night of January 29, 2017, six Muslim worshippers were killed, and many others severely injured when a white man opened fire at a mosque in Québec City (QC, Canada). This article is based on a collective ethno/graphy of the assailant’s trial, from its beginning in March 2018 until the verdict in February 2019. During this period, our research group – formed of three sociologists and a visual artist – attended trial hearings at the Superior Court of Québec and collected related media coverage and political statements. To analyze the legal, political, and media treatment of the 2017 massacre, we put forth a political ethnography of justice, informed by critical scholarship on depoliticization and epistemologies of white ignorance. Our analysis identifies distinct sequences in a process of depoliticization that obscures historical, structural, and political dimensions of anti-Muslim violence. In attending to this process, our article advances sociological research on the institutional treatment, and concealment, of religiously and racially targeted violence.
Budgets and biologicals: The bio-economization of HIV governance
Authors: Tseng Po-Chia
Published: February 2024

Changing global responses to HIV/AIDS entail shifting biological loci of surveillance which are believed to constitute HIV risk. Meanwhile, various local institutions and organizations are mobilized to play a key role in HIV service delivery and surveillance. Drawing on a socio-material approach to the body and the economy, this study theorizes the emergence of three ‘HIV service bio-economies’ devised to provide HIV services while controlling HIV transmission in Taiwan. Instead of presuming a divide between the social and the biological, it analyzes how different bodies are produced through differing modes of HIV surveillance and economization, buttressed by global health sciences, state budgets and quantitative metrics. The analysis of multiple ontologies of HIV underscores the political nature of risk-framing in a transnational context, but also how certain bodies incapable of being enrolled in these economies could be further marginalized – a process which might be understood as an ontological politics of HIV.